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Open AccessArticle

Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space

School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252059, China
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Processes 2019, 7(10), 672; https://doi.org/10.3390/pr7100672
Received: 26 August 2019 / Revised: 14 September 2019 / Accepted: 16 September 2019 / Published: 28 September 2019
Tabular Q-learning (QL) can be easily implemented into a controller to realize self-learning energy management control of a plug-in hybrid electric bus (PHEB). However, the “curse of dimensionality” problem is difficult to avoid, as the design space is huge. This paper proposes a QL-PMP algorithm (QL and Pontryagin minimum principle (PMP)) to address the problem. The main novelty is that the difference between the feedback SOC (state of charge) and the reference SOC is exclusively designed as state, and then a limited state space with 50 rows and 25 columns is proposed. The off-line training process shows that the limited state space is reasonable and adequate for the self-learning; the Hardware-in-Loop (HIL) simulation results show that the QL-PMP strategy can be implemented into a controller to realize real-time control, and can on average improve the fuel economy by 20.42%, compared to the charge depleting–charge sustaining (CDCS) strategy. View Full-Text
Keywords: plug-in hybrid electric bus; energy management; Q-learning; limited state space; Hardware-in-Loop (HIL) simulation plug-in hybrid electric bus; energy management; Q-learning; limited state space; Hardware-in-Loop (HIL) simulation
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Guo, H.; Du, S.; Zhao, F.; Cui, Q.; Ren, W. Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space. Processes 2019, 7, 672.

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