Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

Dong, Hongquan; Zhao, Lingying; Zhou, Hao; Li, Haolin

doi:10.3390/pr11092689

Open AccessArticle

Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

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Processes 2023, 11(9), 2689; https://doi.org/10.3390/pr11092689

Submission received: 13 August 2023 / Revised: 30 August 2023 / Accepted: 4 September 2023 / Published: 7 September 2023

(This article belongs to the Section Energy Systems)

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Abstract

With the advantages of non-pollution and energy-saving, hydrogen fuel cell hybrid vehicles (HFCHVs) are regarded as one of the potential traveling ways in the future. The energy management of FCHVs has a huge energy-efficient potential which is combined with the Internet of Things (IOT) and auto-driving technologies. In this paper, a hierarchical joint optimization method that combines deep deterministic policy gradient and dynamic planning (DDPG-DP) for speed planning and energy management of the HFCHV is proposed for urban road driving scenarios. The results demonstrate that when the HFCHV is operating in driving scenario 1, the traveling efficiency of the DDPG-DP algorithm is 17.8% higher than that of the IDM-DP algorithm, and the hydrogen fuel consumption is reduced by 2.7%. In contrast, the difference in the traveling efficiency and fuel economy is small among the three algorithms in driving scenario 2, the number of idling/stop situations of the DDPG-DP algorithm is reduced compared with that of the IDM-DP algorithm. This will support further research for multi-objective eco-driving optimization of fuel cell hybrid vehicles.

Keywords: hydrogen fuel cell hybrid vehicle; hierarchical joint optimization; traveling efficiency; fuel economy; urban roads driving scenario

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MDPI and ACS Style

Dong, H.; Zhao, L.; Zhou, H.; Li, H. Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections. Processes 2023, 11, 2689. https://doi.org/10.3390/pr11092689

AMA Style

Dong H, Zhao L, Zhou H, Li H. Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections. Processes. 2023; 11(9):2689. https://doi.org/10.3390/pr11092689

Chicago/Turabian Style

Dong, Hongquan, Lingying Zhao, Hao Zhou, and Haolin Li. 2023. "Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections" Processes 11, no. 9: 2689. https://doi.org/10.3390/pr11092689

APA Style

Dong, H., Zhao, L., Zhou, H., & Li, H. (2023). Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections. Processes, 11(9), 2689. https://doi.org/10.3390/pr11092689

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Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

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