融合机器视觉的插电式混合动力汽车预测型能量管理策略  

Predictive Energy Management Strategy of Plug-in Hybrid Electric Vehicle with Computer Vision

作　　者：王姝[1] 韩琪 赵轩[1] 谢鹏辉 Wang Shu;Han Qi;Zhao Xuan;Xie Penghui(School of Automobile,Chang’an University,Xi’an 710000)

机构地区：[1]长安大学汽车学院,西安710000

出　　处：《汽车工程》2025年第4期625-635,共11页Automotive Engineering

基　　金：国家自然科学基金(52472397,52172362);陕西省重点研发计划项目(2024GX-YBXM-260);陕西省科技成果转化计划项目(2024CG-CGZH-19)资助。

摘　　要：针对传统模型预测控制下车速预测不准确和SOC适应性差的问题,以插电式混合动力汽车(PHEV)为研究对象,将基于机器视觉的车速预测模型与深度确定性策略梯度算法(DDPG)相结合,实现PHEV的实时SOC参考轨迹规划和最优动力分配控制。构建基于改进深度确定性策略梯度算法的SOC参考轨迹规划模型,并构建基于机器视觉的级联式长短时间记忆网络车速预测模型,在此基础上使用基于模型预测控制的最优控制器,实现SOC参考轨迹精确跟踪及功率优化。结果表明,相较于传统的DDPG,本文提出的策略使得整车经济性提高了5.66%,达到了全局最优算法的97.93%。同时较不使用机器视觉的能量管理策略提高了2.92%的整车经济性。For the problems of inaccurate speed prediction and poor SOC adaptability under the traditional model predictive control,the plug-in hybrid electric vehicle (PHEV) is taken as the research object,and the speed prediction model based on computer vision is combined with the deep deterministic policy gradient (DDPG) algorithm to achieve the real-time state of charge (SOC) reference trajectory planning and optimal power allocation control of PHEV.A SOC reference trajectory planning model based on the enhanced DDPG is constructed,and a speed prediction model based on computer vision with cascaded long short-term memory network is constructed,based on which the optimal controller based on the model predictive control is used to achieve the accurate tracking of the SOC reference trajectory and power optimization.The results show that compared to the traditional DDPG,the strategy proposed in this paper increases the overall vehicle economy by 5.66%,reaching 97.93% of the global optimal algorithm.It also improves the overall vehicle economy by 2.92% compared to the energy management strategy without computer vision.

关 键 词：插电式混合动力 能量管理策略 机器视觉 车速预测 参考轨迹 

分 类 号：U469.7[机械工程—车辆工程]