scholarly journals Self-Learning Enhanced Energy Management for Plug-in Hybrid Electric Bus With a Target Preview Based SOC Plan Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 103153-103166 ◽  
Author(s):  
Hong-Qiang Guo ◽  
Guoliang Wei ◽  
Fengbo Wang ◽  
Chong Wang ◽  
Shangye Du
Keyword(s):  
Energy Management ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
Self Learning

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

2020 ◽  
Vol 44 (7) ◽  
pp. 5659-5674
Author(s):  
Hongqiang Guo ◽  
Fengrui Zhao ◽  
Hongliang Guo ◽  
Qinghu Cui ◽  
Erlei Du ◽  
...  
Keyword(s):  
Energy Management ◽  
Generalization Performance ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
Self Learning

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

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 672 ◽  
Author(s):  
Hongqiang Guo ◽  
Shangye Du ◽  
Fengrui Zhao ◽  
Qinghu Cui ◽  
Weilong Ren
Keyword(s):  
State Space ◽  
Energy Management ◽  
Minimum Principle ◽  
Management Control ◽  
Real Time Control ◽  
Pontryagin Minimum Principle ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
Self Learning

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.

Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

2016 ◽  
Vol 65 (6) ◽  
pp. 4459-4470 ◽  
Author(s):  
Liang Li ◽  
Bingjie Yan ◽  
Chao Yang ◽  
Yuanbo Zhang ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Energy Management ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric
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