Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus

Yongchang Du, Yue Zhao, Qinpu Wang, Yuanbo Zhang and Huaicheng Xia

Energy, 2016, vol. 115, issue P1, 1259-1271

Abstract: A trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus is presented in this paper, which includes the offline stochastic dynamic programming part and the online implementation part performed by equivalent consumption minimization strategy. In the offline part, historical driving cycles of the fixed route are divided into segments according to the position of bus stops, and then a segment-based stochastic driving condition model based on Markov chain is built. With the segment-based stochastic model obtained, the control set for real-time implemented equivalent consumption minimization strategy can be achieved by solving the offline stochastic dynamic programming problem. Results of stochastic dynamic programming are converted into a 3-dimensional lookup table of parameters for online implemented equivalent consumption minimization strategy. The proposed strategy is verified by both simulation and hardware-in-loop test of real-world driving cycle on an urban bus route. Simulation results show that the proposed method outperforms both the well-tuned equivalent consumption minimization strategy and the rule-based strategy in terms of fuel economy, and even proved to be close to the optimal result obtained by dynamic programming. Furthermore, the practical application potential of the proposed control method was proved by hardware-in-loop test.

Keywords: Plug-in hybrid electric bus; Energy management; Markov chain; Stochastic dynamic programming; Equivalent consumption minimization strategy (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:115:y:2016:i:p1:p:1259-1271

DOI: 10.1016/j.energy.2016.09.056

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