Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Using Deep Reinforcement Learning With Action Trimming

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the...

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Published inIEEE transactions on vehicular technology Vol. 71; no. 7; pp. 7171 - 7185
Main Authors Fu, Zhumu, Wang, Haocong, Tao, Fazhan, Ji, Baofeng, Dong, Yongsheng, Song, Shuzhong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Adaptive filters
Consumption
Dater driven
Deep learning
deep reinforcement learning
Dynamic programming
Electric vehicles
Energy efficiency
Energy management
energy management strategy
fuel cell hybrid electric vehicle
Fuel cells
Fuel consumption
Fuel economy
Fuzzy control
heuristic technique
Hybrid electric vehicles
Hydrogen
Life span
Machine learning
Optimization
Power consumption
Power management
Power sources
Real-time systems
Structural hierarchy
Supercapacitors
Topology
Trimming
Vehicle dynamics
Voltage
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Abstract As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>.
AbstractList As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>.
As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7[Formula Omitted].
Author Dong, Yongsheng
Tao, Fazhan
Ji, Baofeng
Song, Shuzhong
Wang, Haocong
Fu, Zhumu
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Snippet As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different...
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SubjectTerms Adaptive control
Adaptive filters
Consumption
Dater driven
Deep learning
deep reinforcement learning
Dynamic programming
Electric vehicles
Energy efficiency
Energy management
energy management strategy
fuel cell hybrid electric vehicle
Fuel cells
Fuel consumption
Fuel economy
Fuzzy control
heuristic technique
Hybrid electric vehicles
Hydrogen
Life span
Machine learning
Optimization
Power consumption
Power management
Power sources
Real-time systems
Structural hierarchy
Supercapacitors
Topology
Trimming
Vehicle dynamics
Voltage
Title Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Using Deep Reinforcement Learning With Action Trimming
URI https://ieeexplore.ieee.org/document/9762022
https://www.proquest.com/docview/2689808403
Volume 71
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