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This work was supported by National Natural Science Foundation of China (Grant Nos. 61773090, 61304128).
[1] Borhan H, Vahidi A, Phillips A M. MPC-based energy management of a power-split hybrid electric vehicle. IEEE Trans Control Syst Technol, 2012, 20: 593-603 CrossRef Google Scholar
[2] Zhang J Y, Shen T L. Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Trans Control Syst Technol, 2016, 24: 2167-2175 CrossRef Google Scholar
[3] Di Cairano S, Bernardini D, Bemporad A. Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management. IEEE Trans Control Syst Technol, 2014, 22: 1018-1031 CrossRef Google Scholar
[4] Malikopoulos A A. A multiobjective optimization framework for online stochastic optimal control in hybrid electric vehicles. IEEE Trans Control Syst Technol, 2016, 24: 440-450 CrossRef Google Scholar
[5] Xiang C L, Ding F, Wang W D. MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle. Sci China Technol Sci, 2017, 60: 737-748 CrossRef Google Scholar
[6] Zhao Y, Li Z Q, Cheng D Z. Optimal control of logical control networks. IEEE Trans Automat Control, 2011, 56: 1766-1776 CrossRef Google Scholar
[7] Wu Y H, Shen T L. An algebraic expression of finite horizon optimal control algorithm for stochastic logical dynamical systems. Syst Control Lett, 2015, 82: 108-114 CrossRef Google Scholar
Figure 1
(Color online) A validation result. (a) Control law; (b) instantaneous vehicle speed, torque commands and SoC.
