Affiliations: [a] The School of Intelligent Manufacturing and Automobile, Chongqing College of Electronic Engineering, Chongqing, China
| [b] School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| [c] The Department of Chongqing Changan New Energy Vehicle Technology Ltd., Chongqing, China
Correspondence:
[*]
Corresponding author. Jun Gao. E-mail: [email protected].
Note: [1] This research was financially supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202103101), the Postdoctoral Fund of Chongqing Natural Science Foundation of China (Grant No. cstc2020jcyj-bsh0129), and partially by the Youth Project of Science and Technology Research Program of Chongqing Education Commission of China (Grant No. KJQN201802405) and School Level Research Projects (Grant No. 22XJZXZD05).
Abstract: The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study.
Keywords: Energy management strategy, Markov chain, minimum principle, optimal control
