scholarly journals Energy Management Control of Fuel Cell Electric Bus Basing on Fuzzy Rule

2021 ◽  
Vol 2002 (1) ◽  
pp. 012073
Author(s):  
Renguang Wang ◽  
Dong Hao ◽  
Yanyi Zhang ◽  
Xiangfei Meng
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Fuzzy Rule ◽  
Management Control ◽  
Electric Bus

Application of State Transition Energy Management Control Algorithm in Fuel Cell

2021 ◽  
Author(s):  
Zheng Pan ◽  
Qihong Xiao ◽  
Yangliang Chen
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
State Transition ◽  
Motor Vehicle ◽  
Transition Energy ◽  
Simulation Software ◽  
Management Control ◽  
Hydrogen Consumption ◽  
Management Algorithm ◽  
Battery Energy

Dynamic programming algorithms are widely used in motor vehicle fuel cells, and can help battery energy management control to perform error analysis. The paper designs the decision-making process of fuel cell charge and discharge management based on the state transition energy management algorithm, which is used to analyse the cumulative causes of errors and the corresponding results. The article uses simulation software to simulate the algorithm proposed in this paper, and finds that the algorithm is an energy management optimization decision, and the error of the hydrogen consumption obtained by the algorithm relative to the theoretical optimal hydrogen consumption is less than 0.25%.

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.

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