@Article{2018.100000062,
AUTHOR = {Dehua Shi, Shaohua Wang, Yingfeng Cai, Long Chen, ChaoChun Yuan, ChunFang Yin},
TITLE = {Model Predictive Control for Nonlinear Energy Management of a Power Split Hybrid Electric Vehicle},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {27--39},
URL = {http://www.techscience.com/iasc/v26n1/39843},
ISSN = {2326-005X},
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 Bellmanâ€™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.},
DOI = {10.31209/2018.100000062}
}