首页|基于KL散度工况识别的混合动力汽车队列的分层控制

基于KL散度工况识别的混合动力汽车队列的分层控制

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针对混合动力汽车队列行驶过程中工况的适应性问题,提出了一种基于KL(Kullback-Leibler)散度工况识别的分层控制方法.上层控制器利用车—车通信技术,获取队列中前车状态信息,采用模型预测控制(MPC)算法,实现队列纵向控制,并计算出最优跟车车速;下层控制器基于典型工况,离线求解需求功率的转移概率矩阵,并通过Q-Learning算法训练最优Q表嵌入整车模型中;在行驶中以固定时间长度在线更新转移概率矩阵,采用KL散度进行工况识别,根据识别的工况类型,结合当前时刻车速、需求功率和电池荷电状态(SOC),通过在线查表实现转矩分配.结果表明:与未考虑工况识别策略相比,本策略的油耗降低了8.6%;与作为基准的动态规划(DP)相比,增加了4.8%;在与DP油耗基本保持相同的前提下,该策略离线仿真时间缩短21%,不仅能够在线应用,还能实时适应工况变化.
Hierarchical control for hybrid electric vehicle platoon based on KL divergence working condition recognition
A hierarchical control method was proposed for adapting the working condition in the driving process for hybrid-electric-vehicle(HEV)platoon based on a Kullback-Leibler(KL)divergence working condition recognition algorithm.The upper layer controller utilized vehicle-vehicle communication technology,obtained state information of the leading vehicle in the platoon.It adopted the Model Predictive Control(MPC)algorithm,achieved longitudinal control of the platoon,and calculated the optimal speed for the following vehicle.The lower layer controller,initially based on typical working conditions,solved the transition probability matrix of demanded power offline.Trained the optimal Q-table offline,embedded it in the complete vehicle model by the Q-Learning algorithm.During driving,the transition probability matrix was updated online at regular intervals,and KL divergence was used to recognition the working conditions.According to the identified working conditions types,combined the current moment vehicle speed,the demanded power,and the battery state of charge(SOC),the torque allocation were achieved through an online lookup table.The results show that the fuel consumption of this strategy is reduced by 8.6%compared with the strategy without considering the condition identification,and increased by 4.8%compared with the dynamic programming(DP)as the benchmark.Under the premise of maintaining the same fuel consumption as DP,the off-line simulation time is reduced by 21%,which can not only be applied online,but also adapt to the change of working conditions in real time.

hybrid electric vehicle(HEV)vehicle platoonworking condition recognitionmodel predictive control(MPC)algorithmQ-Learning algorithmKL(Kullback-Leibler)divergence

尹燕莉、王福振、詹森、黄学江、张鑫新、张富椿

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重庆交通大学 机电与车辆工程学院,重庆 400074,中国

混合动力汽车 汽车队列 工况识别 模型预测控制(MPC)算法 Q-Learning算法 KL(Kullback-Leibler)散度

四川省新能源汽车智能控制与仿真测试技术工程研究中心项目城市轨道交通车辆系统集成与控制重庆市重点实验室开放基金重庆市教委科学技术研究计划重庆交通大学研究生科研创新项目

XNYQ2022-003CKLURTSIC-KFKT-212005KJQN202000734CYS23505

2024

10.3969/j.issn.1674-8484.2024.02.013

汽车安全与节能学报
清华大学

汽车安全与节能学报

CSTPCD北大核心
影响因子：0.748
ISSN：1676-8484
年,卷(期)：2024.15(2)
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