Crowdsourced EV Charging Control for V2V and V2G Energy Transfer
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Solution Overview
Problem
Current electrical vehicle charging systems lack efficient and flexible infrastructure for energy transfer between vehicles and the grid, leading to limitations in vehicle range, charging convenience, and grid energy management.
Innovation Solution
The implementation of intelligent charging systems with agent-mediated, market-based approaches for vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer, utilizing a middleware node to facilitate energy requests, bid selection, and task allocation among registered service providers, enabling optimized energy distribution and storage within a geo-fenced region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed grid-based charging infrastructure is used, then charging reliability is ensured, but infrastructure cost and complexity increase
Solution Approach 1:
The patent introduces a mobile service provider vehicle as an intermediary charging unit that can travel to customers' locations. This mobile unit acts as a mediator between the grid and the electric vehicle, providing charging services without requiring fixed infrastructure at the customer's location, thus reducing infrastructure complexity while maintaining charging reliability.
Solution Approach 2:
The system enables customers to request charging services through a user interface, and the mobile service provider autonomously navigates to the customer's location and performs charging operations. This self-service approach reduces the need for extensive fixed infrastructure while ensuring reliable charging delivery.
2Adaptability or versatility
If mobile service providers are deployed for V2V and V2G energy transfer, then charging flexibility and coverage improve, but system coordination complexity increases
Solution Approach 1:
The mobile service provider vehicle is designed with multi-functionality, capable of performing both V2V (vehicle-to-vehicle) and V2G (vehicle-to-grid) energy transfers. This universal design allows a single platform to handle multiple charging scenarios, increasing flexibility while managing coordination complexity through standardized protocols.
Solution Approach 2:
The system incorporates feedback mechanisms where the user interface receives customer requests, the route optimization module processes this information, and the mobile service provider executes charging operations while reporting status back to the system. This closed-loop feedback structure coordinates the mobile providers efficiently, managing system complexity through real-time information exchange.
3Loss of time
If route optimization algorithms are implemented for multiple service providers, then travel time and energy consumption reduce, but computational requirements increase
Solution Approach 1:
The route optimization problem is segmented into manageable components: the user interface collects charging requests, the route optimization module processes these requests separately, and each mobile service provider executes its own optimized route. This segmentation allows computational tasks to be distributed and processed efficiently, reducing overall computational requirements while minimizing travel time.
Solution Approach 2:
The route optimization algorithm performs preliminary calculations to determine optimal routes before mobile service providers depart. By pre-computing routes based on customer requests and provider locations, the system minimizes travel time without requiring continuous high-power computation during execution, thus balancing computational requirements with time efficiency.
Data Source
AI summary
Presented are intelligent charging systems for transferring energy to and from motor vehicles, methods for making/operating such systems, and vehicles with V2V and V2G energy transfer capabilities. A method for operating an intelligent charging system includes a system controller receiving, from a user interface of a user, a request to schedule a transfer of electricity to the user. The system controller broadcasts to a crowdsourced set of service providers a solicitation for energy transfer bids for completing the user's transfer request. After receiving multiple bid submissions for completing the transfer request from multiple service providers, the system controller selects an optimized one of the bid submissions using a multi-criteria selection strategy. The multi-criteria selection strategy includes a joint utility maximization function and/or a trade-off Pareto solution. The system controller transmits to the service provider associated with the optimized bid submission a task allocation with instructions to provide the transfer request.