Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle
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| Title: | Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle |
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| Authors: | Tao Li, Wei Cui, Naxin Cui |
| Source: | World Electric Vehicle Journal, Vol 13, Iss 193, p 193 (2022) |
| Publisher Information: | MDPI AG |
| Publication Year: | 2022 |
| Collection: | Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: | hybrid electric vehicle, energy management strategy, deep reinforcement learning, SAC algorithm, automating entropy adjustment, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Transportation engineering, TA1001-1280 |
| Description: | Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value. |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | https://www.mdpi.com/2032-6653/13/10/193; https://doaj.org/toc/2032-6653; https://doaj.org/article/abfce9c8e64649ecabae033554a88198 |
| DOI: | 10.3390/wevj13100193 |
| Availability: | https://doi.org/10.3390/wevj13100193 https://doaj.org/article/abfce9c8e64649ecabae033554a88198 |
| Accession Number: | edsbas.1362B985 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value. |
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| DOI: | 10.3390/wevj13100193 |