Energy management strategy for fuel cell hybrid electric vehicles using Pontryagin’s minimum principle and dynamic SoC planning

ABSTRACT

A reasonable and reliable energy management strategy (EMS) is crucial for fuel cell hybrid electric vehicles (FCHEV). A nonlinear State of Charge (SoC) trajectory planning with Proportional Integral Controller-based Pontryagin’s minimum principle (Nonlinear PI-PMP) EMS is proposed to adjust the power battery SoC consumption rate in response to various power demands and to prevent the fuel cell system (FCS) from operating under unfavorable working conditions. The SoC reference trajectory is updated based on the predicted vehicle speed during the drive, and the PI controller is used to update the co-state in real-time. To comply with the SoC trajectory update framework, a speed prediction method based on LVQ neural network clustering and Improved Markov Velocity Predictor (IMVP) is introduced. Moreover, to balance the economy and durability of FCS, a penalty term is added to the Hamiltonian function to suppress FCS power output fluctuations and maintain the FCS output power within the high-efficiency range. Through the construction of a hybrid driving cycle, the simulation results validate that the proposed nonlinear PI-PMP EMS can significantly reduce hydrogen consumption. Specifically, when the initial SoC is 30%, compared to the Linear PI-PMP, A-PMP, and fuzzy logic strategies, the nonlinear PI-PMP EMS achieves reductions of 4.22%, 4.27%, and 15.75% respectively. Moreover, it efficiently decreases FCS power fluctuations to 1.12.

KEYWORDS:

• Fuel cell electric vehicle
• energy management strategy
• SoC trajectory
• velocity prediction
• Pontryagin’s minimum principle

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the Shanxi Provincial Key Research and Development Project [Grant No. 202102110401020], Shanxi Scholarship Council of China [Grant No. 2021-050], Natural Science Foundation of Shanxi Province [Grant No. 202103021224040].

Notes on contributors

Zhaoyi Yao

Zhaoyi Yao is currently studying as a postgraduate in the College of Mechanical and Vehicle Engineering, Taiyuan university of technology, Taiyuan, China. His research interests is hybrid electric vehicle energy management. Ruipeng Shao is currently studying as a postgraduate in the College of Mechanical and Vehicle Engineering, Taiyuan university of technology, Taiyuan, China. His research interests is vehicle dynamics and control.

Ruipeng Shao

Ruipeng Shao is currently studying as a postgraduate in the College of Mechanical and Vehicle Engineering, Taiyuan university of technology, Taiyuan, China. His research interests is vehicle dynamics and control.

Shanning Zhan

Shanning Zhan is currently studying as a postgraduate in the College of Mechanical and Vehicle Engineering, Taiyuan university of technology, Taiyuan, China. His research interests are vehicle energy management and deep learning.

Rongjia Mo

Rongjia Mo received the M.S. degree in the College of Mechanical and Vehicle Engineering, Taiyuan university of technology, Taiyuan, China, in 2023. He is now employed at BYD Company Limited, Shenzhen, China. His research interests are hybrid electric vehicle energy management and vehicle dynamics.

Zhifei Wu

Zhifei Wu received the B.S. degree in mechanical engineering from Chang’an University, Xi’an, China, in 2005, and the M.S. and Ph.D. degrees in mechanical engineering from the Taiyuan University of Technology, Taiyuan, China, in 2008 and 2012, respectively. He is currently an Associate Professor with the College of Mechanical and Vehicle Engineering, Taiyuan University of Technology. His current research interests are the research of new energy vehicles and vehicle dynamics.