EV Charging Policy Control for Fair Multi-Vehicle Power Allocation
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Solution Overview
Problem
Charging infrastructure for electric vehicles poses challenges due to the need for coordinated power distribution among multiple vehicles, with existing methods either requiring user input that is often inaccurate or leading to uneven battery charging, and existing rule-based systems failing to optimize power management efficiently.
Innovation Solution
A charging control system using genetic programming to determine priority scores for electric vehicles based on historical data, distributing energy fractions dynamically to optimize user satisfaction and fairness among multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based charging control is used to distribute energy equally among electric vehicles, then the control process is simple and computationally inexpensive, but the power management efficiency is low and battery charging is uneven
Solution Approach 1:
The system automatically determines departure times and energy requirements by monitoring vehicle usage patterns and battery state, eliminating the need for manual user input. The charging controller autonomously optimizes energy distribution among multiple vehicles based on real-time system state, achieving both simplicity and efficiency.
Solution Approach 2:
The patent replaces complex optimization algorithms with a rule-based control system that uses simple if-then logic to determine charging priorities. The controller substitutes sophisticated computation with straightforward decision rules based on vehicle arrival times, battery states, and predicted departure times, reducing computational burden while maintaining effective power management.
2Reliability
If user information input is required for charging optimization, then charging plans can be personalized, but infrastructure requirements increase and user convenience decreases
Solution Approach 1:
The system automatically determines departure times and energy requirements by monitoring vehicle usage patterns and battery state, eliminating the need for manual user input. The charging controller autonomously optimizes energy distribution among multiple vehicles based on real-time system state, achieving both simplicity and efficiency.
Solution Approach 2:
The system continuously monitors battery state of charge, vehicle usage patterns, and charging rates to dynamically adjust charging plans. This feedback mechanism allows the system to learn from actual usage and improve charging accuracy over time without requiring explicit user input for each charging session.
Data Source
AI summary
The disclosure concerns methods and systems for controlling charging processes for charging electric vehicles by a charging system based on charging control policies. The disclosure provides approaches for automated generating of charging control policies. The system acquires historical information on charging parameters and battery parameters, and determines, for each time step, information on the available total amount of energy for charging the electric vehicles. The system computes, for each time step, and for each charging control policy of a plurality of charging control policies, a fraction of the total amount of energy for charging the electric vehicles with the charging control policy. The system controls charging for each time step based on the plurality of charging control policies and the computed fraction of the total amount of energy for each charging control policy. The disclosure further proposes an automated generating of charging control policies using a genetic programming approach.