Description:FIELD OF THE INVENTION
This invention relates to adaptive ai-based bidirectional wireless EV charging and grid support system.
BACKGROUND OF THE INVENTION
The increasing penetration of electric vehicles (EVs) into urban and rural areas has placed significant demands on the power grid, especially during peak charging hours. Traditional plug-in charging systems lack flexibility and often do not support grid-responsive behavior. Wireless charging systems, though promising, have struggled with efficiency issues due to coil misalignment, switching losses, and inflexible control strategies. Moreover, the lack of real-time intelligence in managing bidirectional power flow between the grid and EVs limits their effectiveness in modern smart grids. These gaps highlighted the need for an adaptive, intelligent, and efficient wireless bidirectional EV charging system with grid support capabilities.
Electric vehicles (EVs) are increasingly prevalent, but current wireless charging systems remain limited by unidirectional power flow, low adaptability, and poor integration with smart grids. These systems lack real-time intelligence to manage charging or discharging based on grid needs, user behavior, and renewable energy variability. Additionally, inefficiencies in energy transfer and weak security mechanisms hinder their effectiveness. The proposed Adaptive AI-Based Bidirectional Wireless EV Charging and Grid Support System introduces an AI-driven controller for dynamic energy management, reconfigurable compensation networks for efficient bidirectional wireless transfer, and secure IoT protocols for transaction validation—enabling EVs to function as mobile, intelligent grid-supporting energy assets.
OBJECTS OF INVENTION:
The objective of the proposed patent is to develop an Adaptive AI-Based Bidirectional Wireless EV Charging and Grid Support System that seamlessly integrates electric vehicles into the smart grid ecosystem. The system employs advanced AI algorithms to predict user behavior, optimize coil alignment, manage power flow intelligently, and ensure stable bidirectional energy transfer. It dynamically responds to grid conditions, minimizes switching losses, and maintains voltage/current stability. This system transforms EVs into mobile energy assets, providing services such as peak shaving, voltage regulation, and load balancing, thereby enhancing grid resilience and user convenience while reducing infrastructure and operational costs.
PRIOR ART
US20250001887: A wireless charging system and method for an electric vehicle, includes a gantry-like overhead structure for maneuvering in vertical and horizontal directions a power transmitting coil mounted on a charging line (e.g., rod or cable) suspended from the gantry-like overhead structure to a power receiving coil mounted to a surface (e.g., hood) of an electric vehicle (EV) for wireless charging of the EV. The gantry-like overhead system can move along a track to charge multiple vehicles (e.g., fleet vehicles) parked side-by-side in a parking structure. Movement of the gantry-like system can be automatic based on input from at least one sensor to a controller included in the wireless charging system.
US12351059: Example embodiments are directed to a building management system tailored for structures equipped with a network of electric vehicle (EV) chargers. The system integrates various components, including a host interface, HVAC systems with heat pumps and variable frequency drives (VFDs), a network of EV chargers featuring bi-directional charging capabilities, and a building management platform. Load-balancing software within panel metering devices ensures efficient energy distribution to the EV charging units. An artificial intelligence (AI) algorithm optimizes charging schedules based on historical usage data, minimizing energy consumption during peak hours. The system dynamically adjusts power allocation to EV chargers based on HVAC system demand, promoting efficient energy utilization. Real-time monitoring and analysis capabilities enhance energy management, with alerts notifying building managers of potential anomalies.
The primary objective of the present invention is to develop an adaptive AI-based bidirectional wireless charging system for electric vehicles that not only supports charging from the grid to the vehicle (G2V) but also enables the vehicle-to-grid (V2G) power transfer, thereby allowing EVs to act as mobile energy storage assets.
Another objective of the invention is to ensure efficient and reliable wireless energy transfer by utilizing resonant inductive coupling with adaptive coil alignment. This minimizes energy losses caused by misalignment and enhances the practicality of wireless charging for dynamic EV applications.
The invention further aims to integrate artificial intelligence-driven predictive control that optimizes charging and discharging schedules. By considering user driving behavior, energy demand patterns, and grid conditions, the system adapts in real time to improve efficiency, reliability, and user convenience.
A key objective is to enable real-time interaction with the power grid, so that the system can actively contribute to demand-response strategies, peak load reduction, frequency regulation, and voltage stabilization. This enhances the resilience of smart grids and reduces the burden on existing infrastructure.
Finally, the invention seeks to provide a cost-effective, scalable, and sustainable solution by employing edge AI for local decision-making and modular circuit topologies for ease of implementation. This ensures that the system can be deployed across residential, commercial, and public charging networks with minimal additional cost while delivering maximum benefits.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention proposes an Adaptive AI-Based Bidirectional Wireless EV Charging and Grid Support System that enables efficient two-way power transfer between electric vehicles (EVs) and the power grid using resonant inductive coupling. Unlike conventional systems that are limited by misalignment losses, unidirectional flow, and lack of intelligence, the proposed system integrates AI-driven predictive control, soft-switching converters, and secure IoT communication to optimize performance.
The system employs machine learning algorithms to forecast user behavior, driving patterns, and grid conditions, thereby dynamically scheduling charging and discharging. An adaptive resonance-based coil design ensures efficient wireless energy transfer even under misalignment. The EV acts as a mobile energy storage asset, supporting grid functions such as peak load reduction, voltage regulation, and frequency stabilization.
By combining wireless convenience, intelligent decision-making, and grid support, the invention improves energy efficiency, reduces switching losses, and enhances the scalability and resilience of smart grid infrastructure.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention introduces an AI-enhanced, bidirectional wireless EV charging system capable of real-time power flow control and intelligent grid support. The system employs adaptive control strategies using machine learning to manage energy exchange based on grid load, user patterns, and environmental conditions. It overcomes traditional problems of efficiency, misalignment, and limited grid interaction by offering optimized wireless power transfer and active participation of EVs in grid stability functions. It thus ensures a reliable, efficient, and scalable solution for next-generation energy networks.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The system enables wireless, bidirectional power transfer between electric vehicles (EVs) and the power grid using resonant inductive coupling. AI algorithms predict user behavior, driving patterns, and grid conditions to optimize charging and discharging schedules. The vehicle acts as a mobile energy storage unit, providing power back to the grid during peak demand. Adaptive control algorithms ensure efficient alignment and power transfer, even with positional mismatches. Real-time monitoring and communication with the grid allow the system to support frequency regulation, voltage balancing, and demand-response functionalities, effectively integrating EVs as active participants in the smart grid ecosystem.
Conventional electric vehicle (EV) wireless charging systems suffer from misalignment inefficiencies, lack of real-time adaptability, high switching losses, and limited grid support capabilities. Existing bidirectional charging methods lack intelligent control, reducing their effectiveness in dynamic grid environments. This patent proposes an Adaptive AI-Based Bidirectional Wireless EV Charging and Grid Support System that integrates machine learning for behavioral prediction, adaptive resonance-based power transfer, and intelligent grid interaction. The system ensures optimal coil alignment, minimizes energy losses, supports grid frequency and voltage regulation, and enhances overall power stability. It transforms EVs into mobile energy assets, seamlessly interacting with the smart grid for improved efficiency and resilience.
This system uniquely combines AI-driven predictive control with wireless, bidirectional energy transfer. It adapts in real-time to grid conditions and user behavior, improving energy efficiency and supporting grid stability. Unlike conventional systems, it integrates intelligent alignment control, making wireless charging practical, efficient, and responsive for dynamic EV-grid interactions.
The present invention relates to electric vehicle charging systems, and more particularly, to an adaptive AI-based bidirectional wireless charging and grid support system.
With the increasing adoption of electric vehicles, conventional plug-in and wireless charging methods face significant limitations. Existing wireless charging systems often suffer from coil misalignment losses, rigid power transfer control, and lack of effective grid integration. Moreover, unidirectional charging approaches prevent EVs from contributing energy back to the grid, missing opportunities for demand-response, peak shaving, and voltage stabilization.
The proposed invention overcomes these drawbacks by introducing a system that integrates artificial intelligence, resonant wireless transfer, and bidirectional energy management. The charging unit employs resonant inductive coupling with adaptive alignment control, ensuring efficient transfer even under positional mismatches. An AI-driven controller predicts user driving behavior, energy requirements, and grid conditions, optimizing charging and discharging cycles accordingly.
The system includes a bidirectional power converter equipped with soft-switching techniques to reduce switching losses and thermal stress. It further incorporates predictive control mechanisms to minimize overshoot during sudden load variations. A secure IoT-based communication module ensures reliable interaction with the smart grid, enabling real-time demand-response, frequency regulation, and voltage balancing.
By transforming EVs into intelligent mobile storage assets, the invention supports smart grid resilience, reduces infrastructure strain, and provides cost-effective scalability for future energy ecosystems.
The best method of implementing the invention comprises installing a resonant inductive coupling system between the EV and the charging station. The system employs adaptive modular coil structures to facilitate efficient power transfer even under positional deviations. A bidirectional power electronic converter, configured with LCL or S-S compensation topology, ensures efficient wireless transfer with soft-switching operation.
An AI controller, trained using reinforcement learning or predictive models, continuously monitors grid signals, historical energy data, and user behavior patterns to dynamically adjust charging and discharging schedules. Edge-based AI deployment ensures low-latency decision-making.
The IoT communication module establishes secure real-time data exchange with the smart grid, supporting functions such as demand-response, peak load reduction, and frequency stabilization. Predictive control minimizes overshoot, maintaining voltage and current stability under varying load conditions.
Through this method, the EV operates not only as a consumer but also as a flexible grid asset, capable of storing and supplying energy when required, thereby improving system reliability and energy efficiency.
, C , Claims:1. An adaptive AI-based bidirectional wireless electric vehicle (EV) charging and grid support system comprising a resonant inductive coupling unit for wireless power transfer, an artificial intelligence controller configured to predict user behavior and grid conditions, a bidirectional power flow management circuit enabling charging and discharging between EV and grid, and a secure IoT communication module for real-time monitoring and validation, wherein the system dynamically optimizes alignment, switching, and energy distribution to enhance efficiency and grid stability.
2. The system as claimed in claim 1, wherein the AI controller employs reinforcement learning or predictive algorithms to determine optimal charging and discharging schedules.
3. The system as claimed in claim 1, wherein the resonant inductive coupling unit comprises adaptive coil alignment control for minimizing misalignment losses.
4. The system as claimed in claim 1, wherein the bidirectional power flow management circuit utilizes soft-switching techniques selected from zero-voltage switching (ZVS) or zero-current switching (ZCS).
5. The system as claimed in claim 1, wherein the IoT communication module is configured to exchange real-time data with the smart grid for demand-response functions.
6. The system as claimed in claim 1, wherein predictive control minimizes voltage and current overshoot under sudden load variations.
7. The system as claimed in claim 1, wherein the system performs frequency regulation and voltage balancing to support grid stability.
8. The system as claimed in claim 1, wherein embedded AI models are implemented on edge devices to reduce computational and infrastructure costs.
9. The system as claimed in claim 1, wherein adaptive resonance-based topologies including LCL or S-S compensation networks are employed to reduce switching losses.
10. The system as claimed in claim 1, wherein the EV functions as a mobile energy storage asset providing peak shaving, voltage support, and load balancing.