Dehua Shi1,4, Shaohua Wang1,2,*, Yingfeng Cai1, Long Chen1, ChaoChun Yuan1, ChunFang Yin3
Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 27-39, 2020, DOI: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 More >
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