The invention discloses an electric vehicle charging method and device based on multi-agent deep reinforcement learning, and the method comprises the steps: building a charging control model based on a Markov game framework and a real-time charging scene, and the charging control model comprises a state variable, an action variable, a reward function and a state transfer function; constructing a control strategy network based on the charging control model, and constructing a parameter updating model of the control strategy network; and constructing an evaluation network, and optimizing the control strategy network based on the evaluation network and the parameter updating model to obtain an optimal control strategy. A charging control model is constructed through a Markov game framework, so that multi-agent collaborative optimization of vehicle charging control is constructed, charging resources of each electric vehicle are dynamically allocated, it is ensured that under limited power grid resources, the charging requirements of all vehicles are met to the maximum extent, and the charging efficiency and economic benefits in different scenes are planned as a whole.
本发明公开了基于多智能体深度强化学习的电动汽车充电方法及装置,其方法包括:基于马尔可夫博弈框架及实时充电场景构建充电控制模型,所述充电控制模型包括状态变量、动作变量、奖励函数和状态转移函数;基于所述充电控制模型构建控制策略网络,并构建所述控制策略网络的参数更新模型;构建评价网络,并基于所述评价网络及所述参数更新模型对所述控制策略网络进行优化,获取最优控制策略。通过马尔可夫博弈框架构建充电控制模型,以构建车辆充电控制的多智能体协同优化,动态分配每辆电动汽车的充电资源确保在有限的电网资源下,最大化满足所有车辆的充电需求,以统筹不同场景中的充电效率及经济效益。
Electric vehicle charging method and device based on multi-agent deep reinforcement learning
基于多智能体深度强化学习的电动汽车充电方法及装置
2025-03-11
Patent
Electronic Resource
Chinese
European Patent Office | 2022
|European Patent Office | 2023
|European Patent Office | 2025
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