A robust co-state predictive model for energy management of plug-in hybrid electric bus. (20th March 2020)

• Record Type: Journal Article
• Authors: Guo, Hongqiang; Liang, Binbin; Guo, Hongliang; Zhang, Kun
• Is Part Of: Journal of cleaner production. Volume 250(2020)
• Publication Date: 2020-03-20
• Subjects: Plug-in hybrid electric bus -- Energy management -- PMP -- Co-state predictive model -- Design for six sigma; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.119478
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: This paper proposes a robust co-state predictive model for Pontryagin's Minimum Principle (PMP)-based energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust co-state predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy has good real-time control performance, and can averagely improve the fuel economy by 35.19% compared to a rule-based control strategy. Graphical abstract: Image 1052 Highlights: A robust co-state predictive model is designed by the Design For Six Sigma method. The predictive model can counter the noises of driving cycles and stochastic vehicle mass. The predictive model can simultaneously minimize the mean and standard deviation of the fuel consumption. The robust and real-time control performances of the strategy are verified by hardware-in-loop test.