@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 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.},
DOI = {10.31209/2018.100000062}
}