Open Access

ARTICLE

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

Dehua Shi^1,4, Shaohua Wang^1,2,*, 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,

Intelligent Automation & Soft Computing 2020, 26(1), 27-39. https://doi.org/10.31209/2018.100000062

Abstract

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.

---

Keywords

---