An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses. (15th June 2017)

• Record Type: Journal Article
• Authors: Xie, Shanshan; He, Hongwen; Peng, Jiankun
• Is Part Of: Applied energy. Volume 196(2017)
• Page Start: 279
• Page End: 288
• Publication Date: 2017-06-15
• Subjects: Stochastic model predictive control -- Markov Chain Monte Carlo Method -- Plug-in hybrid electric bus -- Energy management strategy -- Hardware-in-the-loop experiment; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.12.112
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Velocity is predicted by multi scale single step method with post-processing. A state reconstitution method is proposed to tackle reference state deficiencies. SMPC-based strategies with variable horizons are built to improve energy management for practical cycle. HIL experiments with practical driving cycles are conducted to verify the strategy. Abstract: Model predictive control (MPC) can effectively solve online optimization issues, even with various constraints, when maintained at high robustness. Considering the energy management issue of plug-in hybrid electric bus (PHEB) as a constrained nonlinear optimization problem, a strategy based on stochastic model predictive control (SMPC) is put forward and verified in this paper. Firstly, Markov Chain Monte Carlo Method (MCMC) is adopted to forecast velocity sequences at every current state, in the form of multi scale single step (MSSS), with post-processing algorithms to moderate fluctuations of the prediction results like average filtering, quadratic fitting, and the like. The offline simulation results show that the optimization can effectively improve the predictive accuracy, make the following energy management feasible and reduce the fuel consumption by 1.9%. Then the SMPC-based energy management strategy is proposed. In order to prevent the driving cycle state deficiencies from interrupting the prediction for practical application, a state reconstitution method is constructed accordingly. Besides, the predictive steps are made time-varying by an online accuracy estimation method and a corresponding threshold to maintain the accuracy of forecast. Finally, the hardware-in-the-loop (HIL) experiments are conducted and the results show that the SMPC-based strategy is reasonable and the fuel consumption decreases by 3.9% further with variable predictive steps than that of fixed ones. In summary, this paper illustrates an effective SMPC-based methodology for energy management for PHEB, and techniques like MSSS prediction with post-processing, state reconstitution method, online accuracy estimation can be adopted to solve similar problems.