Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

IEEE Access

Hong-Qiang Guo¹  Xing-Qun Cheng¹  Fahad Muhammad²  Cong-Zhi Liu²  Jia-Wang Yong² 

[1] School of Mechanical and Automotive Engineering, Liaocheng University, Shandong, China;State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China;

关键词: Plug-in hybrid electric vehicle;    model predictive iterative learning control;    battery aging;    nonlinear optimization;    2-D Lyapunov stability theory;   

DOI  :  10.1109/ACCESS.2019.2919684

来源: DOAJ

【 摘 要 】

A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)-based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV.

【 授权许可】

Unknown