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Model Predictive Control for Nonlinear Energy Management of a Power Split Hybrid Electric Vehicle

Dehua Shi1,4, Shaohua Wang1,2,*, Yingfeng Cai1, Long Chen1, ChaoChun Yuan1, ChunFang Yin3

1Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2 School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
3 School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
4 Jiangsu Chunlan Clean Energy Research Institute Co., Ltd., Taizhou 225300, China

* Corresponding Author: Shaohua Wang, email

Intelligent Automation & Soft Computing 2020, 26(1), 27-39.


Model predictive control (MPC), owing to the capability of dealing with nonlinear and constrained problems, is quite promising for optimization. Different MPC strategies are investigated to optimize HEV nonlinear energy management for better fuel economy. Based on Bellman’s principle, dynamic programming is firstly used in the limited horizon to obtain optimal solutions. By considering MPC as a nonlinear programming problem, sequential quadratic programming (SQP) is used to obtain the descent directions of control variables and the current control input is further derived. To reduce computation and meet the requirements of real-time control, the nonlinear model of the system is approximated to be linear and linear time-varying (LTV) MPC strategy is studied. Simulation results demonstrate that the nonlinear MPC using SQP algorithm has best fuel economy, while the MPC using approximated linear model is superior in saving computation time.


Cite This Article

D. Shi, S. Wang, Y. Cai, L. Chen, C. Yuan et al., "Model predictive control for nonlinear energy management of a power split hybrid electric vehicle," Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 27–39, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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