Stochastic Model Predictive Control for Dual-Motor Battery Electric Bus Based on Signed Markov Chain Monte Carlo Method

Abstract

With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

Keywords

• Energy management strategy
• dual-motor coupling powertrain
• driving cycle recognition
• signed Markov chain Monte Carlo method
• stochastic model predictive control