基于多信息交互与深度强化学习的电动汽车充电导航策略

针对电动汽车动态行驶行为和随机充电行为的多信息融合特征以及多系统建模复杂度,提出了一种基于多信息交互与深度强化学习的电动汽车充电导航策略。该策略首先对“电动汽车集群优化储能云平台”采集的电动汽车实际运行数据进行建模与挖掘,通过数据预处理以及数据可视化显示得到电动汽车行驶、充电信息以及城市充电站信息。其次,分析了电动汽车充电调度过程符合马尔科夫决策定义,引入深度强化学习方法建立了充电导航模型。将“车-站-网”实时信息作为深度Q网络算法的状态空间,并将充电站的分配作为智能体的执行动作。通过对充电过程不同时段出行的成本和时间决策目标的评估,确定行驶途中与到站后的奖励函数。执行最高奖励对应的最优动作-值函数,为车主推荐最优充电站和规划行驶路径。最后,设计了多场景仿真算例验证了所提策略的可行性和有效性。

Electric Vehicle Charging Navigation Strategy Based on Multi-Information Interaction and Deep Reinforcement Learning

For the multi-information fusion feature and multi-system modeling complexity of dynamic driving behavior and random charging behavior for electric vehicles(EVs), a charging navigation strategy of EVs based on multi-information interaction and deep reinforcement learning(DRL) is proposed in this paper. In this strategy, the EV actual operation data collected by ‘optimization energy storage cloud platform of electric vehicle clusters’ is firstly modeled and mined. Through data preprocessing and data visual display, the EV driving and charging information as well as urban charging station information are obtained. Then, the EV charging scheduling process conforming to Markov decision process(MDP) is analyzed, and the DRL is introduced to establish a charging navigation model. The real-time information of ‘vehicles-stations-networks’ is regarded as the state space of deep Q network(DQN), and the allocation of charging station is taken as the execution action of agents. Based on the evaluation of travel cost and time decision-making objectives in different periods of charging process, the reward functions of enroute and arrival a station is determined. The optimal action value function corresponding to the highest reward is implemented to recommend the optimal charging station and plan for the driving path for the owner. Finally, a multi-scene simulation example is designed to verify the feasibility and effectiveness of the strategy proposed in this paper.