Welcome to Journal of Automotive Safety and Energy,

Journal of Automotive Safety and Energy ›› 2021, Vol. 12 ›› Issue (4): 557-569.DOI: 10.3969/j.issn.1674-8484.2021.04.015

• Automotive Energy Efficiency and Environment Protection • Previous Articles     Next Articles

Model predictive control of super-mild hybrid electric vehicle based on Markov chain and Q-Learning

YIN Yanli1,2(), MA Yongjuan1, ZHOU Yawei2, WANG Ruixin2, ZHAN Sen1, MA Shenpeng1, HUANG Xuejiang1, ZHANG Xinxin1   

  1. 1. School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    2. Baotou Bei-Ben Heavy Vehicle Co.Ltd, Baotou 014000, China
  • Received:2021-05-25 Online:2021-12-31 Published:2022-01-10

Abstract:

A model predictive control energy management strategy for super-light hybrid electric vehicles (HEV) was proposed to take into account the global optimality of the energy management strategy and the real-time operation at the same time based on Markov chain and Q-Learning algorithm. The multi-step Markov model was used to predict the acceleration change process to calculate the future required power of HEV. An energy management strategy optimization model was established by taking the minimum equivalent fuel consumption and the local balance of the state of charge (SOC) of power battery as the objective function. The Q-learning algorithm was used to solve the optimization problem in the prediction time domain to obtain the optimal torque distribution sequence. The simulation analysis was carried out under the ECE_EUDC+UDDS cycle conditions on the base of MATLAB / Simulink platform. The results show that the control strategy solved by the Q-Learning solution reduces the simulation time by 4 s under the same fuel economy condition, comparing with the control strategy based on the dynamic programming (DP) solution. The proposed control strategy can significantly improve the operating efficiency and has better real-time performance.

Key words: super-light hybrid electric vehicles, model predictive control, Markov chain, Q-learning algorithm, multi-step Markov model, energy management

CLC Number: