Description:FIELD OF THE INVENTION

[0001] The present invention relates to a system for managing energy in EV charging networks that efficiently manages energy distribution across EV charging stations based on demand and grid conditions, optimizing infrastructure efficiency. Furthermore, the system accurately forecasts renewable energy availability (solar/wind) to maximize its use for EV charging, enabling more sustainable and efficient EV charging.

BACKGROUND OF THE INVENTION

[0002] The smart energy management system for EV charging networks optimizes power flow, ensuring grid stability and cost-effectiveness. It integrates renewable energy sources, battery storage, and dynamic pricing to manage demand fluctuations. Key functionalities include real-time monitoring, predictive analytics for load forecasting, and intelligent scheduling of charging sessions. This system prioritizes efficient energy distribution, minimizes peak demand charges, and enhances the overall reliability and sustainability of EV charging infrastructure, benefiting both users and grid operators.

[0003] Traditional EV charging management typically relies on uncoordinated "first-come, first-served" approaches or static grid capacity limits, leading to significant limitations. This often results in grid strain and instability due to unpredictable demand surges, particularly during peak hours, which can cause voltage fluctuations and even blackouts. Furthermore, charging during peak times incurs higher electricity costs for both operators and consumers. These methods also struggle to efficiently integrate intermittent renewable energy sources, as they lack the flexibility to match variable generation with fluctuating EV charging needs. As EV adoption rapidly increases, these reactive strategies prove unsustainable, necessitating costly infrastructure upgrades and failing to optimize charging schedules based on real-time grid conditions, energy prices, or vehicle departure times.

[0004] US2025103926A1 discloses a disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.

[0005] WO2020208655A1 discloses a technique for charging of electric vehicles using renewable energy are described. In an example, information regarding a current location of an electric vehicle is obtained. Based on the current location of the electric vehicle, at least one charging station within a predefined area from the current location of the electric vehicle is identified and availability of renewable energy at the at least one charging station to charge the electric is determined. A notification regarding availability of renewable energy at the at least one charging station is provided to a user device of a driver of the electric vehicle.

[0006] Conventionally, many systems are existing EV charging management systems often lack real-time demand-based energy distribution, accurate renewable energy forecasting, and robust communication for load balancing, leading to inefficiencies and grid instability.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system offers efficient energy distribution based on real-time demand and grid conditions. The system accurately forecasts renewable energy availability for optimized usage, ensuring sustainable EV charging. A reliable communication system prevents overloads, guaranteeing stable power flow.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of efficiently manage the distribution of energy across multiple EV charging stations based on current demand and grid conditions, thus enhancing the overall efficiency of the EV charging infrastructure.

[0010] Another object of the present invention is to develop a system that is capable of accurately forecast the availability of renewable energy sources such as solar and wind to optimize their use for EV charging, therefore enabling more efficient and sustainable electric vehicle (EV) charging infrastructure.

[0011] Yet another object of the present invention is to develop a system that is capable of provide a reliable communication system that coordinates energy sharing and distribution among charging stations and EVs to prevent overloads and bottlenecks, thus ensuring efficient and stable power flow within the EV charging network.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a system for managing energy in EV charging networks that accurately forecasts renewable energy availability (solar/wind) to optimize its use for EV charging, leading to more efficient and sustainable EV charging infrastructure. Additionally, the system is providing a reliable communication system to coordinate energy sharing among charging stations and EVs, preventing overloads and bottlenecks for stable power flow.

[0014] According to an embodiment of the present invention, a system for managing energy in EV charging networks, comprising a module associated with the system that collects real-time data on traffic, an AI (artificial intelligence) engine associated with the system that decides how to share energy based on traffic, a forecasting module associated with the system that predicts how much solar and wind energy is available, a Vehicle-to-Grid (V2G) control unit enabling bidirectional energy transfer between EVs and the grid, a communication module associated with the system facilitates multi-node coordination and establishes even distribution of power across multiple charging stations to prevent bottlenecks and overloads.

[0015] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates an isometric view of a system for managing energy in EV charging networks.

DETAILED DESCRIPTION OF THE INVENTION

[0017] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0018] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0019] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0020] The present invention relates to a system for managing energy in EV charging networks that efficiently manages energy distribution across EV charging stations based on demand and grid conditions, enhancing infrastructure efficiency. The system also provides a reliable communication system to coordinate energy sharing among stations and EVs, preventing overloads and bottlenecks for stable power flow within the network.

[0021] Referring to Figure 1, the flow chart of a system for managing energy in EV charging networks is illustrated. The The proposed system is designed to coordinate the distribution of energy across EV charging stations dynamically. It comprises several interconnected modules, each serving distinct yet synergistic functions: data collection, decision-making via artificial intelligence, renewable energy forecasting, bidirectional energy transfer control, and multi-node communication for coordination. Together, these components facilitate a resilient, efficient, and sustainable EV charging network.

[0022] At the core of the system lies the data collection module, which continuously acquires real-time information critical for informed decision-making. Situated within the infrastructure, this module gathers data on traffic patterns, energy demands at various charging stations, and the availability of charging stations themselves. Traffic data, sourced from sensors, cameras, or vehicle tracking systems, informs the system about vehicle flow and congestion levels across different nodes. Energy needs are monitored through meters and sensors embedded within each charging station, providing granular insights into current load, peak times, and station utilization rates. Additionally, the module tracks the status of the charging stations, including operational readiness, available chargers, and maintenance schedules. This real-time data forms a comprehensive picture of the network's operational state, enabling the system to respond promptly to fluctuations in demand, prevent bottlenecks and optimize resource allocation. By integrating various data streams, the system maintains situational awareness, which is essential for adaptive energy management.

[0023] Complementing the data collection module is an advanced Artificial Intelligence (AI) engine responsible for strategic decision-making. Employing machine learning algorithms, predictive analytics, and optimization techniques, the AI engine analyses incoming data to determine the most efficient way to distribute energy across the network. Its primary function is to decide how to share and allocate available energy resources based on current traffic conditions, charging needs, and the stability of the electrical grid. The AI engine considers multiple factors, such as the urgency of charging demands, the capacity of individual stations, grid load levels, and the overall stability of the power infrastructure. For instance, during peak traffic hours with high charging demand, the AI prioritize energy distribution to prevent overloads and ensure timely vehicle charging. Conversely, during periods of low demand, it can optimize energy flow to maintain grid stability and prepare for future surges.

[0024] Given the increasing integration of renewable energy sources like solar and wind into power grids, the system incorporates a forecasting module dedicated to predicting renewable energy availability. Using weather data, meteorological models, and historical patterns, this module estimates the expected generation of solar and wind power over various time horizons. Accurate forecasting allows the system to plan energy distribution proactively, leveraging renewable sources when they are most abundant. For example, if the forecast predicts high solar generation during midday, the system prioritizes charging sessions that utilize this clean energy, reducing reliance on grid-supplied electricity. Conversely, during periods of low renewable output, the system can adjust its strategies to conserve stored energy or draw from the grid in a controlled manner.

[0025] This predictive capability enhances the sustainability of the EV charging network by maximizing renewable energy consumption, reducing greenhouse gas emissions, and lowering operational costs. It also enables better integration of intermittent renewable sources into the energy management paradigm, ensuring a balanced and resilient power supply.

[0026] A pivotal feature of the system is the Vehicle-to-Grid (V2G) control unit, which facilitates bidirectional energy flow between EVs and the electrical grid. Unlike traditional charging systems that solely draw energy from the grid, V2G technology allows EV batteries to discharge stored energy back into the grid when needed, effectively turning EVs into mobile energy storage units. The V2G control unit coordinates this bi-directional transfer based on real-time demand signals, grid stability requirements, and user preferences. Conversely, during low demand periods or when renewable energy generation is high, EVs are charged or discharge energy to balance supply and demand efficiently.

[0027] This dynamic interaction not only enhances grid reliability but also offers economic incentives to EV owners, who receive compensation for providing grid services. The V2G system thus plays a crucial role in creating a flexible, distributed energy resource network, promoting sustainable and economically beneficial energy exchange.

[0028] Ensuring seamless operation across multiple charging stations and network nodes requires a robust communication infrastructure. The communication module acts as the nervous system of the entire framework, enabling real-time data exchange, coordination, and control signals among various components. This module employs secure, high-speed communication protocols to facilitate multi-node synchronization, load balancing, and even distribution of power. It orchestrates the coordination among stations to prevent bottlenecks and overloads, especially during high-demand periods. For example, if one station approaches overload, the communication system can reroute charging requests to neighbouring stations or adjust the power flow dynamically. Moreover, the communication module supports remote monitoring, diagnostics, and management, allowing operators to oversee the network's health and performance proactively. It also ensures that the AI engine and V2G control units operate cohesively, sharing insights and executing coordinated strategies for optimal energy distribution.

[0029] The present invention work in the best manner, where thev presents holistic, integrated framework for EV charging, coordinating energy distribution dynamically. The data collection module continuously gathers real-time traffic patterns, energy demands at charging stations, and station availability, forming comprehensive picture of the network's operational state. The Artificial Intelligence (AI) engine then analyzes this data using machine learning and predictive analytics to strategically distribute energy based on traffic, charging needs, and grid stability, preventing overloads and ensuring timely charging. Furthermore, the forecasting module predicts renewable energy availability (solar and wind) using weather data, enabling the system to prioritize clean energy use and reduce reliance on grid-supplied electricity. The Vehicle-to-Grid (V2G) control unit facilitates bidirectional energy flow, allowing EVs to discharge stored energy back into the grid, enhancing grid reliability and offering economic incentives. Finally, the communication module acts as the nervous system, employing secure, high-speed protocols for real-time data exchange, coordination, and control signals, ensuring seamless operation, preventing bottlenecks, and supporting remote monitoring across multiple charging stations and network nodes, all contributing to resilient, efficient, and sustainable EV charging network.

[0030] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A system for managing energy in EV charging networks, comprising:

i) a module associated with the system that collects real-time data on traffic, energy needs, and available charging stations;

ii) an AI (artificial intelligence) engine associated with the system that decides how to share energy based on traffic, charging needs, and grid stability;

iii) a forecasting module associated with the system that predicts how much solar and wind energy is available;

iv) a Vehicle-to-Grid (V2G) control unit enabling bidirectional energy transfer between EVs and the grid, thereby facilitating a bi-direction energy transfer between EVs and Grid, based on demand; and

v) a communication module associated with the system facilitates multi-node coordination and establishes even distribution of power across multiple charging stations to prevent bottlenecks and overloads.

2) The system as claimed in claim 1, wherein the AI engine uses machine learning protocols, trained on past charging data.

3) The system as claimed in claim 1, wherein the system uses renewable energy first to reduce pollution.

4) The system as claimed in claim 1, wherein the communication system allows EVs to share energy with each other.

5) The system as claimed in claim 1, wherein the AI uses pricing models to offer cheaper charging options.