DESC:METHOD AND SYSTEM FOR OPTIMIZING VEHICLE PARAMETERS
CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202421073583 filed on 28/09/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
Generally, the present disclosure relates to energy management. Particularly, the present disclosure relates to optimizing vehicle parameters for energy management in an electric vehicle.
BACKGROUND
Intelligent energy management in an electric vehicle refers to the automated control and optimization of power usage based on real-time internal and external operating conditions. The intelligent energy management enables the electric vehicles to dynamically adjust key operational parameters to maximize driving efficiency, range, and performance. Initially, in hybrid electric vehicles, control strategies were designed to switch between electric and combustion power to improve fuel economy. Over time, as fully electric vehicles gained prominence, intelligent energy management techniques evolved to improve the performance and reliability of modern electric vehicles.
Conventionally, intelligent energy management in electric vehicles comprises predefined strategies based on combinations of the vehicle state, driver input, and so on. Conventional approaches are generally classified into three main types: rule-based control, model-based predictive control, and real-time adaptive control. In rule-based approach, fixed logic such as, but not limited to, reducing power output at low battery levels without learning or adapting to changing conditions. The model-based predictive control approach employs mathematical models to optimize performance over a given route. The real-time adaptive control approach employs live data streams from vehicle sensors to make continuous adjustments during driving. The approaches primarily focus on optimizing energy distribution within the vehicle to extend range, improve battery health, and support safe, efficient driving.
However, there are certain problems associated with the existing or above-mentioned mechanism for controlling at least one operational parameter of an electric vehicle. For instance, the rule-based control approach lacks flexibility and fails to respond to dynamic driving conditions or unexpected infrastructure availability. Further, the model-based predictive control approach excludes real-time data of external resources, such as battery stations, resulting in inaccurate range estimations. Furthermore, the real-time adaptive control approach frequently operates without integrating live information from the charging infrastructure or considering battery degradation trends. A critical gap exists in the ability to correlate battery state with real-time distance to available charging stations and adjust the vehicle behaviour accordingly, which leads to inefficient energy use, unreliable range predictions, and increased risk of vehicle immobilization. Consequently, the limitations of the above-mentioned approaches contribute to range anxiety, suboptimal power usage, limited access to charging infrastructure, and inadequate driver support, all of which reduce overall confidence and efficiency in electric vehicle operation.
Therefore, there exists a need for a system and method of optimizing vehicle parameters that is efficient and overcomes one or more problems as mentioned above.
SUMMARY
An object of the present disclosure is to provide a system for controlling at least one operational parameter of an electric vehicle.
Another object of the present disclosure is to provide a method for controlling at least one operational parameter of an electric vehicle.
Yet another object of the present disclosure is to provide a system and method for controlling at least one operational parameter of an electric vehicle by accurately correlating battery charge status, vehicle location, and charge data availability at a battery station.

In accordance with a first aspect of the present disclosure, there is provided a system for controlling at least one operational parameter of an electric vehicle, the system comprising:
- a positioning module configured to determine a current location of the electric vehicle;
- a battery monitoring configured to detect a current State of Charge (SoC) of the electric vehicle;
- a communication module communicably coupled to at least one battery station via a communication network, and configured to receive charge availability data from the at least one battery station;
- a control module communicably coupled to the positioning module, the battery monitoring module, and the communication module; and
- a display interface operatively connected to the control module,
wherein, the control module is configured to determine an estimated travel range based on the current SoC, and a predicted distance to the battery station based on the current location and the charge availability data, and generates at least one control signal for modulating the at least one operational parameter of the electric vehicle corresponding to the predicted distance exceeding the estimated travel range.

The system and method for controlling at least one operational parameter of an electric vehicle, as described in the present disclosure, are advantageous in terms of accurate prediction of distance and travel range by evaluating real-time battery status, vehicle location, and charge station availability to reach a battery station. In response to scenarios with an insufficient estimated range, active adjustment of key vehicle parameters, such as torque, speed, and regenerative braking, helps conserve energy. Furthermore, continuous optimization is achieved through a closed-loop feedback mechanism, allowing real-time adaptation to changing conditions. In addition, range accuracy improves through the incorporation of a predictive battery degradation model that accounts for aging and performance decay. Moreover, real-time alerts and energy-saving recommendations enhance driver awareness, and as a result, the risk of vehicle immobilization and range anxiety during energy-critical conditions is significantly reduced.

In accordance with another aspect of the present disclosure, there is provided a method of controlling at least one operational parameter of an electric vehicle, the method comprising:
- determining current location of the electric vehicle via a position module;
- detecting a current state of charge of the electric vehicle via a battery monitoring module;
- receiving charge availability data from at least one battery station via a communication module ;
- determining an estimated travel range based on the current SoC, and a predicted distance to the battery station based on the current location and the charge availability data, via a control module; and
- modulating at least one operational parameter of the electric vehicle via the control module.
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 controlling at least one operational parameter of an electric vehicle, in accordance with an embodiment of the present disclosure.
Figure 2 illustrates a flow chart of a method for controlling at least one operational parameter of an electric vehicle, 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 “operational parameters”, “vehicle control parameters”, “performance variables,” and “drive control metrics” are used interchangeably and refer to measurable or controllable variables within an electric vehicle that influence energy consumption and performance of the electric vehicle. Specifically, the operational parameters are dynamic vehicle settings that govern propulsion, energy recovery, and power management. The parameters include, but not limited to, motor torque, regenerative braking intensity, vehicle speed, throttle response, power output limits, and auxiliary system loads. The operational parameters are regulated by one or more control modules. Furthermore, the operational parameters are continuously adjustable in real time based on internal vehicle states, battery condition, and external inputs such as location and charging infrastructure availability. Subsequently, the modulation of operational parameters enables the optimization of energy usage, extension of the electric vehicle's range, and adaptation to changing driving conditions, thereby contributing to efficient and reliable electric vehicle operation.
As used herein, the terms “electric vehicle” and “EV” are used interchangeably and refer to a vehicle configured to be powered wholly or partially by one or more electric motors using energy stored in onboard rechargeable batteries. Specifically, the electric vehicle comprises components for propulsion, energy storage, power management, and control modules designed to optimize driving performance and energy efficiency. The electric vehicle includes interfaces for communication with external infrastructure, such as charging stations, and incorporate modular or swappable battery packs to enable rapid energy replenishment. Furthermore, the electric vehicle integrates sensor and electronic control units that monitor and modulate operational parameters in real time, thereby enhancing driving range, safety, and user experience.
As used herein, the terms “positioning module”, “location module”, and “navigation module” are used interchangeably and refer to a unit configured to determine the current geographic location of the electric vehicle. Specifically, the positioning module is integrated within the electric vehicle and communicably coupled to a control module to provide real-time location data for range estimation. The positioning module includes satellite-based navigation technologies, such as GPS, GLONASS, Galileo, and BeiDou. Furthermore, the positioning module continuously transmits updated spatial coordinates to enable dynamic route planning and energy management based on the electric vehicle's location relative to available charging infrastructure. Subsequently, the positioning module supports intelligent modulation of operational parameters of the electric vehicle by providing accurate location data of the electric vehicle.
As used herein, the terms “State of Charge (SoC)”, “battery charge level”, “remaining battery capacity”, and “current charge status” are used interchangeably and refer to an indicator representing the current available energy within the electric vehicle’s battery relative to the maximum capacity. Specifically, the SoC is determined by a battery monitoring module, which continuously measures electrical parameters such as voltage, current, and temperature to accurately assess the remaining charge. The SoC is further calculated using algorithms that account for battery chemistry, age, and usage history to provide a reliable and real-time energy status. Furthermore, the SoC data is communicated to the control module to support accurate range estimation, energy management, and decision-making for the electric vehicle’s operational adjustments. Subsequently, maintaining precise SoC information reduces the risk of unexpected power depletion, improves battery health management, and enhances overall vehicle performance and safety.
As used herein, the terms “communication module”, “data communication unit”, “connectivity module”, and “vehicle communication interface” are used interchangeably and refer to a unit configured to enable data exchange between the electric vehicle and external infrastructure such as battery charging stations, cloud networks, or remote systems. Specifically, the communication module is integrated within the electric vehicle and communicably coupled to a control module to receive real-time charge availability data from external charging stations through wireless protocols including, but not limited to cellular networks, Wi-Fi, and vehicle-to-everything (V2X) interfaces. The communication module further aggregates and filters incoming data to identify and prioritize reachable charging stations based on availability and proximity. Furthermore, the communication module synchronizes external data with the electric vehicle’s internal data from a positioning module and a battery monitoring module, enabling the control module to make accurate range estimations and perform energy-optimized adjustments. The communication module ensures seamless connectivity, supports infrastructure-aware decision-making, and enhances energy efficiency through continuous real-time communication.
As used herein, the terms “battery station”, “charging station”, “energy replenishment station”, and “battery swapping station” are used interchangeably and refer to an external infrastructure unit configured to provide electric energy to the electric vehicle through battery replacement or charging. Specifically, the battery station is communicably connected to the communication module of the vehicle to transmit real-time charge availability data, station status, and accessibility information. The types of battery stations include fixed charging stations, automated battery swapping stations, and hybrid stations that support both charging and swapping operations. Further, the battery station operates as a networked node that continuously updates its availability, energy capacity, and queue status, enabling the electric vehicle to evaluate the feasibility of reaching the station based on current battery charge and location. Furthermore, the integration of battery station data in the optimization process of the operational vehicle parameter allows intelligent route planning, accurate range estimation, and prioritization of optimal energy replenishment points. Subsequently, the battery station supports reduced waiting times, improved resource utilization, and ensures continuity of vehicle operation under energy-critical conditions.
As used herein, the terms “communication network”, “data exchange network”, “vehicle connectivity network”, and “wireless communication infrastructure” are used interchangeably and refer to a networked medium configured to enable data transmission between an electric vehicle and external infrastructure such as remote servers and cloud-based platforms. Specifically, the communication network facilitates real-time connectivity between the communication module of the electric vehicle and the battery station to support energy management and route optimization functions. The types of communication networks include, but are not limited to, cellular networks such as 4G, 5G, and so on, Wi-Fi, satellite communication network, and vehicle-to-everything (V2X) networks. Further, the communication network supports continuous data flow for transmitting charge availability at the battery station. Furthermore, the network enables secure, low-latency, and scalable connectivity essential for accurate range prediction, remote diagnostics, and seamless integration with intelligent energy infrastructure. Subsequently, the communication network enhances operational transparency, supports real-time decision-making, and improves the responsiveness and reliability of the electric vehicle.
As used herein, the terms “control module” and “energy control unit” are used interchangeably and refer to a processing unit configured to receive, process, and respond to real-time data related to vehicle energy status, location, and external charging infrastructure. Specifically, the control module is integrated within the electric vehicle and is communicably coupled to the positioning module, battery monitoring module, and communication module. Subsequently, the communication module contributes to determining an estimated travel range based on the current SoC of the electric vehicle, and a predicted distance to the battery station, and therefore, decision-making for energy optimization. Further, the control module is configured to evaluate the current SoC of the electric vehicle, predict travel range, and distance to available charging stations. Further, the control module generates encoded control signals containing operational commands for vehicle subsystems, including electronic control units responsible for torque regulation, regenerative braking, and speed limitation. Furthermore, the control module operates in a closed-loop feedback architecture to dynamically adjust the operational parameters based on real-time changes in the current SoC of the electric vehicle, position of the electric vehicle, and charge availability at the battery station. Advantageously, the control module ensures accurate range prediction, prevents energy depletion, reduces risk of vehicle immobilization, and supports intelligent and efficient energy management throughout vehicle operation.
As used herein, the terms “display interface” and “ visual interface” are used interchangeably and refer to a visual output unit configured to present real-time information to a driver regarding the operational status of the electric vehicle, energy metrics, and real-time warning alerts. Specifically, the display interface is operatively connected to the control module and receives updated data related to battery charge status, estimated travel range, predicted distance to charging stations, and modulation of the operational parameters of the electric vehicle. The display interface includes, but not limited to, digital dashboards, touchscreens, heads-up displays, and integrated infotainment panels configured to render graphical and textual data. Further, the display interface is designed to present energy-saving recommendations, real-time alerts, and range warnings in response to dynamic vehicle and environmental conditions. Furthermore, the display interface supports driver awareness by continuously updating visual content based on inputs from the positioning module, battery monitoring module, and communication module. Subsequently, the display interface improves situational awareness, enhances energy-efficient decision-making, and contributes to the seamless interaction between the driver and the electric vehicle.
As used herein, the terms “predicted distance” and “anticipated route length” are used interchangeably and refer to the measurement of the distance between the current location of the electric vehicle and a target location, such as a charging station. Specifically, the predicted distance is determined by the positioning module using current vehicle location data combined with navigation and mapping information. The types of predicted distances include straight-line distance, route-based distance accounting for roadways, and dynamic distance estimates considering traffic or environmental factors. Further, the predicted distance is continuously updated to reflect real-time changes in the electric vehicle position and route conditions. Furthermore, accurate predicted distance estimation enables effective range management, informed decision-making regarding charging station selection, and optimization of operational parameters of the electric vehicle to prevent range depletion.
As used herein, the terms “control signal”, “modulation command”, and “ command signal” are used interchangeably and refer to an encoded data transmission generated to regulate one or more operational parameters of the electric vehicle. Specifically, the control signal is generated by the control module based on synchronized inputs from the positioning module, battery monitoring module, and communication module. The control signal includes structured command sets configured to adjust the operational parameters, such as, but not limited to, torque delivery, regenerative braking intensity, and speed limitation through electronic control units. Further, the control signal is encoded with error detection logic to ensure secure and accurate communication across vehicle networks. Furthermore, the control signal operates in a closed-loop feedback framework, enabling dynamic adjustment of the operational parameters of the electric vehicle. Subsequently, the control signal ensures precise execution of energy-saving strategies, supports predictive control actions, and maintains vehicle performance within optimal thresholds under varying operational scenarios.
As used herein, the terms “predictive battery degradation model”, “battery aging model,” and “battery performance decay model” are used interchangeably and refer to a computational model configured to estimate the reduction in performance, capacity, and efficiency of a battery of the electric vehicle over time. Specifically, the predictive battery degradation model is integrated within the battery monitoring module and operates in coordination with the control module to provide adjusted SoC readings and range predictions based on real-world usage patterns and historical data. The model comprises analytical or machine-learning-based algorithms trained to account for parameters, such as, but not limited to, charge-discharge cycles, temperature exposure, depth of discharge, and charging behaviour. Further, the model continuously refines estimated results using real-time sensor inputs and performance logs, ensuring dynamic adjustment of range estimations and battery health indicators. Furthermore, the predictive battery degradation model enables more accurate energy planning, reduces unexpected power depletion events, and supports long-term battery management strategies by proactively compensating for aging-related losses in battery performance.
As used herein, the terms “filtering algorithm”, “data prioritization logic”, and “selection algorithm” are used interchangeably and refer to a computational procedure configured to process and prioritize data inputs based on predefined relevance or utility criteria. Specifically, the filtering algorithm is embedded within the communication module and operates on charge availability data received from the battery station to identify and rank the most viable charging battery station. The algorithm includes, but is not limited to, weighted scoring models, proximity-based filtering, queue length analysis, and availability prediction methods designed to evaluate stations based on distance, compatibility, energy status, and operational readiness. Further, the filtering algorithm enables the control module to select optimal charging destinations, improve routing decisions, and reduce time lost due to unavailable or distant energy sources. The filtering algorithm supports efficient data handling, enhances decision accuracy, and ensures that the vehicle maintains energy access within practical and strategic constraints.
As used herein, the terms “command sets” and “instruction groups” are used interchangeably and refer to predefined collections of encoded instructions configured to regulate one or more operational parameters of the electric vehicle. Specifically, the command sets are generated by the control module based on inputs received from the positioning module, battery monitoring module, and communication module. The command sets are embedded within the control signal transmitted to an electronic control unit. The command sets include directives to adjust torque delivery, modify regenerative braking intensity, limit maximum vehicle speed, or initiate energy-saving drive modes in response to real-time energy conditions and infrastructure availability. Further, the command sets are encoded using error detection and correction logic to ensure integrity and security during communication across vehicle networks. Furthermore, the command sets enable precise, coordinated modulation of the operational parameters of the electric vehicle by ensuring that each functional unit receives relevant and validated instructions. Subsequently, the command sets contribute to efficient vehicle behaviour control, adaptive energy management, and consistent performance under varying operational scenarios.
As used herein, the terms “error detection codes” and “error checking codes” are used interchangeably and refer to encoded data sequences appended to transmit the control signal to identify errors during communication. Specifically, the error detection codes are incorporated by the control module to encode the control signal sent to an electronic control unit within the electric vehicle, ensuring data integrity and secure transmission. The codes comprise, but are not limited to, Cyclic Redundancy Checks (CRC), parity bits, and checksums that detect alterations or corruption in the transmitted data. Further, the error detection codes facilitate real-time identification of communication faults, allowing immediate corrective actions to maintain reliable command execution. Furthermore, the use of error detection codes enhances communication robustness, minimizes errors, and supports safe and accurate modulation of the operational parameters of the electric vehicles.
As used herein, the terms “electronic control unit” and “ECU” are used interchangeably and refer to an embedded computing device configured to receive, process, and execute the control signal for managing various operational functions of the electric vehicle. Specifically, the electronic control unit receives encoded control signals from the control module and adjusts the operational parameters such as torque commands, regenerative braking intensity, and maximum allowable vehicle speed based on real-time energy management requirements. The types of electronic control units, but are not limited to, powertrain control units, battery management controllers, and traction control modules. Further, the electronic control unit operates by interpreting command sets encoded with error detection codes to ensure accurate and safe implementation of vehicle commands. Furthermore, the electronic control unit enables precise, adaptive control over vehicle dynamics and energy usage, thereby enhancing driving efficiency, safety, and battery longevity under varying operating conditions.
As used herein, the terms “torque command” and “torque control signal” are used interchangeably and refer to an encoded instruction configured to regulate the torque output of the electric vehicle’s motor or drivetrain components. Specifically, the torque command is generated by the control module based on inputs from the positioning module, battery monitoring module, and communication module to optimize vehicle performance and energy efficiency. The torque commands include directives to increase, decrease, or limit torque delivery during various driving conditions such as acceleration, cruising, or regenerative braking. Further, the torque command is transmitted to the electronic control unit responsible for motor control, which decode and execute the command to adjust torque accordingly. Furthermore, the torque command enables adaptive vehicle response, enhances drivability, and contributes to energy-saving strategies by precisely managing power output relative to real-time battery state and driving requirements.
As used herein, the terms “closed-loop feedback,” “feedback control,” “closed-loop control”, and “real-time feedback mechanism” are used interchangeably and refer to a control process that continuously monitors output parameters and adjusts the control signals. Specifically, the closed-loop feedback operates within the control module to dynamically modulate vehicle operational parameters based on real-time data from the positioning module, battery monitoring module, and communication module. The types of feedback include but are not limited to proportional, integral, and derivative control schemes that ensure precise regulation of operational parameters, such as, but not limited to, torque, speed, and braking intensity. Further, the closed-loop feedback mechanism processes sensor inputs and responses to correct deviations from target performance, enabling adaptive control under varying driving conditions. Furthermore, the implementation of closed-loop feedback enhances system stability, improves energy efficiency, and minimizes the risk of vehicle immobilization by continuously optimizing control signals.
As used herein, the terms “real-time alerts”, “instant notifications,” and “live warnings” are used interchangeably and refer to timely messages or signals generated to inform a driver about the status or critical conditions of the electric vehicle. Specifically, the real-time alerts are provided by the display interface to communicate information related to battery charge status, range estimations, energy-saving recommendations, and potential operational risks based on continuous monitoring of vehicle parameters. The types of real-time alerts include, but not limited to, visual, auditory, and haptic feedback designed to ensure immediate driver awareness. Further, the real-time alerts are generated by the control module based on processing data from the positioning module, communication module, and battery monitoring module to provide actionable insights during driving. Furthermore, the real-time alerts enhance driver decision-making, improve safety, reduce range anxiety, and promote efficient vehicle operation by enabling proactive responses to dynamic driving conditions.
As used herein, the terms “energy-saving modes”, “power-saving modes,” “eco-driving modes”, and “energy optimization settings” are used interchangeably and refer to predefined or adaptive operational states configured to reduce energy consumption and enhance the efficiency of the electric vehicle. Specifically, the energy-saving modes are activated by the control module based on inputs from the battery monitoring module, positioning module, and communication module to optimize operational parameters, such as, but not limited to, torque, speed, and regenerative braking. The types of energy-saving modes include, but are not limited to, reduced power output, limited maximum speed, and enhanced regenerative braking. Furthermore, the energy-saving modes adjust support vehicle behavior dynamically according to real-time data on battery charge, distance to charging stations, and predicted travel range. Furthermore, the energy-saving modes prolong battery life, extend driving range, reduce energy costs, and support sustainable vehicle operation under varying driving conditions.
In accordance with a first aspect of the present disclosure, there is provided a system for controlling at least one operational parameter of an electric vehicle, the system comprising:
- a positioning module configured to determine a current location of the electric vehicle;
- a battery monitoring configured to detect a current SoC of the electric vehicle;
- a communication module communicably coupled to at least one battery station via a communication network, and configured to receive charge availability data from the at least one battery station;
- a control module communicably coupled to the positioning module, the battery monitoring module, and the communication module; and
- a display interface operatively connected to the control module,
wherein the control module is configured to determine an estimated travel range based on the current SoC, and a predicted distance to the battery station based on the current location and the charge availability data, and generates at least one control signal for modulating the at least one operational parameter of the electric vehicle corresponding to the predicted distance exceeding the estimated travel range.

Referring to figure 1, in accordance with an embodiment, there is described a system 100 for controlling at least one operational parameter of an electric vehicle 102. The system 100 comprises a positioning module 104 configured to determine a current location of the electric vehicle 102; a battery monitoring module 106 communicably coupled to the positioning module 104 and configured to detect a current SoC of the electric vehicle 102 and a communication module 108 communicably coupled to at least one battery station 110 via a communication network 112, and configured to receive charge availability data from the at least one battery station 110. The system 100 further comprises a control module 114 communicably coupled to the positioning module 104, the battery monitoring module 106, and the communication module 108, a display interface 116 and an electronic control unit 118 operatively connected to the control module 114. The control module is configured to determine an estimated travel range based on the current SoC, and a predicted distance to the battery station based on the current location and the charge availability data, and generate at least one control signal for modulating the at least one operational parameter of the electric vehicle corresponding to the predicted distance exceeding the estimated travel range.
The system 100 receives real-time input data from a plurality of modules integrated within the electric vehicle 102 and the external charging infrastructure. Specifically, the positioning module 104 continuously determines the current geographic location of the electric vehicle 102 using onboard GPS and satellite-based positioning data. Simultaneously, the battery monitoring module 106 acquires the current SoC of the electric vehicle 102 and transmits to the control module 114. Furthermore, the communication module 108 establishes a network connection with at least one battery station 110 via a communication network 112 and receives live charge availability data, station location, charging capacity, queue status, and so on from the battery station 110. Further, the control module 114 processes the current SoC along with predictive battery degradation models and calculates the estimated travel range of the electric vehicle 102. Additionally, the control module 114 computes the predicted distance to the available battery station 110, based on the current location from the positioning module 104 and station location from the communication module 108. The control module 114 further compares the predicted distance to the estimated travel range to identify whether the electric vehicle 102 has sufficient charge to reach the battery station 110. When the predicted distance exceeds the estimated range, the control module 114 generates at least one control signal to modulate an operational parameter of the electric vehicle 102. The control module 114 transmits the encoded control signals to the Electronic Control Units (ECUs) 118. Further, the electronic control unit 118 of the electric vehicle 102 adjusts commands such as, but not limited to, torque commands, control regenerative braking intensity, and limit maximum allowable vehicle speed based on the comparison between predicted distance and estimated travel range. Moreover, the display interface 116 is updated with relevant visual alerts and guidance, comprising the current range limit, proximity to the nearest viable battery station, and recommended driving behaviours. The system 100 operates continuously in real time, ensuring that operational adjustments are made dynamically and based on current vehicle state and infrastructure data. Specifically, the control module 114 ensures that energy-critical conditions are addressed proactively by enforcing operational limits before energy depletion occurs. Advantageously, the system 100 reduces the risk of range failure by dynamically assessing the viability of reaching the next available battery station. Furthermore, the system 100 ensures energy-aware decision-making without human intervention, thereby eliminating uncertainty during low battery conditions. Beneficially, the automated control of high-energy-consuming components enhances overall vehicle efficiency and battery life.
In an embodiment, the battery monitoring module 106 is configured to integrate the SoC data with a predictive battery degradation model and transfer the updated SoC to the control module 114. The battery monitoring module 106 receives continuous input from sensors measuring parameters such as, but not limited to, voltage, current, temperature, and charge/discharge activity of the battery cells. Additionally, the battery monitoring module 106 organizes and transmits the receive information to the predictive battery degradation model for supplementary processing. The predictive battery degradation model is embedded within the battery monitoring module 106, which processes the input data using degradation curves developed from electrochemical aging. Moreover, the predictive battery degradation model calculates capacity reduction and internal resistance increase using fixed lookup tables and time-weighted stress indicators. Additionally, the predictive battery degradation model transfers the updated SoC to the control module 114. The control module 114 utilizes the updated SoC to manage power delivery and energy distribution across the system. Furthermore, the control module 114 applies the adjusted capacity information to define charge thresholds, discharge windows, and load balancing strategies. Moreover, the control module 114 maintains high energy efficiency, real-time responsiveness, and system safety under all load profiles. The predictive battery degradation model within the battery monitoring module 106 enables accurate and health-aware estimation of usable capacity under real-world conditions. Specifically, the transfer of the updated SoC to the control module 114 ensures precise energy management based on the actual battery condition. Advantageously, the system 100 eliminates capacity overestimation risks and promotes operational consistency across varying scenarios. Additionally, the system 100 achieves extended battery life, decreased failure likelihood, and stable long-term performance through accurate charge regulation and degradation awareness.
In an embodiment, the communication module 108 is configured to aggregate the charge availability data from the at least one battery station 110 and apply a filtering algorithm to prioritize the charge stations 110. The communication module 108 collects charge availability data from the battery station 110, comprising parameters such as, but not limited to, remaining capacity, operational status, and current load. Furthermore, the communication module 108 aggregates the charge availability data to create a comprehensive overview of all battery stations 110 within the network. Moreover, the communication module 108 incorporates additional information such as geographic location and historical reliability metrics. Additionally, the communication module 108 formats the aggregated data to prepare for application of the filtering algorithm. The filtering algorithm within the communication module 108 processes the aggregated data by applying predefined prioritization criteria. Furthermore, the filtering algorithm assigns weighted scores to each battery station 110 based on factors including, but not limited to, charge availability, proximity to the user, response time, and station reliability. The filtering algorithm filters out stations that do not meet operational thresholds and ranks the remaining battery stations 110 accordingly. Furthermore, the filtering algorithm generates a prioritized list of battery stations 110 optimized for efficient charging allocation. The control module 114 receives the prioritized list of battery stations 110 from the communication module 108. Moreover, the control module 114 updates commands dynamically as new data arrives from the communication module 108. The integration of the filtering algorithm in the communication module 108 ensures that charging stations 110 are selected and prioritized based on real-time, comprehensive data analysis. Specifically, the prioritization explicitly improves system efficiency by reducing wait times and balancing station loads. Advantageously, the system 100 enhances charging throughput and responsiveness across the network. Further, results in improved resource utilization, reduced operational bottlenecks, and elevated user satisfaction.
In an embodiment, the control module 114 is configured to encode at least one control signal with command sets for operational parameters modulation, via a set of error detection codes. The control module 114 generates control signals to regulate operational parameters that affect battery consumption, such as, but not limited to, voltage levels, current flow, HVAC usage, infotainment systems, lighting, and auxiliary electrical components. Furthermore, the control module 114 structures command sets that define strategies for the modulation of operational parameters. Moreover, the control module 114 embeds error detection codes into the control signals to preserve integrity during communication. Additionally, the control module 114 transmits the encoded control signals to the electronic control unit 118. The control module 114 applies error detection techniques such as Cyclic Redundancy Check (CRC) and parity bits to identify transmission faults. Specifically, the error detection codes enable the electronic control unit 118 to verify the accuracy of the received control signals associated with battery-powered components. Moreover, the control module 114 filters out corrupted signals, ensuring only validated commands reach execution. The error detection codes within the control module 114 functionally safeguard modulation accuracy across all energy-consuming components. Additionally, the control module 114 facilitates early detection of faults, allowing timely corrective action to maintain energy efficiency. The encoding control signals with error detection codes by the control module 114 ensures reliable modulation of battery consumption parameters. Specifically, the controlled operation of HVAC and auxiliary arrangements contributes to optimized energy distribution and extended driving range. Advantageously, the protection against communication errors minimizes power waste and prevents performance degradation. Additionally, the system 100 enhances operational stability, supports energy-efficient component usage, and improves battery longevity through robust and accurate control signaling.
In an embodiment, the control module 114 is configured to transmit the encoded control signals to a plurality of electronic control units 118 of the electric vehicle 102. The control module 114 prepares encoded control signals with command sets that modulate operational parameters of the electric vehicle 102. Further, the control module 114 manages signal transmission protocols, ensuring data integrity and synchronization across the electronic control units 118. The transmission process is employed by the control module 114 through standardized communication interfaces, such as, but not limited to, CAN bus or Ethernet, to ensure compatibility with multiple electronic control units 118. Furthermore, the control module 114 implements error checking and acknowledgement protocols to verify the successful delivery of the encoded control signals. Specifically, the control module 114 sequences and routes control signals to the respective electronic control unit 118 based on the functional roles of the electronic control unit 118. The reliable transmission of encoded control signals by the control module 114 enables coordinated control of multiple subsystems within the electric vehicle 102. Advantageously, the control module 114 supports rapid adaptation to dynamic driving conditions through real-time command updates. The control module 114 prevents communication errors and data loss, ensuring stable vehicle performance. Furthermore, the explicit multi-unit communication reduces latency and improves response times within the electric vehicle 102.
In an embodiment, the electronic control unit 118 of the electric vehicle 102 is configured to adjust torque commands, control regenerative braking intensity, and limit maximum allowable vehicle speed based on the comparison between predicted distance and estimated travel range. The electronic control unit 118 receives input data that comprises the predicted travel distance and the current estimated travel range of the electric vehicle 102. Specifically, the electronic control unit 118 performs a comparison between the predicted distance and the estimated range to assess remaining energy sufficiency. Further, the electronic control unit 118 dynamically modifies torque commands to optimize power usage depending on the comparison. Additionally, the electronic control unit 118 adjusts regenerative braking intensity to maximize energy recovery when the travel range approaches the predicted distance. The electronic control unit 118 also imposes a speed limit on the electric vehicle 102 to conserve battery energy when the estimated range is near or below the predicted travel distance. Furthermore, the electronic control unit 118 continuously monitors vehicle speed and adjusts the maximum allowable speed accordingly. The electronic control unit 118 integrates data from various sensors and energy models to enforce the constraints effectively. Moreover, the electronic control unit 118 ensures seamless transitions between torque adjustment, regenerative braking, and speed limitation to maintain drivability. The adaptive control executed by the electronic control unit 118 explicitly optimizes energy consumption and extends the available travel range. Advantageously, the dynamic modulation of torque and braking enhances battery efficiency while maintaining safety and performance. Moreover, the speed limitation based on range assessment prevents battery depletion and reduces the risk of unexpected vehicle stops. Furthermore, the coordinated adjustments improve the reliability of the electric vehicle 102 during long-distance travel. The configuration of the electronic control unit 118 to adjust driving parameters increases driving efficiency and battery utilization. Furthermore, the explicit range-based control strategy generally improves vehicle safety and energy management. Moreover, the system ensures consistent and predictable vehicle operation under variable travel conditions. Additionally, results in extended travel distances, improved battery life, and enhanced driver confidence.
In an embodiment, the control module 114 is configured to execute a closed-loop feedback control based on the at least one parameter modulation and dynamically adjust the control signals. The control module 114 processes at least one parameter modulation to generate error signals, which are fed back into the algorithm. Moreover, the control module 114 dynamically updates the control signals in real-time to maintain system 100 stability and optimize performance according to the feedback. Additionally, the closed-loop feedback mechanism ensures precise regulation of system 100 variables under varying operational conditions. The closed-loop feedback control implemented by the control module 114 significantly improves system accuracy and responsiveness. Specifically, the dynamic adjustment of control signals reduces steady-state error and enhances disturbance rejection. Advantageously, the system 100 maintains optimal functionality without manual intervention, leading to increased reliability. Additionally, the approach results in enhanced operational efficiency and robustness of the electric vehicle 102 in complex environments.
In an embodiment, the display interface 116 is configured to receive the dynamically adjusted control signals of the at least one operational parameter corresponding to the predicted distance exceeding the estimated travel range.. The display interface 116 receives real-time modulating data related to the operational parameters when the predicted distance surpasses the estimated travel range. Furthermore, the display interface 116 processes and visually presents the received information to the user, enabling immediate awareness of critical conditions of the electric vehicle 102. Moreover, the display interface 116 is updated dynamically to reflect changes in the modulating data, ensuring continuous feedback regarding the travel range status. The display interface 116 enhances user decision-making by explicitly providing crucial operational insights related to travel range limitations. Specifically, the immediate visualization of modulating data prevents unexpected behaviour and promotes proactive adjustments of the electric vehicle 102. Advantageously, the configuration improves user confidence during operation of the electric vehicle 102.
In an embodiment, the display interface 116 is configured to provide real-time alerts and energy-saving modes recommendations based on the received dynamically adjusted control signals of at least one operational parameter. The display interface 116 continuously receives modulating data corresponding to the operational parameters and analyzes the data to detect critical thresholds. Furthermore, the display interface 116 generates real-time alerts to inform the user about the electric vehicle 102 condition requiring immediate attention. Moreover, the display interface 116 evaluates the modulating data to recommend energy-saving modes that optimize overall system 100 efficiency. Additionally, the display interface 116 dynamically updates alerts and recommendations to reflect ongoing changes in the performance of the electrical vehicle 102. The display interface 116 enhances the reliability of the system 100 by enabling timely user responses through real-time alerts. Specifically, the energy-saving mode recommendations contribute to reduced power consumption. Advantageously, the configuration improves user awareness and supports sustainable system management. Further, the display interface 116 facilitates efficient and informed control of the electric vehicle 102.
In accordance with a second aspect, there is described a method of controlling at least one operational parameter of an electric vehicle, the method comprising:
- determining a current location of the electric vehicle via a positioning module;
- detecting a current state of charge of the electric vehicle via a battery monitoring module;
- receiving charge availability data from at least one battery station via a communication module;
- determining an estimated travel range based on the current SoC, and a predicted distance to the battery station based on the current location and the charge availability data, via a control module; and
- modulating at least one operational parameter of the electric vehicle via the control module.
Figure 2 describes a method 200 for controlling at least one operational parameter of an electric vehicle 102. The method 200 starts at a step 202. At the step 202, the method 200 comprises determining a current location of the electric vehicle 102 via a positioning module 104. At a step 204, the method comprises detecting a current state of charge of the electric vehicle 102 via a battery monitoring module 112. At a step 206, the method comprises receiving charge availability data from at least one battery station 110 via a communication module 108. At a step 208, the method comprises determining an estimated travel range based on the current SoC, and a predicted distance to the battery station 110 based on the current location and the charge availability data, via a control module 114. At a step 210, the method comprises modulating at least one operational parameter of the electric vehicle 102 via the control module 114.
In an embodiment, the method 200 comprises determining an estimated travel range based on the current SoC of the electric vehicle 102 and a predicted distance to the battery station 110 based on the current location and the charge availability data at the battery station 110.
In an embodiment, the method 200 comprises generating at least one control signal based on the comparison between the predicted distance and the estimated travel range.
In an embodiment, the method 200 comprises transmitting the encoded control signal to the electronic control units 118 configured to modulate at least one operational parameter of the electric vehicle 102.
In an embodiment, the method 200 comprises displaying real-time notifications and alerts via the display interface 116 when the predicted distance exceeds the estimated range.
In an embodiment, a method 200 for controlling at least one operational parameter of an electric vehicle 102. The method 200 comprises determining a current location of the electric vehicle 102 via a positioning module 104. The method comprises identifying at least one battery station 110 via a communication network 112. Further, the method comprises receiving charge availability data from at least one battery station 110 via a communication module 108. The method comprises receiving the current SoC of the electric vehicle 102 via a battery monitoring module 106. The method 200 comprises determining an estimated travel range based on the current SoC of the electric vehicle 102 and a predicted distance to the battery station 110 based on the current location and the charge availability data at the battery station 110. The method 200 comprises generating at least one control signal based on the comparison between the predicted distance and the estimated travel range. The method comprises modulating one or more operational parameters of the electric vehicle 102 via a control module 114. The method 200 comprises transmitting the encoded control signal to the electronic control units 118 configured to modulate at least one operational parameter of the electric vehicle 102. The method 200 comprises executing the modulation of one or more operational parameters of the electric vehicle 102 via the electronic control units 118. The method 200 comprises displaying real-time notifications and alerts via the display interface 116 when the predicted distance exceeds the estimated range. The method repeats continuously, based on changes in the current SoC of the electric vehicle 102, location of the electric vehicle 102, and the availability of battery stations 110.
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 controlling at least one operational parameter of an electric vehicle (102), the system (100) comprising:
- a positioning module (104) configured to determine a current location of the electric vehicle (102);
- a battery monitoring module (106) configured to detect a current State of Charge (SoC) of the electric vehicle (102);
- a communication module (108) communicably coupled to at least one battery station (110) via a communication network (112), and configured to receive charge availability data from the at least one battery station (110);
- a control module (114) communicably coupled to the positioning module (104), the battery monitoring module (106), and the communication module (108); and
- a display interface (116) operatively connected to the control module (114).
wherein, the control module (114) is configured to determine an estimated travel range based on the current SoC, and a predicted distance to the battery station (110) based on the current location and the charge availability data, and generates at least one control signal for modulating the at least one operational parameter of the electric vehicle (102) corresponding to the predicted distance exceeding the estimated travel range.

2. The system as claimed in claim 1, wherein the battery monitoring module (106) is configured to integrate the SoC data with a predictive battery degradation model and transfer the updated SoC to the control module (114).

3. The system as claimed in claim 1, wherein the communication module (108) is configured to aggregate the charge availability data from the at least one battery station (110) and apply a filtering algorithm to prioritize the charge stations (110).

4. The system as claimed in claim 1, wherein the control module (114) is configured to encode the at least one control signal with command sets for operational parameters modulation, via a set of error detection codes.

5. The system as claimed in claim 1, wherein the control module (114) is configured to transmit the encoded control signals to a plurality of electronic control units (118) of the electric vehicle (102).

6. The system as claimed in claim 5, wherein the electronic control unit (118) of the electric vehicle (102) is configured to adjust torque commands, control regenerative braking intensity, and limit maximum allowable vehicle speed based on the comparison between predicted distance and estimated travel range.

7. The system as claimed in claim 1, wherein the control module (114) is configured to execute a closed-loop feedback control based on the at least one parameter modulation and dynamically adjust the control signals.

8. The system as claimed in claim 1, wherein the display interface (116) is configured to receive the dynamically adjusted control signals of the at least one operational parameter corresponding to the predicted distance exceeding the estimated travel range.

9. The system as claimed in claim 1, wherein the display interface is configured to provide real-time alerts and energy-saving modes recommendations based on the received dynamically adjusted control signals of the at least one operational parameter.

10. A method (200) of controlling at least one operational parameter of an electric vehicle (102), the method comprising:
- determining current location of the electric vehicle (102) via a positioning module (104);
- detecting a current state of charge of the electric vehicle (102) via a battery monitoring module (112);
- receiving charge availability data from at least one battery station (110) via a communication module (108);
- determining an estimated travel range based on the current SoC, and a predicted distance to the battery station (110) based on the current location and the charge availability data, via a control module (114); and
- modulating at least one operational parameter of the electric vehicle (102) via the control module (114).