基于交通预测信息的电动汽车充电路径规划  

Electric Vehicle Charging Routing Planning Based on Traffic Prediction

作　　者：师泽宇 陈阳舟 安树科 SHI Zeyu;CHEN Yangzhou;AN Shuke(Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China;College of Artificial Intelligence and Automation,Faculty of Information Techology,Beijing University of Technology,Beijing 100124,China;School of Public Security Management,Yunnan Police College,Kunming 650223,China)

机构地区：[1]北京工业大学交通工程北京市重点实验室,北京100124 [2]北京工业大学信息学部人工智能与自动化学院,北京100124 [3]云南警官学院治安管理学院,昆明650223

出　　处：《北京工业大学学报》2024年第8期974-984,共11页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61573030);云南省教育厅科学研究基金资助项目(2022J0587)。

摘　　要：针对电动汽车充电路径规划问题,以行程时间为优化目标,以荷电状态作为硬性约束,基于预测交通状态标定的动态异构交通网及车辆动力学模型,构建最优控制问题。为了在线求解,基于模型预测控制和强化学习方法,构建混合学习优化算法(hybrid learning optimization algorithm, HLOA),依托竞争深度Q网络(Dueling deep Q-networks, Dueling DQN),设计离线、在线模块,来实现参数的混合多步训练,并结合预测交通状态、网络拓扑及车辆动力学模型,输出在线行驶策略。采用某市局部路网构建交通仿真实验,分析预测交通信息的价值、HLOA算法可行性及结构必要性。结果表明,预测信息存在价值,HLOA在线结果可拟合最优策略且较其他算法精度提高33.65%。该研究可协助电动汽车用户导航,为自动驾驶电动汽车提供技术支撑。Aiming at the electric vehicle charging routing problem,an optimal control model was built based on the dynamic heterogeneous traffic network,vehicle dynamics model,and predicted traffic state.The travel time of the vehicle is the optimized objective and the battery state is the hard constraint in the model.To solve the optimal control model online,a hybrid learning optimization algorithm(HLOA)was constructed based on the spatial-discrete model predictive control theory and reinforcement learning method.Relying on Dueling deep Q-networks(Dueling DQN),the algorithm has designed two solving modules,i.e.,offline and online,to realize the mixed multi-step training of its parameters.After the training,the algorithm combines the predicted traffic state,dynamic heterogeneous traffic network,and vehicle dynamics model to output online driving strategies in real-time.Based on the above methods,the local road network in one city is the experimental scenario.Combined with the traffic simulation,the competitive ratio was used as the evaluation index to analyze the value of the predicted traffic information and the feasibility of HLOA.The experimental results show the value of the prediction information and the feasibility of HLOA.The competitive ratio of HLOA is 33.65%higher than the other algorithms.The algorithm can help electric vehicle users to plan their trips.It can also provide technical support for autonomous vehicles.

关 键 词：智能交通系统 车辆路径规划 电动汽车充电路径规划 强化学习 在线寻优 混合学习优化算法 

分 类 号：U492.2[交通运输工程—交通运输规划与管理]