DESC:SYSTEM AND METHOD FOR DISPENSING BATTERY PACK TO ELECTRIC VEHICLE USER

CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202421070093 filed on 17/09/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
Generally, the present disclosure relates to battery swapping stations. Particularly, the present disclosure relates to a system and a method for dispensing a battery pack to an electric vehicle user.
BACKGROUND
Swappable battery charging stations are infrastructure systems designed to facilitate the rapid replacement of depleted batteries in electric vehicles (EVs), electric two-wheelers, or other battery-powered machinery. Unlike traditional EV charging stations that require a vehicle to remain stationary during the charging process, the swappable battery stations allow users to exchange a depleted battery for a fully charged one, significantly reducing downtime. The stations typically consist of a battery storage rack, an automated or semi-automated swapping mechanism, a charging module for multiple batteries, and a control unit for monitoring and managing battery health, charging cycles, and inventory.
Conventionally, the swappable battery station monitors the state of charge (SoC), temperature, and health of each battery stored within the housing. As a user arrives, the station identifies a compatible, fully charged battery and initiates the swapping process through robotic arms or user-assisted modules. Once a depleted battery is received, the depleted battery is assessed for safety and placed into a charging slot with an optimized charging schedule based on usage patterns, grid demand, and battery condition. The station also interfaces with external energy sources such as solar panels or grid-tied energy storage to manage load balancing and cost efficiency.
However, there are certain problems associated with the existing or above-mentioned mechanism to optimize energy management in a swappable battery charging station. For instance, while the charging station is in operation, the charging station continuously draws power from the grid, leading to overcharging of the battery station and the stored batteries. Further, the continuous charging leads to thermal inefficiencies, which lead to battery degradation. Furthermore, the existing mechanism is equipped with suboptimal load distribution, thereby increasing operational costs and redundant strain on the local power grid. Moreover, as the number of active users and batteries scales, the complexity of real-time energy allocation intensifies.
Therefore, there exists a need for a secure, interoperable, and automated alternative for optimized energy management in a swappable battery charging station.
SUMMARY
An object of the present disclosure is to provide a system for optimized energy management in a swappable battery charging station.
Another object of the present disclosure is to provide a method for optimized energy management in a swappable battery charging station.
In accordance with a first aspect of the present disclosure, there is provided a system for optimized energy management in a swappable battery charging station, the system comprising:
- a plurality of charging slots configured to accommodate and electrically interface with battery units;
- a centralized control module, the centralized control module comprising:
- a demand scheduler configured to track real-time battery swapping requests and predict energy demand patterns;
- a charging control unit configured to regulate charge rate selection for the battery units based on real-time battery condition parameters;
- an energy source allocator configured to dynamically select a charging input source between a grid and a plurality of renewable energy sources; and
- a grid transaction manager configured to control the drawing of energy from the grid or the supply of energy to the grid,
wherein the centralized control module is configured to dynamically maximize energy dispensing efficiency of the charging station via a multi-variable optimization model based on the predicted energy demand patterns, regulated charge rate, selected charging input source, and energy drawing control.
The system for optimized energy management in a swappable battery charging station, as described in the present disclosure, is advantageous in terms of intelligent, adaptive, and energy-efficient operation of a swappable battery charging station through real-time control, predictive analytics, and dynamic optimization. Further, autonomous management of grid interactions based on tariff signals and energy arbitrage strategies enhances economic performance while maintaining energy availability. Furthermore, continuous evaluation of power quality and renewable forecasting maximizes the use of clean energy, contributing to sustainability goals. Moreover, the multi-variable optimization model ensures coordinated decision-making across all subsystems, leading to extended battery life, minimized energy wastage, and scalable deployment in diverse usage environments.
In accordance with another aspect of the present disclosure, there is provided a method for optimized energy management in a swappable battery charging station, the method comprising:
- receiving a real-time battery swapping request, via a centralized control module;
- tracking real-time battery swapping requests and predicting energy demand patterns, via a demand scheduler;
- regulating charge rate selection for the battery units based on real-time battery condition parameters and energy demand patterns, via a charging control unit;
- selecting a charging input source between a grid and a plurality of renewable energy sources based on the charge rate selection, via an energy source allocator; and
- controlling drawing of energy from the grid or the supply of energy to the grid, via a grid transaction manager.

Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
Figure 1 illustrates a block diagram of a system for optimized energy management in a swappable battery charging station, in accordance with an embodiment of the present disclosure.
Figure 2 illustrates a flow chart of a method for optimized energy management in a swappable battery charging station, in accordance with another embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
As used herein, the terms “swappable battery charging station”, “battery unit exchange station”, “power swap station”, “battery swapping station”, and “station” are used interchangeably and refer to an integrated energy infrastructure designed to support the rapid exchange, charging, and energy management of battery units used in electric vehicles (EV) or similar energy storage applications. Specifically, the swappable battery charging station comprises a plurality of charging slots and a centralized control module. Further, the plurality of charging slots is configured to accommodate and electrically interface with standardized battery units, enabling continuous service delivery by allowing depleted batteries to be replaced instantly with fully charged ones. The aforementioned subsystems enable operation using a multi-variable optimization model that is periodically executed and re-executed based on variations in battery status, energy source conditions, and demand fluctuation thresholds. Moreover, the swappable battery charging stations are classified into three operational types, including, but not limited to, grid-dependent stations, renewable-integrated stations, and hybrid stations. Therefore, the swappable battery charging station is a hybrid station and ensures optimized charging of the battery units.
As used herein, the terms “charging slots”, “battery slots”, and “slots” are used interchangeably and refer to electrically and mechanically configured interfaces within the swappable battery charging station, designed to house and simultaneously charge multiple standardized battery units. Further, each of the charging slots comprises hardware interfaces that ensure secure physical accommodation and electrical connectivity between the station and the battery unit, thereby enabling automated charging and data exchange processes. The charging slots are directly integrated with the centralized control module, allowing real-time monitoring and adaptive charge regulation based on individual battery condition parameters. Moreover, the charging slots are equipped with sensors and communication modules that relay battery-specific data to the centralized control module for optimization of energy allocation and charge scheduling. Types of charging slots include, but are not limited to, linear slots arranged in sequence for high-throughput operations, modular bay-type slots designed for robotic swapping systems, and vertically stacked slots for space-constrained environments. Furthermore, each slot operates as a smart node in the distributed charging architecture, participating in load balancing and source allocation through coordinated input from the energy source allocator and the demand scheduler.
As used herein, the terms “battery units” and “battery” are used interchangeably and refer to energy storage modules designed for integration with electric vehicles and compatible charging infrastructure. Each battery unit comprises a rechargeable electrochemical cell assembly enclosed in a structurally robust casing with embedded electrical, thermal, and communication interfaces, ensuring compatibility with the plurality of charging slots in a swappable battery charging station. Further, the battery units are engineered to support rapid insertion and removal while maintaining secure electrical contact and real-time data exchange with the charging control system. Furthermore, the battery units operate as discrete, intelligent subsystems capable of participating in energy demand forecasting and optimization routines executed by the centralized control module. Additionally, types of battery units include high-capacity modules optimized for long-range usage, fast-charging variants with enhanced thermal dissipation and current handling capabilities, and modular stackable units designed for flexible energy capacity scaling. Each unit is uniquely identified and tracked through transaction logs, enabling the demand scheduler to correlate usage patterns with charging behavior and support predictive energy management across the swappable battery ecosystem.
As used herein, the terms “centralized control module”, “control module,” and “control unit” are used interchangeably and refer to an integrated computational and decision-making subsystem within a swappable battery charging station, configured to manage energy flow, optimize charging behavior, and coordinate system-level operations. Specifically, the centralized control module comprises multiple functional units, including a demand scheduler, a charging control unit, an energy source allocator, and a grid transaction manager, each operating in real time to ensure efficient energy utilization and continuous service delivery. Further, the charging control unit selects and regulates charge rates based on real-time battery condition parameters. Furthermore, the energy source allocator determines the optimal charging input source through evaluation of grid-side electrical parameters, including, but not limited to, voltage stability, current availability, and Total Harmonic Distortion, combined with short-term weather predictions to forecast renewable energy availability. Moreover, the grid transaction manager controls bi-directional energy exchange with the electrical grid by monitoring dynamic tariff rates, enabling energy arbitrage through energy storage during low-tariff periods and export during high-tariff windows. Additionally, the centralized control module executes a multi-variable optimization model at predefined intervals and during critical events such as deviations in battery status, input availability, or demand thresholds, ensuring adaptive energy management. Further, the types of centralized control modules include, but are not limited to, edge-processed controllers for localized deployment, cloud-synchronized systems for large-scale predictive optimization, and hybrid models with distributed computation nodes coordinated through the main control interface.
As used herein, the term “demand scheduler” refers to a subsystem responsible for predicting energy demand and coordinating charging operations based on anticipated battery swapping activity. Specifically, the demand scheduler integrates a machine learning-based prediction engine trained on comprehensive historical datasets including timestamped swapping records, user-specific behavior patterns, and long-term energy demand fluctuations. Further, the demand scheduler ensures energy availability aligns with predicted load without overcommitting grid or renewable resources. Additionally, the types of demand schedulers include static model-based schedulers designed for predictable usage environments, adaptive learning-based schedulers trained with real-time data feedback loops, and federated demand schedulers deployed in multi-station networks with demand signals synchronized across distributed locations to optimize regional energy distribution. Consequently, the demand scheduler maintains continuous alignment between operational capacity and predicted energy requirements to maximize station throughput and minimize energy wastage.
As used herein, the terms “charging control unit” and “charging control module” are used interchangeably and refer to a subsystem designed to regulate the charging behavior of individual battery units based on real-time operational conditions. Specifically, the charging control unit receives real-time data from embedded sensors within each battery unit, including state-of-charge, internal resistance, temperature, voltage level, and current flow, and applies intelligent logic to determine the most suitable charging profile for each unit. Further, adaptive charging is achieved through selection among predefined modes such as Constant Current, Constant Voltage, and pulse charging, based on both the battery’s current condition and the available capacity from the selected input energy source. Furthermore, the charging control unit synchronizes with an energy source allocator to ensure that charge rate decisions align with grid stability parameters or renewable source availability. Additionally, the types of charging control units include distributed control units embedded at the individual charging slot level for localized execution, centralized units managing all slots through a master control interface, and hybrid models where critical decision logic is distributed but coordinated through a unified optimization framework. Each configuration of the charging control unit functions as a critical node in the overall energy management system, facilitating safe, efficient, and demand-responsive battery charging within the swappable station architecture.
As used herein, the term “battery condition parameters” refers to a set of measurable electrical, thermal, and physical metrics that define the operational state and health of a battery unit within a swappable battery charging station. Specifically, the aforementioned parameters provide critical input to the charging control unit for executing real-time decisions related to charge rate, profile selection, and safety management. The key battery condition parameters include, but not limited to, state-of-charge, temperature, internal resistance, voltage, and current flow. The state-of-charge quantifies the remaining energy capacity of the battery units. Further, the temperature affects the electrochemical performance and thermal safety limits of the battery units. Furthermore, the internal resistance influences efficiency and heat generation during the charging of the battery units. The aforementioned parameters are continuously monitored through integrated sensor arrays within each battery unit and transmitted to the centralized control module for processing. Additionally, types of battery condition parameter sets include basic operational datasets for standard battery units, extended diagnostic sets for high-performance or fast-charging units, and predictive health datasets incorporating degradation models and usage history for advanced condition forecasting. Therefore, accurate acquisition and interpretation of battery condition parameters enable high-efficiency, adaptive, and safe energy transfer in alignment with the dynamic requirements of a swappable battery ecosystem.
As used herein, the term “energy source allocator” refers to a subsystem responsible for dynamically selecting and managing the input energy source used to charge battery units. The energy source allocator evaluates real-time electrical parameters from the grid, including, but not limited to, voltage stability, current availability, and Total Harmonic Distortion, to determine the quality and reliability of grid-supplied power. In parallel, the energy source allocator accesses predictive weather data through an integrated forecast module to assess short-term availability of renewable energy sources such as solar and wind. Further, the source selection is executed through a weighted decision logic framework that accounts for forecasted energy demand, real-time charging requirements, and energy quality indicators. Furthermore, the energy source allocator ensures optimal distribution of load across available energy sources and simultaneously prioritizes efficiency, sustainability, and cost-effectiveness. Moreover, integration of the energy source allocator with the charging control unit ensures that energy delivery aligns with battery condition parameters and charge profiles, maintaining system-wide consistency. Additionally, types of energy source allocators include grid-dominant allocators for urban environments with stable infrastructure, renewable-biased allocators configured for off-grid or hybrid installations, and adaptive allocators designed to switch intelligently between multiple sources based on operational thresholds and demand predictions. The energy source allocator functions as a critical control layer that enables continuous energy optimization and resilient power management within the swappable battery charging architecture.
As used herein, the term “charging input source” refers to the origin of electrical energy used to power the charging operations of battery units within a swappable battery charging station. The charging input sources include both conventional grid power and renewable energy systems, such as, but not limited to, photovoltaic arrays, water energy, and wind turbines, which are interfaced through the energy source allocator. The selection of the charging input source is governed by real-time evaluation of grid electrical parameters, including but not limited to voltage stability, current availability, and Total Harmonic Distortion, with forecasted renewable energy availability derived from weather prediction models. The energy source allocator implements a weighted decision logic to determine the most efficient and cost-effective input source based on predicted energy demand, charge scheduling, and tariff rate structures. Further, the chosen input source is routed through power conditioning and distribution units to individual charging slots, as adaptive charge profiles are applied based on battery condition parameters. Additionally, charging input sources are classified into grid-dependent sources relying entirely on utility supply, renewable-dominant sources configured for solar or wind priority operation, and hybrid sources where input selection is dynamically optimized through continuous system monitoring.
As used herein, the term “grid” refers to an external electrical power distribution network interfaced with a swappable battery charging station for the purpose of energy import and export. Further, the grid functions as a centralized energy infrastructure supplying alternating current at standardized voltage and frequency levels, regulated by regional utility providers. Within the charging station architecture, the grid serves as a primary or secondary charging input source, depending on availability, tariff conditions, and system demand. The energy source allocator evaluates real-time grid-side electrical parameters, including but not limited to voltage stability, current availability, and Total Harmonic Distortion, to determine the suitability of the grid as an active power source. Furthermore, the grid transaction manager interfaces with the grid through a bi-directional power exchange module, enabling controlled energy import during low-tariff windows and export during peak-tariff conditions, thereby facilitating energy arbitrage operations. Moreover, integration with the centralized control module ensures that the grid interactions align with demand forecasts and optimization routines. Additionally, grid usage is categorized into three operational types: full-grid stations reliant entirely on utility-supplied power, supplemental-grid stations utilizing the grid as a backup to renewable energy systems, and bidirectional-grid stations capable of dynamic import and export based on real-time economic and technical parameters. The grid operates as a key component in the overall energy management strategy of the swappable battery charging station, supporting continuous operation, economic efficiency, and grid-responsive behavior.
As used herein, the term “renewable energy source” refers to a naturally replenishable source of electrical power integrated into a swappable battery charging station to reduce dependency on conventional utility supply and enhance energy sustainability. Specifically, the renewable energy sources include photovoltaic systems, wind turbines, and other clean energy generation units connected through power conditioning interfaces to the energy source allocator. The energy source allocator evaluates short-term renewable availability using a forecast module that processes near-term weather predictions. Further, the selection of renewable energy as a charging input source is based on weighted decision logic that incorporates forecasted generation, current load demand, and battery condition requirements. Furthermore, renewable energy contributes to adaptive charging operations by powering charging slots directly or supplementing grid energy based on real-time optimization parameters. Furthermore, system-wide coordination between renewable input and charging control ensures maximum utilization of clean energy without compromising operational stability. Additionally, types of renewable energy sources include fixed-axis solar panel arrays optimized for high-efficiency generation in static installations, dual-axis tracking photovoltaic systems designed for dynamic angle adjustment, and variable-speed wind turbines configured for microgrid integration. Each renewable energy source is managed within the centralized control module to align with the station's multi-variable optimization model, supporting environmentally efficient and demand-responsive energy delivery across the swappable battery infrastructure.
As used herein, the term “grid transaction manager” refers to a subsystem responsible for regulating the bidirectional flow of electrical energy between the charging station and the utility grid. Further, the grid transaction manager interfaces with external grid infrastructure through a power exchange gateway that supports real-time monitoring, energy import, and energy export operations. Specifically, during low-tariff windows, the grid transaction manager initiates energy import for battery charging or local storage, and during high-tariff conditions, stored energy is exported back to the grid to perform energy arbitrage. Furthermore, integration with the energy source allocator ensures that grid power usage remains synchronized with the overall source selection strategy. Additionally, the types of grid transaction managers include passive managers configured solely for unidirectional import, active managers designed for controlled bidirectional energy exchange with embedded tariff logic, and predictive managers that incorporate forecast data to pre-emptively schedule import/export operations based on expected pricing shifts. The grid transaction manager plays a critical role in maintaining economic efficiency and energy availability and simultaneously enabling responsive participation in smart grid environments.
As used herein, the term “energy dispensing efficiency” refers to the ratio of usable electrical energy delivered to battery units relative to the total energy input into a swappable battery charging station, governed by real-time operational control and multi-source energy management. Further, the energy dispensing efficiency is maximized through coordinated execution of a multi-variable optimization model within the centralized control module, which continuously processes inputs from the demand scheduler, charging control unit, energy source allocator, and grid transaction manager. Furthermore, the types of energy dispensing efficiency implementations include static efficiency systems with fixed energy routing logic, dynamic real-time efficiency systems utilizing predictive load balancing and adaptive control, and hybrid models with cloud-based analytics for cross-station energy orchestration. High energy dispensing efficiency supports optimal resource utilization, economic operation, and sustainable energy delivery within the swappable battery charging ecosystem.
As used herein, the term “multi-variable optimization model” refers to a computational framework designed to coordinate and maximize energy dispensing efficiency by simultaneously processing and balancing multiple dynamic parameters. Specifically, the model operates on real-time data streams received from the demand scheduler, charging control unit, energy source allocator, and grid transaction manager, executing algorithmic decisions that align energy input, charging operations, and energy transactions with forecasted demand and operational constraints. Further, core variables include predicted energy demand patterns, real-time battery condition parameters, and tariff-based energy pricing for optimizing grid interaction. The predicted energy demand patterns are derived from machine learning analysis of historical and real-time battery swapping data. Furthermore, real-time battery condition parameters include, but are not limited to, state-of-charge and internal resistance, and input source quality metrics including grid voltage stability and renewable energy forecasts. Moreover, the optimization model is executed at predefined intervals and re-executed when variations in battery conditions, energy input availability, or predicted demand exceed established control thresholds, enabling adaptive responsiveness. Additionally, the model outputs coordinated control directives that govern source allocation, charge profile selection, slot-level energy distribution, and grid energy exchange, ensuring synchronized efficiency across all functional subsystems. The types of multi-variable optimization models include rule-based deterministic models for predefined operational environments, adaptive feedback-based models incorporating real-time learning and recalibration, and hybrid cloud-assisted models that integrate distributed predictive analytics across multiple stations. The multi-variable optimization model serves as the core intelligence engine of the energy management system, facilitating high-throughput, demand-aligned, and cost-efficient operation within the swappable battery infrastructure.
As used herein, the term “energy demand patterns” refers to temporally distributed trends and fluctuations in energy requirements associated with battery swapping activities within a swappable battery charging station. The patterns represent the aggregated outcome of user behavior, transaction frequency, time-of-day utilization, and seasonal variations, and serve as foundational input to predictive scheduling and optimization processes. Specifically, the demand scheduler within the centralized control module utilizes a machine learning-based engine trained on timestamped transaction logs, user behavior analytics, and historical load profiles to forecast energy demand patterns with high granularity. Further, the energy demand patterns drive the activation and sequencing of charging slots, allocation of charging input sources, and tariff-optimized grid interactions. Furthermore, the patterns inform the multi-variable optimization model for proactive charge scheduling, load balancing, and source selection. Additionally, types of energy demand patterns include cyclical daily patterns characterized by peak usage during commuting hours, irregular patterns triggered by unpredictable user behavior or external conditions, and forecast-stabilized patterns that result from consistent operational feedback loops in high-traffic stations. Advantageously, the accurate identification and processing of energy demand patterns ensure precise alignment between charging infrastructure capabilities and real-world energy usage dynamics, supporting continuous operational efficiency within the swappable battery ecosystem.
As used herein, the term “regulated charge rate” refers to the controlled electrical current and voltage applied to a battery unit during the charging process within a swappable battery charging station, determined in real time based on operational and environmental parameters. Further, the charging control unit within the centralized control module receives real-time feedback from each battery unit, including state-of-charge, internal resistance, temperature, and voltage levels, and selects a corresponding charge profile to maintain optimal efficiency, safety, and battery lifespan. Furthermore, regulated charge rate selection is executed using adaptive control logic that dynamically chooses from predefined charging modes such as, but not limited to, constant current, constant voltage, and pulse charging, with the profile tailored to the specific condition of the battery and the capacity of the selected input energy source. Moreover, variations in charge rate are continuously recalibrated in response to changing battery conditions or energy input parameters, as processed by the multi-variable optimization model. Additionally, the regulated charge rate types include fixed-rate profiles for uniform battery chemistries, condition-based variable rate profiles responsive to thermal and electrical characteristics, and predictive profiles that incorporate battery usage history and degradation trends. Implementation of regulated charge rate control across the plurality of charging slots enables uniform energy distribution, reduces thermal stress, and enhances overall system throughput within the swappable battery charging architecture.
As used herein, the term “energy drawing control” refers to the systematic regulation of electrical energy intake from external sources into a swappable battery charging station, governed by real-time operational parameters and economic considerations. The control function is managed by the grid transaction manager in coordination with the energy source allocator, both operating under the directives of the centralized control module. Specifically, the energy drawing control determines the timing, quantity, and quality of energy imported from the grid or sourced from renewable systems, based on dynamic tariff structures, forecasted demand, and input source availability. Further, grid-based energy drawing is modulated using bi-directional interfaces that respond to real-time tariff data, enabling energy import during low-tariff periods and curtailment or reversal during peak-tariff windows to support energy arbitrage strategies. Renewable energy intake is managed based on short-term generation forecasts, with drawing schedules aligned to periods of optimal output. Furthermore, the multi-variable optimization model processes data from the demand scheduler, battery condition monitoring systems, and charging slot status to establish control setpoints for energy intake. Moreover, the resulting directives ensure that energy drawing is responsive to fluctuations in demand and system capacity, and also avoid overloading and inefficiencies. Additionally, types of energy drawing control include passive control schemes based on fixed thresholds, adaptive control systems utilizing real-time sensor feedback and predictive analytics, and distributed control frameworks that synchronize multiple charging stations across a network to balance grid load. Each implementation of energy drawing control supports efficient energy utilization, grid responsiveness, and sustainable charging operations across the plurality of charging slots.
As used herein, the term “timestamped transaction logs” refers to chronologically ordered digital records capturing detailed data from each battery swapping event within a swappable battery charging station, including precise time references and associated operational parameters. Further, each log entry documents the initiation and completion time of a battery exchange, user identification data, battery unit identification, charging slot assignment, charge level at entry and exit, source of charging input, and energy dispensed during the transaction. Furthermore, the logs form the primary dataset used by the demand scheduler within the centralized control module to train machine learning models for predicting energy demand patterns. Moreover, the logs also support the anomaly detection module by serving as historical baselines for detecting deviations in usage behavior or energy flow. Timestamp precision enables accurate sequencing of operations, latency measurement, and system throughput analysis across a plurality of charging slots. Further, integration of the logs with real-time control subsystems ensures traceability, performance optimization, and load forecasting accuracy. Additionally, types of timestamped transaction logs include raw logs directly captured from hardware events, enriched logs supplemented with predictive analytics and user behavior metrics, and synchronized logs aggregated from distributed charging stations within a network.
As used herein, the term “user behavior patterns” refers to identifiable trends and recurrent actions exhibited by end-users during interaction with a swappable battery charging station, captured and analysed to enhance predictive energy management and operational efficiency. The patterns are derived from timestamped transaction logs and include metrics such as, but not limited to, frequency of battery swaps, preferred time intervals for charging activity, typical energy consumption levels, slot usage preferences, and responsiveness to pricing fluctuations. Further, the demand scheduler within the centralized control module processes the aforementioned behavioral metrics using a machine learning-based engine trained to correlate historical usage trends with projected energy demand. Furthermore, the user behavior patterns enable segmentation of users based on operational profiles, such as high-frequency commercial users, low-frequency residential users, and time-sensitive fleet operators. Moreover, the anomaly detection module utilizes deviations from established behavior patterns to trigger predictive maintenance, dynamic scheduling adjustments, or security verifications. Additionally, types of user behavior patterns include time-based cyclic behaviors reflecting consistent daily or weekly usage, context-driven patterns influenced by external factors such as weather or traffic conditions, and dynamic behavioral shifts resulting from changes in pricing schemes or system configuration.
As used herein, the term “energy demand history” refers to the cumulative, time-indexed record of electrical energy consumption associated with battery charging and swapping events within a swappable battery charging station, serving as a foundational dataset for forecasting and optimization. The historical dataset includes, but not limited to, detailed measurements of energy dispensed per transaction, temporal distribution of charging activity, duration of energy draw events, charging input source utilization, and slot-specific energy throughput across all operational intervals. The demand scheduler within the centralized control module utilizes energy demand history to train a machine learning-based engine, enabling accurate prediction of future energy requirements based on long-term consumption patterns and temporal trends. Further, the analysis of energy demand history allows identification of high-demand intervals, slot-level utilization intensity, and responsiveness to external factors such as tariff variations or renewable generation availability. Furthermore, historical energy data supports calibration of the multi-variable optimization model, guiding the selection of charging input sources, regulated charge rates, and energy drawing schedules. Additionally, the types of energy demand history include aggregate station-wide records used for macro-level planning, user-specific consumption traces for personalized scheduling, and slot-level energy delivery logs for load distribution analysis across the plurality of charging slots. Integration of energy demand history into predictive control frameworks ensures consistent energy availability, demand-responsive system behavior, and efficient resource allocation within the swappable battery infrastructure.
As used herein, the term “anomaly detection module” refers to a computational subcomponent of the demand scheduler within the centralized control module of a swappable battery charging station, designed to identify deviations in operational behavior that differ from predicted or expected patterns. Specifically, the module continuously compares real-time data streams, including battery swapping frequency, energy consumption per transaction, user interaction metrics, and charging slot utilization against established baselines derived from trained machine learning models. Further, deviations are quantified using statistical thresholds and control bands configured to reflect acceptable operational tolerances. Upon detection of anomalies, the module triggers corrective mechanisms such as demand forecast recalibration, charging schedule adjustments, or alert generation for system diagnostics. The anomaly detection module enhances the robustness and responsiveness of the energy management system by preventing over- or under-allocation of energy resources, identifying emerging load patterns, and isolating potentially faulty components or abnormal user behaviors. Furthermore, the data inputs include timestamped transaction logs, user behavior patterns, battery condition parameters, and historical energy demand records. Types of anomaly detection implementations include rule-based systems operating on fixed thresholds, statistical learning models adapting to seasonal and cyclical behavior, and neural network-based modules capable of identifying complex, non-linear deviations. Integration of anomaly detection across the plurality of charging slots ensures localized monitoring and rapid system-wide response to irregularities, maintaining operational stability, energy efficiency, and predictive accuracy within the swappable battery charging infrastructure.
As used herein, the term “predefined tolerance band” refers to a statistically established range within which operational deviations are acceptable during the monitoring and prediction of energy demand in a swappable battery charging station. The range is configured based on historical system performance metrics, user behavior variability, transaction timing accuracy, and slot-level energy throughput consistency. Further, the tolerance band functions as a decision boundary within the anomaly detection module of the demand scheduler, enabling differentiation between normal fluctuations and statistically significant anomalies. Real-time values for battery swaps, energy consumption, and user interaction frequency are continuously compared against predicted values generated by a machine learning-based engine; deviations falling outside the pre-defined tolerance band trigger recalibration of demand forecasts or adjustments in scheduling logic. The tolerance band is dynamically aligned with system parameters such as time-of-day load variation, peak usage intervals, and renewable energy availability forecasts. Types of tolerance bands include fixed bands based on historical averages, adaptive bands that self-adjust in response to long-term behavioral trends, and context-specific bands applied selectively across the plurality of charging slots based on usage frequency, user classification, or energy source configuration. Advantageously, the implementation of pre-defined tolerance bands ensures predictive accuracy, demand alignment, and operational stability across the entire energy management framework of the swappable battery charging station.
As used herein, the term “adaptive charging profile” refers to a dynamically selected charging strategy applied to a battery unit within a swappable battery charging station, configured based on real-time battery condition parameters and available input source capacity. The charging control unit within the centralized control module determines the most appropriate charging profile by analyzing parameters such as state-of-charge, internal resistance, battery temperature, voltage level, and current draw. Based on the abovementioned metrics, the system selects from a set of predefined charging modes, including Constant Current (CC), Constant Voltage (CV), and pulse charging, ensuring optimal energy transfer efficiency, thermal safety, and battery longevity. Each profile is calibrated to match the electrochemical behavior of the battery under current operational conditions and also aligns with the energy delivery constraints determined by the energy source allocator. Adaptive charging profiles are recalculated as battery parameters evolve during the charging process, allowing seamless transitions between modes to maintain performance and safety thresholds. Integration of adaptive profiles across the plurality of charging slots allows slot-specific control, enabling parallel charging of heterogeneous battery types under differing profiles without compromising station-wide energy efficiency. Types of adaptive charging profiles include static baseline profiles for standard operating conditions, real-time sensor-driven profiles for active environmental adaptation, and predictive profiles that incorporate battery usage history and degradation trends. Implementation of adaptive charging profiles ensures intelligent energy allocation, thermal stability, and extended battery service life within the high-throughput operational environment of the swappable battery charging infrastructure.
As used herein, the term “constant current” refers to a charging mode wherein a fixed electrical current is supplied to a battery unit throughout the initial phase of the charging cycle within a swappable battery charging station. The constant current mechanism maintains a uniform current and also allows the voltage to rise as the battery charges, enabling efficient energy transfer during the low to mid state-of-charge range. The charging control unit within the centralized control module selects constant current as part of an adaptive charging profile based on real-time battery condition parameters such as initial state-of-charge, temperature, and internal resistance. Further, constant current mode ensures minimized thermal stress and uniform charge distribution across battery cells, making it suitable for fast, controlled energy replenishment in early charging stages. Further, the constant current mode is applied selectively across the plurality of charging slots based on slot-specific battery diagnostics, allowing simultaneous execution of different charging strategies across multiple batteries. Types of constant current applications include fixed-rate profiles for uniform battery chemistries, load-adaptive current settings based on energy input constraints, and precision-controlled current regulation for fast-charging operations. Implementation of constant current charging within the overall energy management framework supports consistent battery performance, controlled thermal dynamics, and optimized energy utilization across the swappable battery charging station infrastructure.
As used herein, the term “constant voltage” refers to a regulated charging mode in which a fixed voltage level is maintained across a battery unit during the terminal phase of the charging cycle within a swappable battery charging station. Specifically, the charging control unit, operating under the centralized control module, initiates constant voltage mode once the battery reaches a predefined voltage threshold, transitioning from the initial constant current phase. During constant voltage charging, the supplied current gradually decreases in response to the increasing internal resistance of the battery, enabling a controlled completion of the charge cycle and simultaneously preventing overcharging and thermal buildup. The charging mode relies on continuous monitoring of real-time battery condition parameters such as voltage stability, temperature gradient, and charge acceptance rate, ensuring precise regulation and enhanced safety. Constant voltage is integrated into adaptive charging profiles and deployed selectively across the plurality of charging slots depending on battery state-of-charge, energy source availability, and slot-level load balancing requirements. Types of constant voltage implementations include fixed-voltage control profiles for standardized battery chemistries, temperature-compensated voltage profiles for thermally sensitive environments, and dynamic voltage ceiling adjustments based on aging characteristics or prior usage history. Application of constant voltage mode enhances battery lifespan, maintains voltage uniformity, and contributes to the overall energy dispensing efficiency within the optimized energy management framework of the swappable battery charging station.
As used herein, the term “pulse charging” refers to an advanced charging technique in which electrical current is delivered to a battery unit in discrete pulses rather than as a continuous flow, forming part of the adaptive charging profile framework implemented by the charging control unit within the centralized control module of a swappable battery charging station. Each pulse consists of a high-current charging burst followed by a short rest period, allowing electrochemical stabilization within the battery and reducing internal heat accumulation. The charging control unit determines pulse amplitude, duration, and frequency based on real-time battery condition parameters such as state-of-charge, impedance, and thermal profile. Pulse charging enhances ion diffusion, minimizes electrode degradation, and promotes uniform charge distribution, especially in battery chemistries with high energy density or thermal sensitivity. Deployment across the plurality of charging slots occurs selectively, where specific slots apply pulse charging to battery units exhibiting non-linear charge acceptance behavior or requiring enhanced cycle life. Integration with the energy source allocator ensures that pulsed current demand remains within dynamic capacity constraints of the selected input source. Types of pulse charging implementations include fixed-frequency pulsing for standard batteries, variable-frequency pulsing tailored to state-of-health metrics, and high-efficiency regenerative pulse charging designed for rapid energy replenishment with reduced heat dissipation. Advantageously, the use of pulse charging within the multi-mode charging strategy improves energy transfer efficiency, extends battery longevity, and contributes to the precision-controlled energy dispensing operation of the swappable battery charging station infrastructure.
As used herein, the term “power evaluation module” refers to a critical subcomponent of the energy source allocator integrated within the centralized control module of a swappable battery charging station, designed to assess and validate the quality and suitability of grid-based input sources for real-time charging operations. The module performs an analytical evaluation of grid-side electrical parameters, including voltage stability, current availability, and Total Harmonic Distortion (THD), determining whether the supplied electrical characteristics align with operational thresholds required for safe and efficient energy transfer. Evaluation is executed continuously and in synchronization with battery charging demand forecasts and adaptive profile requirements, ensuring that input power characteristics do not degrade battery health or system reliability. Power evaluation module interfaces with the demand scheduler and charging control unit, allowing dynamic routing of charging operations to charging slots aligned with optimal input conditions. The applications across the plurality of charging slots include individual slot-level power qualification, cluster-based source assignment based on THD limits, and distributed input validation for load balancing across heterogeneous grid nodes. Types of power evaluation configurations include fixed-threshold monitoring aligned with regulatory standards, adaptive evaluation driven by temporal load behavior, and predictive input analysis based on historical fluctuation trends. Implementation of the power evaluation module supports intelligent source selection, reduces energy loss, and ensures high-integrity grid utilization within the intelligent energy management framework of the swappable battery charging infrastructure.
As used herein, the terms “Total Harmonic Distortion” and “THD” are used interchangeably and refer to a quantitative measure of waveform distortion in electrical power systems, representing the extent to which voltage or current waveforms deviate from their ideal sinusoidal shape due to the presence of harmonic frequencies. The THD is a key grid-side electrical parameter analysed by the power evaluation module embedded in the energy source allocator of the centralized control module. The THD directly impacts power quality, system efficiency, and the thermal stress imposed on battery charging circuits. The power evaluation module continuously monitors THD levels in real time, ensuring that input energy from the grid remains within predefined harmonic distortion limits before routing it to the charging slots. Excessive THD leads to inefficient energy transfer, component degradation, and increased heating of battery units, necessitating dynamic rerouting or source switching. The applications across the plurality of charging slots involve distributed THD validation, where each slot or group of slots receives power conditioned to individual tolerance thresholds. Types of THD management include slot-specific THD filtering for high-sensitivity batteries, synchronized THD tracking across clustered slots for load balancing, and predictive harmonic profiling to support future energy source allocation. Integration of THD analysis ensures grid compliance, equipment protection, and consistent energy quality in the overall operation of the swappable battery charging system.
As used herein, the term “forecast module” refers to a functional component of the energy source allocator embedded within the centralized control module of a swappable battery charging station, designed to anticipate near-term renewable energy availability based on meteorological and environmental predictions. The module analyses data such as, but not limited to, solar irradiance, wind speed, temperature gradients, and atmospheric patterns to generate predictive models for incoming energy generation from multiple renewable energy sources. The forecast outputs influence charging input source selection by pre-biasing renewable energy allocation over grid power whenever projected conditions indicate sufficient clean energy production, thus optimizing energy sustainability and cost-efficiency. The forecast module operates in conjunction with the demand scheduler to align renewable energy availability with predicted energy demand patterns, enabling proactive charging slot assignment and charging mode configuration. Across the plurality of charging slots, the forecast module supports differentiated source routing by preselecting slots expected to be serviced by renewable energy during specific time intervals. Types of forecast module implementations include real-time solar irradiance forecasting for photovoltaic input optimization, short-horizon wind availability modeling for wind-based energy integration, and multi-source hybrid forecast aggregation for mixed renewable systems. Each forecast strategy contributes to dynamic source allocation, grid relief, and maximized energy dispensing efficiency, ensuring intelligent, data-driven energy management across all charging operations within the swappable battery infrastructure.
As used herein, the term “weighted decision logic” refers to a structured computational mechanism implemented within the energy source allocator of the centralized control module in a swappable battery charging station, configured to perform dynamic selection of charging input sources based on assigned priority weights. The logic framework assigns quantitative weights to multiple influencing parameters, including grid-side electrical characteristics, renewable energy forecast data, predicted energy demand patterns, and charging slot availability. Source selection is executed by evaluating the cumulative weight score of each input option and routing charging energy to the slots with the most optimal input-to-load efficiency ratio. The weighted decision logic ensures real-time balancing between renewable energy and grid power based on resource availability, cost, and system performance requirements. The execution occurs in coordination with the power evaluation module and forecast module to establish a continuous optimization loop that governs the quality, sustainability, and economy of charging input. Across the plurality of charging slots, weighted decision logic enables differential source mapping, where specific slots are powered by high-weighted sources depending on localized load or source alignment. Types of weighted decision logic applications include threshold-based allocation for high-demand slots, proportional source distribution based on energy forecast confidence scores, and anomaly-adjusted weighting for outlier slot behaviors. Integration of this logic system supports intelligent, context-aware energy routing that maximizes operational efficiency and supports adaptive, scalable energy management in high-density swappable battery charging environments.
In accordance with a first aspect of the present disclosure, there is provided a system for optimized energy management in a swappable battery charging station, the system comprising:
- a plurality of charging slots configured to accommodate and electrically interface with a plurality of battery units;
- a centralized control module, the centralized control module comprising:
- a demand scheduler configured to track real-time battery swapping requests and predict energy demand patterns;
- a charging control unit configured to regulate charge rate selection for the battery units based on real-time battery condition parameters;
- an energy source allocator configured to dynamically select a charging input source between a grid and a plurality of renewable energy sources; and
- a grid transaction manager configured to control the drawing of energy from the grid or the supply of energy to the grid,
wherein the centralized control module is configured to dynamically maximize energy dispensing efficiency of the charging station via a multi-variable optimization model based on the predicted energy demand patterns, regulated charge rate, selected charging input source, and energy drawing control.
Referring to figure 1, in accordance with an embodiment, there is described a system 100 for optimized energy management in a swappable battery charging station 102. The system 100 comprises a plurality of charging slots 104 configured to accommodate and electrically interface with battery units 106. Further, the system 100 comprises a centralized control module 108. The centralized control module 108 comprises a demand scheduler 110 configured to track real-time battery swapping requests and predict energy demand patterns. Further, the centralized control module 108 comprises a charging control unit 112 configured to regulate charge rate selection for the battery units 106 based on real-time battery condition parameters. Furthermore, the centralized control module 108 comprises an energy source allocator 114 configured to dynamically select a charging input source between a grid 116 and a plurality of renewable energy sources 118. Moreover, the centralized control module 108 comprises a grid transaction manager 120 configured to control the drawing of energy from the grid 116 or the supply of energy to the grid 116. Additionally, the centralized control module 108 is configured to dynamically maximize energy dispensing efficiency of the charging station 102 via a multi-variable optimization model based on the predicted energy demand patterns, regulated charge rate, selected charging input source, and energy drawing control.
In an embodiment, the system 100 for optimized energy management in a swappable battery charging station 102 operates through coordinated interaction among a plurality of charging slots 104 and a centralized control module 108. Specifically, the charging slots 104 are configured to accommodate and electrically interface with battery units, enabling controlled charging operations under supervisory logic. Further, the centralized control module 108 receives real-time data inputs, including battery swapping requests, condition parameters from connected battery units 106, and availability indicators from energy sources. Based on the abovementioned inputs, the centralized control module 108 executes a multi-variable optimization model to determine the most efficient charging strategy, balancing real-time energy demand, battery health parameters, available energy sources, and grid interaction protocols. Furthermore, the regulation of charging rates, source selection, and power transaction management occurs dynamically and continuously, ensuring that energy is dispensed under optimal cost-performance conditions. The demand scheduler 110 within the centralized control module 108 tracks incoming battery swapping requests in real time and processes historical usage data to forecast energy demand patterns. Further, the charging control unit 112 regulates the charge rate by evaluating battery-specific parameters such as state of charge, internal resistance, and temperature. Furthermore, charge rates are selected adaptively to maintain optimal battery health and align with the predicted energy requirement curve. Moreover, the energy source allocator 114 dynamically selects between the grid 116 and a plurality of renewable energy sources 118 based on current availability and operational metrics. Simultaneously, the grid transaction manager 120 monitors the state of the grid, including energy prices and load conditions, and executes decisions regarding energy import or export to maintain the system 100 balance and capitalize on favourable tariff windows. Additionally, the system 100 uses real-time responsiveness to track fluctuating demand patterns, efficiently utilizes available renewable and non-renewable energy sources, and minimizes dependency on grid energy during peak load conditions. Further, the multi-variable optimization model ensures maximal energy efficiency and cost-effectiveness and simultaneously prolongs battery life through condition-aware charging control. Furthermore, the energy source allocation and grid interaction are managed with precision, resulting in reduced operational expenditure and improved sustainability of the charging infrastructure. The integration of predictive analytics with real-time control enhances system intelligence, supporting high-throughput, low-latency battery swapping operations under variable usage scenarios.
In an embodiment, the demand scheduler 110 comprises a machine learning-based engine trained with historical battery swapping records, wherein the historical battery swapping records comprise timestamped transaction logs, user behavior patterns, and energy demand history. The demand scheduler 110 is configured within the centralized control module 108 and employs a machine learning-based engine trained on historical battery swapping records to enable predictive energy management. The historical records include timestamped transaction logs, user behavior patterns, and energy demand history, forming a structured dataset for training the predictive model. Further, the machine learning engine utilizes supervised or semi-supervised learning algorithms to extract correlations between temporal usage patterns and corresponding energy consumption. Specifically, the real-time battery swapping requests are continuously ingested and compared against learned behavior models to generate dynamic forecasts of upcoming energy demand. The output of the predictive model serves as an input to the optimization model in the centralized control module 108, allowing pre-emptive resource allocation and energy routing decisions. Furthermore, the training phase of the machine learning engine involves multiple iterations of model optimization based on historical usage characteristics, including seasonal variations, peak/off-peak demand cycles, and user-specific activity trends. Feature extraction methods isolate high-influence variables such as daily frequency of swaps, time-of-day usage clusters, and user-specific demand intervals. Furthermore, the inference procedure applies the trained model to current system data to predict short-term and mid-term energy demand with high temporal resolution. Forecasted demand outputs guide the charging control unit in scheduling charging slots, prioritizing charging profiles, and assigning energy input sources. Moreover, the prediction accuracy is continuously updated through feedback mechanisms, allowing adaptive learning based on deviation from actual demand, thereby ensuring alignment with dynamic operational conditions. Additionally, the integration of a machine learning-based demand scheduler 110 enables anticipatory energy management, reducing latency in response to battery swapping requests and minimizing energy wastage through targeted charging. Further, forecast-driven control enhances energy throughput by aligning energy sourcing and charge scheduling with expected demand peaks, leading to reduced grid 116 load during high-tariff periods and better utilization of renewable inputs. Consequently, the system 100 maintains higher service availability and faster turnaround times at the charging station 102, enhancing user satisfaction. Energy storage and dispatch operations benefit from predictive intelligence, allowing improved lifecycle performance of battery units 106 and reducing total cost of operation.
In an embodiment, the machine learning-based engine is configured to predict future energy demand by processing real-time swapping requests in combination with trained data. The machine learning-based engine within the demand scheduler 110 processes real-time battery swapping requests in combination with trained data to generate future energy demand predictions. Real-time input data includes, but is not limited to, current timestamp, location identifier, and battery unit identifiers associated with the swap request. Further, the engine retrieves relevant features from the trained dataset, including historically associated user behavior patterns, swap frequencies, and contextual temporal patterns. Using pattern-matching and predictive modeling techniques, such as regression trees or neural networks, the engine outputs a time-series forecast representing anticipated energy demand within defined intervals. The forecasting process executes in low-latency computational cycles to maintain synchronization with incoming request streams. Furthermore, the inference logic integrates both short-term and long-term dependencies by applying sliding window analytics and time-decay weighting on historical data, and swapping events are encoded into structured feature vectors, enabling rapid classification into high-demand or low-demand categories. The forecast results are available to the centralized control module 108 in real time and used by the optimization engine to modify energy allocation strategies, update charging slot activation schedules, and initiate pre-charging operations for anticipated demand surges. Furthermore, the concurrent execution of the prediction model and the charging control logic maintains continuous alignment between projected demand curves and ongoing energy management operations. Moreover, the adaptation mechanisms within the machine learning engine retrain the model periodically based on newly accumulated real-time swap records to preserve prediction accuracy. Anticipated demand awareness improves energy resource distribution, minimizes idle time across charging slots, and prevents underutilization of renewable sources. Additionally, the adaptive forecasting ensures reduced energy draw from the grid during forecasted low-demand intervals, supporting energy arbitrage and lowering operational costs. Energy storage units and available grid input are matched efficiently to predicted high-demand windows, maintaining service quality and also optimizing energy economics. Predictive functionality also reduces response time for charging system activation, ensuring faster turnaround and improved user throughput at the battery swapping interface.
In an embodiment, the demand scheduler 110 comprises an anomaly detection module 122 configured to compute deviations between actual swapping requests and predicted energy demand patterns generated by the machine learning engine. The anomaly detection module 122 within the demand scheduler 110 computes deviations between actual battery swapping requests and predicted energy demand patterns generated by the machine learning engine. Further, real-time swap request data is continuously compared against the forecasted demand curve using statistical deviation metrics such as mean absolute error, standard deviation bounds, or residual analysis. Further, the anomaly detection module 122 receives actual swap timestamps, location-specific swap counts, and user-specific identifiers, and aligns the data temporally with the predicted demand profile. Discrepancies are quantified as deviation scores and categorized into predefined anomaly classes based on severity thresholds. The aforementioned classifications are used by the centralized control module 108 to determine corrective actions in the optimization loop. Furthermore, deviation computation involves mapping predicted versus actual request patterns over rolling time windows, with dynamic recalibration triggered when deviation scores exceed configured baseline thresholds. Anomalies are further segmented by location, user behavior, and energy consumption variance to isolate systemic versus random deviations. Additionally, detected anomalies are flagged and logged, and the flagged data is forwarded to the machine learning engine for model retraining. The anomaly detection module 122 operates in parallel with the prediction and charging control processes, ensuring that deviation handling occurs without interrupting ongoing energy management functions. Consequently, integration of the anomaly detection module enhances resilience and adaptability of the energy management system by ensuring continuous alignment between predictive demand models and real-world operational data. Further, immediate identification and classification of anomalous behavior prevent overestimation or underestimation of energy demand, thereby stabilizing energy allocation and avoiding energy deficits or overcommitments. Furthermore, the feedback loop between anomaly detection and model retraining maintains high prediction fidelity under evolving user behavior and environmental conditions.
In an embodiment, the anomaly detection module 122 is configured to compare the computed deviations with a pre-defined tolerance band and dynamically adjust the predicted energy demand patterns. The tolerance band is configured based on historical variance thresholds, statistical confidence intervals, and operational constraints of the charging station. Further, each computed deviation from the actual battery swapping requests is evaluated against the defined band to determine the implication of the deviation. Furthermore, deviations falling within the tolerance band are treated as expected fluctuations, although those exceeding the band trigger adjustment logic. Additionally, adjustment logics are applied to predicted energy demand patterns, which involves modifying time-series data points using residual correction techniques and updating input feature weights based on deviation classification. Further, the machine learning engine receives flagged anomaly vectors and executes localized retraining or smoothing algorithms to update the prediction model without full reinitialization. Furthermore, the updated model parameters are deployed to the demand scheduler 110 in real time, ensuring continuity in forecast availability. The centralized control module 108 incorporates the adjusted prediction into the multi-variable optimization model, allowing real-time adaptation of charging schedules, energy source selection, and grid transaction logic based on corrected demand expectations. Moreover, the entire cycle maintains deterministic timing to avoid lag in energy management decision-making. Consequently, comparison of deviations with the predefined tolerance band and corresponding dynamic adjustment of energy demand patterns ensures sustained alignment between system predictions and real-time user behavior. Furthermore, the ability to update predictions within strict tolerances enhances robustness under fluctuating or abnormal usage conditions. Moreover, operational stability is maintained without manual intervention, supporting autonomous control over charging slot allocation and energy input routing.
In an embodiment, the charging control unit 112 is configured to receive real-time battery condition parameters and select an adaptive charging profile based on current battery conditions and available source capacity. The battery condition parameters include state of charge, temperature, internal resistance, charge cycle count, and voltage stability. The above-mentioned parameters are transmitted from sensors embedded within the battery units and processed by the charging control unit 112 to determine the appropriate charging mode. Further, the selection of the charging profile involves rule-based and data-driven decision logic that maps condition values to predefined charging modes. The charging control unit 112 operates continuously during active charging sessions, dynamically adjusting current and voltage levels in response to fluctuations in battery condition inputs. Additionally, charging profile selection is executed using a decision matrix aligned with operational constraints and battery health optimization targets. For instance, higher internal resistance or elevated temperature values trigger the selection of a lower current mode to prevent degradation. In scenarios with high source capacity availability, the charging control unit 112 selects higher-efficiency modes to reduce total charging time. Further, the charging control unit 122 communicates directly with the energy source allocator 114 to validate input capacity before applying the selected profile. Furthermore, real-time feedback from battery units 106 is used to monitor charging effectiveness and maintain alignment with the selected profile. Moreover, charging events are logged and stored for audit, model refinement, and service history tracking. Consequently, the aforementioned steps prevent overcharging, reduce thermal stress, and extend battery lifecycle by aligning charging intensity with actual cell behavior. Furthermore, efficiency improvements in charge delivery reduce energy losses and shorten charging cycles, supporting higher throughput in the swappable battery infrastructure. Moreover, real-time adjustment capability enhances responsiveness to condition fluctuations, maintaining safety compliance and improving the reliability of the charging process.
In an embodiment, the adaptive charging profile is dynamically selected from a plurality of predefined modes, wherein the predefined modes comprise Constant Current (CC), Constant Voltage (CV), and pulse charging, selected based on the real-time battery condition parameters. In the constant current mode, a fixed current is supplied until the battery reaches a specific voltage threshold. Upon reaching the voltage limit, the profile transitions to Constant Voltage mode, as the voltage is held constant as the current gradually reduces. Further, pulse charging mode delivers intermittent bursts of current separated by rest periods, selected when thermal or electrochemical stress indicators exceed defined limits. Furthermore, mode selection occurs through a rules engine that processes live telemetry data, including, but not limited to, temperature gradients, voltage rise rate, and impedance fluctuations. For instance, elevated temperatures or high internal resistance trigger a transition from CC to pulse charging to mitigate thermal accumulation. Conversely, batteries exhibiting stable voltage response and low impedance are assigned CC or CV modes for efficient energy transfer. Moreover, transitions between modes are synchronized with battery behavior thresholds to prevent voltage overshoots, current surges, or thermal runaway, preserving battery integrity and charge consistency. Consequently, dynamic selection from predefined charging modes based on real-time battery conditions enhances safety, performance, and energy efficiency within the charging station. Further, tailored application of CC, CV, and pulse profiles ensures that charge delivery remains aligned with battery-specific requirements under varying environmental and load conditions. The adaptive logic reduces the risk of overheating, overcharging, and electrolyte degradation, directly contributing to longer battery lifespan and lower replacement costs.
In an embodiment, the energy source allocator 114 comprises a power evaluation engine 124 configured to dynamically evaluate grid-side electrical parameters comprising voltage stability, current availability, and Total Harmonic Distortion (THD). The power evaluation engine 124 continuously monitors incoming power quality metrics from the grid interface using embedded sensors and power quality analysers. Further, voltage stability is assessed by measuring voltage fluctuation over time and comparing the fluctuation against configured stability thresholds. Furthermore, current availability is determined based on real-time current draw capacity and phase balance. Moreover, the THD is quantified by analyzing harmonic content using Fast Fourier Transform (FFT) techniques applied to voltage and current waveforms, identifying the presence of distortion components that affect charging performance. Additionally, evaluation of grid-side parameters occurs in synchronization with charging cycle scheduling to ensure compatibility between source quality and charging requirements. Further, as voltage fluctuations exceed acceptable thresholds or the THD levels compromise waveform integrity, the energy source allocator 114 flags the grid input as unsuitable for high efficiency charging. The allocator 114 further communicates status to the centralized control module 108 for dynamic source reassignment. Consequently, real-time evaluation of grid-side electrical parameters ensures that only power sources meeting quality standards are allocated for battery charging, protecting battery units 106 from exposure to unstable or distorted input. Further, use of high-integrity grid 116 input enhances charge efficiency, reduces heat generation, and minimizes energy loss due to harmonic interference. Moreover, rejection of substandard grid 116 input prevents charging inefficiencies and safeguards battery health, extending operational lifespan and reducing system maintenance overhead. Furthermore, the dynamic evaluation process contributes to optimal energy routing, supports fault-tolerant operation, and aligns with predictive energy demand.
In an embodiment, the energy source allocator 114 comprises a forecast module 126 configured to determine charging input based on near-term weather predictions to pre-bias renewable energy allocation. The forecast module 126 receives meteorological data from integrated weather APIs or localized weather sensors, including parameters such as solar irradiance, wind speed, cloud cover, temperature, and humidity. Further, the parameters are processed using time-series forecasting algorithms to estimate short-term renewable energy generation potential from photovoltaic and wind sources. The forecast module 126 operates within fixed time intervals to align energy source planning with fluctuating environmental conditions and updates predictions periodically based on incoming weather data streams. Furthermore, based on predicted renewable energy availability, the forecast module 126 generates a source preference profile that pre-biases the energy source allocator 114 toward selecting renewable inputs during periods of expected surplus. As solar irradiance values exceed configured thresholds or wind patterns fall within optimal operating ranges, the allocator 114 schedules corresponding energy flows from the renewable subsystem 118 to the charging control unit 112. Moreover, the forecast module 126 communicates directly with the centralized control module 108 to update optimization parameters, ensuring that charging operations are aligned with predicted renewable availability. Additionally, forecast outputs also inform energy storage planning and grid transaction decisions, enabling proactive management of surplus or deficit conditions in advance of actual demand events. Consequently, incorporation of a weather-driven forecast module into the energy source allocator enhances renewable energy utilization by aligning source selection with environmental conditions. Further, pre-biasing the allocation logic toward forecasted renewable availability reduces dependency on grid input, lowers energy costs, and improves the environmental footprint of the charging station. Furthermore, predictive renewable sourcing increases charging efficiency and enables stable operation even under variable weather conditions.
In an embodiment, the energy source allocator 114 is configured to dynamically select charging input by evaluating grid-side electrical parameters and forecasting renewable energy availability, wherein the selection is performed using a weighted decision logic based on predicted demand. The power evaluation module 124 measures voltage stability, current availability, and the THD, and simultaneously, the forecast module 126 estimates renewable generation potential using weather prediction data. Further, the inputs are normalized and converted into weighted decision variables. The decision logic applies pre-assigned weights to each input parameter based on the input parameter’s influence on charging efficiency, cost, and reliability. Furthermore, predicted energy demand, generated by the demand scheduler 110, acts as a controlling factor that adjusts the weight distribution dynamically based on urgency, expected load, and time sensitivity. The weighted decision logic executes iterative evaluations at defined time intervals. Each potential energy source, including grid 116, solar, and wind, is scored against the weighted criteria, and the highest scoring source is selected for allocation to the charging control unit 112. In scenarios, with demand spikes coincide with poor grid power quality, the logic shifts preference to available renewable sources, even with moderate availability, to maintain charging stability. Further, in case, renewable output is low and grid quality parameters meet optimal thresholds, grid energy is prioritized. The energy source allocator 114 stores historical decision outcomes and their performance metrics to refine future weight adjustments through feedback learning, ensuring continuous improvement in selection accuracy. Consequently, execution of weighted decision logic for charging input selection optimizes energy sourcing by balancing quality, availability, cost, and demand alignment. Further, integration of power quality evaluation with environmental forecasting and predictive demand modelling ensures that source selection reflects real-time operational priorities. Furthermore, the weighted decision logic maximizes the use of renewable energy when environmental conditions are favourable and maintains system stability under grid fluctuations. Moreover, dynamic weight redistribution enables the system to respond to varying conditions without manual intervention, improving automation and reliability.
In an embodiment, the grid transaction manager 120 comprises a bi-directional interface configured to control energy export to or import from the grid 116, based on dynamic tariff rate monitoring. The bi-directional interface enables both inward and outward energy flow through power electronic converters and switching modules managed by the centralized control module 108. Further, real-time tariff rates are obtained through integration with utility tariff databases or smart grid communication protocols. Furthermore, the grid transaction manager 120 continuously compares current tariff rates against configured thresholds to determine optimal transaction direction. As import tariffs fall below the defined cost-efficiency limit, the grid transaction manager 120 initiates energy draw from the grid 116. Conversely, as export tariffs exceed profit thresholds, surplus energy from renewable sources 118 or storage units is routed to the grid 116. Moreover, energy flow control is executed through synchronized commands to solid-state switching devices and current regulators embedded in the grid interface hardware. The grid transaction manager 120 receives input from the energy source allocator 114 and forecast module 126 to assess the availability of surplus energy suitable for export. Simultaneously, the grid transaction manager 120 receives input from the demand scheduler 110 and charging control unit 112 to estimate upcoming internal energy demand. Based on the composite assessment, the grid transaction manager 120 determines whether to permit or block grid interaction, aligning transaction timing with both financial and operational efficiency. Consequently, implementation of a bi-directional grid interface controlled by dynamic tariff monitoring supports real-time energy arbitrage, enabling cost minimization and revenue generation within the swappable battery charging station. Further, import and export transactions are aligned with pricing signals and energy availability, improving operational economics. Furthermore, the grid transaction manager 120 ensures energy availability during high-demand intervals and capitalizing on excess renewable production during off-peak periods. Moreover, dynamic control of grid 116 interaction enhances energy flexibility, reduces stress on internal storage units, and contributes to grid stability by supporting load balancing.
In an embodiment, the grid transaction manager 120 is configured to perform energy arbitrage by storing energy during low-tariff windows and exporting energy during peak-tariff conditions. The centralized control module 108 receives real-time tariff data from the utility interface and categorizes tariff windows into low, medium, and peak bands based on predefined pricing thresholds. Further, during low-tariff periods, the grid transaction manager 120 authorizes energy import operations and directs the incoming energy to integrated energy storage subsystems. Furthermore, charging operations are coordinated such that non-urgent charging cycles are deferred to the windows to maximize cost-efficiency. The energy storage units are charged in accordance with available capacity and energy demand forecasts, preventing overcharging or energy overflow. Moreover, during peak-tariff intervals, the grid transaction manager 120 assesses stored energy availability and compares the availability against the current energy demand and grid export profitability. Additionally, in case internal energy reserves exceed demand requirements, the manager triggers export operations through the bi-directional interface. Further, the export control logic prioritizes stability and synchronization with the grid phase to avoid injection disturbances. Consequently, execution of energy arbitrage through tariff-aware storage and export enhances the economic performance of the swappable battery charging station 102. Importing energy at low tariffs reduces average energy procurement costs, and exporting at high tariffs generates revenue from surplus capacity. The arbitrage strategy reduces dependence on real-time grid input during peak pricing, improving energy availability and charging continuity during high-demand intervals. Use of predictive tariff scheduling ensures proactive resource allocation and maximizes return on stored energy investment.
In an embodiment, the optimization model is executed at predefined time intervals and re-executed in response to variations in battery condition parameters, energy input availability and predicted demand patterns exceeding configured control setpoints. The predefined execution intervals are established based on system latency requirements, energy flow rates, and typical variation frequencies observed in operational data. During each interval, the optimization engine retrieves updated input values from the charging control unit 112, energy source allocator 114, and demand scheduler 110. Further, the optimization model recalculates optimal energy routing, charging priorities, and grid interaction strategies using updated parameters and applies changes through real-time control signals. Furthermore, event-driven re-execution occurs as monitored values cross defined thresholds that represent significant deviations from nominal operating conditions. Thresholds are configured for each monitored variable, such as battery temperature deviation, grid voltage fluctuation, or demand forecast delta. Moreover, in case deviation is detected, the centralized control module 108 triggers an immediate re-optimization cycle. The aforementioned process includes validation of constraint compliance, recalculation of decision weights, and prioritization of critical charging operations. The re-optimization ensures alignment of system performance with current operating context, preventing inefficiencies that arise from reliance on outdated data. Additionally, the model incorporates a feedback mechanism to measure the impact of executed decisions, supporting further refinement of control strategies. Consequently, timely recalculation of energy management strategies ensures accurate alignment between available resources and actual requirements, improving charging station efficiency and reliability. Dynamic adaptation to parameter variations minimizes response latency, maintains safety margins, and reduces system stress during demand peaks or supply instability. Further, continuous optimization reduces unnecessary energy draw, enhances renewable energy utilization, and ensures cost-effective charging operations.
In accordance with a second aspect, there is described a method for optimized energy management in a swappable battery charging station, the method comprising:
- receiving a real-time battery swapping request, via a centralized control module;
- tracking real-time battery swapping requests and predicting energy demand patterns, via a demand scheduler;
- regulating charge rate selection for the battery units based on real-time battery condition parameters and energy demand patterns, via a charging control unit;
- selecting a charging input source between a grid and a plurality of renewable energy sources based on the charge rate selection, via an energy source allocator; and
- controlling drawing of energy from the grid or the supply of energy to the grid, via a grid transaction manager.
Figure 2 describes a method 200 optimized energy management in a swappable battery charging station. At step 202, the method 200 comprises receiving a real-time battery swapping request via a centralized control module. At step 204, the method 200 comprises tracking real-time battery swapping requests, and predicting energy demand patterns, via a demand scheduler. At step 206, the method 200 comprises regulating charge rate selection for the battery units based on real-time battery condition parameters and energy demand patterns, via a charging control unit. At step 208, the method 200 comprises selecting a charging input source between a grid and a plurality of renewable energy sources based on the charge rate selection, via an energy source allocator. At step 210, the method 200 comprises controlling drawing of energy from the grid or the supply of energy to the grid, via a grid transaction manager.
In an embodiment, the method 200 comprises initiating a real-time battery swapping request, via the EV.
In an embodiment, the method 200 comprises deriving the energy demand patterns using machine learning analysis of historical and real-time battery swapping data.
In an embodiment, the method 200 comprises executing regulated charge rate selection, via adaptive control logic.
In an embodiment, the method 200 comprises maximizing energy dispensing efficiency, via a multi-variable optimization model.
In an embodiment, the method 200 comprises initiating a real-time battery swapping request, via the EV. At step 202, the method 200 comprises receiving, a real-time battery swapping request via a centralized control module. Further the method 200 comprises deriving, the energy demand patterns using machine learning analysis of historical and real-time battery swapping data. At step 204, the method 200 comprises tracking, real-time battery swapping requests, and predicting energy demand patterns, via a demand scheduler. At step 206, the method 200 comprises regulating, charge rate selection for the battery units based on real-time battery condition parameters and energy demand patterns, via a charging control unit. Furthermore, the method 200 comprises executing, regulated charge rate selection, via adaptive control logic. At step 208, the method 200 comprises selecting, a charging input source between a grid and a plurality of renewable energy sources based on the charge rate selection, via an energy source allocator. At step 210, the method 200 comprises controlling, drawing of energy from the grid or the supply of energy to the grid, upon selection of the grid for optimized energy management in a swappable battery charging station, via a grid transaction manager. Additionally, the method 200 comprises maximizing energy dispensing efficiency, via a multi-variable optimization model.
The present disclosure presents various advantages including intelligent, adaptive, and energy-efficient operation of a swappable battery charging station through real-time control, predictive analytics, and dynamic optimization. Further, integrated control of charging, grid interaction, and renewable utilization ensures reliable performance, extended battery life, and sustainable energy management.
It would be appreciated that all the explanations and embodiments of the system 100 also apply mutatis-mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combinations of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, and “is” used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:WE CLAIM:
1. A system (100) for optimized energy management in a swappable battery charging station (102), the system (100) comprising:
- a plurality of charging slots (104) configured to accommodate and electrically interface with a plurality of battery units (106);
- a centralized control module (108), the centralized control module (108) comprising:
- a demand scheduler (110) configured to track real-time battery swapping requests and predict energy demand patterns;
- a charging control unit (112) configured to regulate charge rate selection for the battery units (106) based on real-time battery condition parameters;
- an energy source allocator (114) configured to dynamically select a charging input source between a grid (116) and a plurality of renewable energy source (118); and
- a grid transaction manager (120) configured to control the drawing of energy from the grid (116) or the supply of energy to the grid (116),
wherein the centralized control module (108) is configured to dynamically maximize energy dispensing efficiency of the charging station (102) via a multi-variable optimization model based on the predicted energy demand patterns, regulated charge rate, selected charging input source, and energy drawing control.

2. The system (100) as claimed in claim 1, wherein the demand scheduler (110) comprises a machine learning-based engine trained with historical battery swapping records, and wherein the historical battery swapping records comprises timestamped transaction logs, user behavior patterns, and energy demand history.

3. The system (100) as claimed in claim 2, wherein the machine learning-based engine is configured to predict future energy demand by processing real-time swapping request in combination with trained data.

4. The system (100) as claimed in claim 1, wherein the demand scheduler comprises an anomaly detection module (122) configured to compute deviations between actual swapping requests and predicted energy demand patterns generated by the machine learning engine.

5. The system (100) as claimed in claim 4, wherein the anomaly detection module (122) is configured to compare the computed deviations with a pre-defined tolerance band and dynamically adjust the predicted energy demand patterns.

6. The system (100) as claimed in claim 1, wherein the charging control unit (112) is configured to receive real-time battery condition parameters and select an adaptive charging profile based on current battery conditions and available source capacity.

7. The system (100) as claimed in claim 6, wherein the adaptive charging profile is dynamically selected from a plurality of predefined modes and wherein the predefined modes comprises Constant Current (CC), Constant Voltage (CV), and pulse charging, selected based on the real-time battery condition parameters.

8. The system (100) as claimed in claim 1, wherein the energy source allocator (114) comprises a power evaluation module (124) configured to dynamically evaluate grid-side electrical parameters comprising voltage stability, current availability, and Total Harmonic Distortion (THD).

9. The system (100) as claimed in claim 1, wherein the energy source allocator (114) comprises a forecast module (126) configured to determine charging input based on near-term weather predictions to pre-bias renewable energy allocation.

10. The system (100) as claimed in claim 1, wherein the energy source allocator (114) is configured to dynamically select charging input by evaluating grid-side electrical parameters and forecasting renewable energy availability, wherein the selection is performed using a weighted decision logic based on predicted demand.

11. The system (100) as claimed in claim 1, wherein the grid transaction manager (120) comprises a bi-directional interface configured to control energy export to or import from the grid (116), based on dynamic tariff rate monitoring.

12. The system (100) as claimed in claim 10, wherein the grid transaction manager (120) is configured to perform energy arbitrage by storing energy during low-tariff windows and exporting energy during peak-tariff conditions.

13. The system (100) as claimed in claim 1, wherein the optimization model is executed at predefined time intervals and re-executed in response to variations in battery condition parameters, energy input availability and predicted demand patterns exceeding configured control setpoints.

14. A method (200) for optimized energy management in a swappable battery charging station (102), the method (200) comprising:

- receiving, a real-time battery swapping request, via a centralized control module;
- tracking, real-time battery swapping requests and predicting energy demand patterns, via a demand scheduler;
- regulating, charge rate selection for the battery units based on real-time battery condition parameters and energy demand patterns, via a charging control unit;
- selecting, a charging input source between a grid and a plurality of renewable energy sources based on the charge rate selection, via an energy source allocator; and
- controlling, drawing of energy from the grid or the supply of energy to the grid, via a grid transaction manager.