Distributed EV Battery Charging Coordination via Bidirectional Signaling
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Solution Overview
Problem
Current intelligent charging solutions for electric vehicle batteries are centralized and unidirectional, requiring users to share personal data and lacking user decision dynamics, which leads to suboptimal charging times and increased network costs due to transformer aging and Joule losses.
Innovation Solution
A distributed management method for electric vehicle traction batteries that uses charge management automatons to establish bidirectional communication with a network manager, determining optimal charging profiles based on overall electrical consumption signals, allowing users to make individual choices that minimize costs while considering network impacts, without sharing personal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized charging management is implemented with a central planner collecting personal user data, then charging optimization for network constraints is improved, but user privacy is compromised and system complexity increases
Solution Approach 1:
The system segments the centralized charging management into distributed autonomous agents: a network manager agent and multiple user agents. Each agent operates independently with local decision-making capabilities, eliminating the need for a central planner to collect personal user data while maintaining coordinated charging optimization across the network.
Solution Approach 2:
The invention introduces an intermediary communication protocol that enables indirect interaction between user agents and the network manager agent through anonymous signaling. This intermediary layer allows charging optimization without direct exposure of personal user information, resolving the contradiction between optimization reliability and privacy protection.
2Ease of operation
If users are given full control over charging decisions without centralized coordination, then user autonomy and privacy are improved, but charging optimization for network constraints deteriorates
Solution Approach 1:
The system implements feedback loops where user agents receive signaling from the network manager agent about network constraints and charging conditions. This feedback enables autonomous user agents to make optimized charging decisions independently, maintaining both user autonomy and charging optimization without requiring centralized control.
Solution Approach 2:
Each user agent autonomously manages its own charging decisions based on local information and received signaling, eliminating the need for users to share personal data or for centralized control. The system achieves coordinated optimization through self-service autonomous decision-making at each user endpoint.
Data Source
Figure 1~2
AI summary
The invention relates to a method for managing the charging of traction batteries of electric vehicles, where each battery (10) is charged across one and the same local electrical energy distribution network (20), characterized in that it comprises a step of co-ordination of the charging, where there is provided a charging management automaton associated with each battery, able to establish a bidirectional communication with a battery manager (30) implemented at the level of said network, and where, before the actual start of charging, an optimal charging profile is determined for each battery, with the aid of said associated charging management automaton, by taking account of an electrical consumption signal provided to said automaton by said manager, representative of a profile of global electrical consumption envisaged on said network resulting from individual choices of charging profile that are specific to each battery and are emitted iteratively by each respective automaton destined for said manager.