混合动力汽车的动力最优控制策略研究  

Study on the dynamic optimal control strategy of hybrid vehicles

作　　者：杨延勇[1] Yang Yanyong(Beijing Institute of Technology,Zhuhai,Guangdong Zhuhai,519088,China)

机构地区：[1]北京理工大学珠海学院,广东珠海519088

出　　处：《机械设计与制造工程》2019年第2期67-72,共6页Machine Design and Manufacturing Engineering

摘　　要：提出一种基于行驶特征预测和离线最优轨迹的最优控制策略,将离线最优轨迹运用到在线中。利用动态规划(DP)算法获得了离线全局最优轨迹。将行驶特征分为行驶工况和行驶模式。选取了11种标准行驶工况,利用欧几里得贴近度法实现了对行驶工况的预测;将行驶模式定义为5种类别,利用BP神经网络实现了对行驶模式的预测。采用神经网络对标准工况下离线最优轨迹及相应汽车状态进行学习,设计了基于神经网络和离线最优轨迹的能量分配模型,以及基于行驶特征预测和离线最优轨迹的最优控制策略并进行仿真验证。结果表明:与ADVISOR自带的电机助力策略相比,所提的最优控制策略使得燃油经济性提高了7.51%,同时工况适应性良好。It presents an online control strategy based on the prediction of driving characteristics and the off-line optimal trajectory.The off-line optimal trajectory is applied to the on-line control.The off-line global optimal trajectory is obtained in dynamic programming(DP)algorithm.The driving characteristics are divided into driving conditions and driving modes.Eleven standard driving conditions are selected,and the Euclidean proximity method is used to realize the prediction of driving conditions.The driving modes are defined as five categories,and the BP neural network is used to realize the prediction of driving modes.The off-line optimal trajectory and the corresponding vehicle state are studied in the neural network.The energy allocation model based on the neural network and the off-line optimal trajectory is designed.Combined with the prediction of driving characteristics,a comprehensive on-line control strategy based on the prediction of driving characteristics and the off-line optimal trajectory is designed and verified by simulation.The results show that the fuel economy of the proposed optimal control strategy is improved by 7.51% compared with the motor-assisted strategy of ADVISOR,and the operation condition adaptability of the strategy is good.

关 键 词：混合动力汽车 最优控制策略 行驶特征预测 欧几里得贴近度法 BP神经网络 

分 类 号：U462.1[机械工程—车辆工程]