基于随机动态规划的PHEV队列分层优化控制策略研究  

Hierarchical Optimization Control Strategy for Plug-in Hybrid Electric Vehicles Queue Based on Stochastic Dynamic Programming

作　　者：朱兰馨 周长登 崔佳伦 Zhu Lanxin;Zhou Changdeng;Cui Jialun(Kunming University of Science and Technology,Kunming 650500;Kunming Branch of the 705 Research Institute,China Shipbuilding Industry Corporation,Kunming 650101)

机构地区：[1]昆明理工大学,昆明650500 [2]中国船舶重工集团公司第705研究所昆明分部,昆明650101

出　　处：《汽车技术》2023年第9期9-17,共9页Automobile Technology

摘　　要：以插电式混合动力汽车(PHEV)队列为研究对象,提出了一种基于径向基神经网络(RBFNN)与随机动态规划(SDP)的PHEV队列分层控制策略。详细分析了PHEV的动力总成结构及其数学模型,构建了分层控制框架,上层采用RBFNN训练来自模型预测控制(MPC)的驾驶数据,以速度控制器进行跟车控制,下层控制器根据上层传递的车速及需求功率等信息建立马尔可夫链模型,基于SDP理论实现PHEV动力电池与发动机之间的最优能量分配。仿真结果表明:高速工况下,在确保安全行驶的同时,相较于电量消耗-电量维持(CD/CS)策略和规则控制策略,基于所提出的策略,队列中车辆综合能耗明显降低。A hierarchical control strategy based on Radial Basis Function Neural Network(RBFNN)and Stochastic Dynamic Programming(SDP)was proposed for Plug-in Hybrid Electric Vehicle(PHEV)queue.Firstly,the powertrain structure and mathematical model of PHEV were analyzed in detail,then a hierarchical control framework was constructed.The upper layer adopted RBFNN to train driving data derived from Model Predictive Control(MPC)to generate the speed tracking controller.According to the information of the speed and power demand transmitted from the upper layer,a Markov chain model was established for the lower layer controller,the Markov chain model can realize the optimal energy distribution between PHEV traction battery and the engine based on the SDP theory.The simulation results show that compared with CD/CS strategy and rule-based strategy,the energy consumption of PHEVs in the queue is significantly reduced while ensuring safe driving under high-speed conditions based on the proposed strategy.

关 键 词：分层优化 径向基神经网络 随机动态规划 马尔可夫链 

分 类 号：U469.7[机械工程—车辆工程]