EV Charging Policy Combination for Fair Power Distribution
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Solution Overview
Problem
Existing charging systems for electric vehicles face challenges in efficiently distributing power among multiple vehicles, leading to uneven battery charging and user dissatisfaction due to the need for manual input of user data and the limitations of rule-based control methods.
Innovation Solution
A method that combines multiple charging control policies using historical data and multi-objective evolutionary optimization to distribute energy fairly among electric vehicles, minimizing average and maximum additional charging times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple charging control policies are combined using historical data and multi-objective evolutionary optimization, then user satisfaction and fairness in energy distribution are improved, but computational complexity and system requirements increase
Solution Approach 1:
The system performs preliminary computation of optimization parameters and charging schedules in advance, using historical charging data to pre-calculate optimal energy distribution strategies. This allows the system to prepare charging plans before actual charging sessions begin, reducing real-time computational burden while maintaining high user satisfaction and fairness in energy allocation.
2Reliability
If coordinated charging is implemented to avoid overloading the power grid, then power grid stability is improved, but the batteries of some electric vehicles are not charged as much as desired or as technically possible
Solution Approach 1:
The system dynamically adjusts charging power distribution based on real-time grid conditions, vehicle battery states, and predicted power availability. By continuously monitoring and adapting charging rates, the system maintains power grid stability while maximizing the charging amount for each vehicle within the constraints of available power, ensuring neither grid overload nor unnecessary charging limitations occur.
3Device complexity
If rule-based control is used for charging distribution, then computational cost is reduced, but power management efficiency and user satisfaction decrease
Solution Approach 1:
The system automatically collects charging data from vehicles and users, and autonomously performs optimization calculations to determine energy distribution without requiring manual user input or complex real-time interactions. This self-service approach enables the system to implement sophisticated multi-objective optimization while keeping operational complexity low, as the system independently manages data collection, processing, and charging control.
Data Source
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AI summary
The invention concerns to a method and system for controlling charging processes for charging electric vehicles by a charging system. The charging system performs charging the batteries of the electric vehicles based on a plurality of charging control policies. The method comprises acquiring historical information on charging parameters and battery parameters from a database, and determining information on the available total amount of energy for charging the batteries the electric vehicles for each time step. The method computes, for each time step, and for each charging control policy of the plurality of charging control policies, based on the acquired historical information, a fraction of the total amount of energy for charging the batteries of the electric vehicles with the charging control policy. The method then controls charging, during each time step, the batteries of the electric vehicles based on the plurality of charging control policies and the computed fraction of the total amount of energy for each charging control policy.