Actor-Critic TD3-based Deep Reinforcement Learning for Energy Management Strategy of HEV
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Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In the last decade, deep reinforcement learning (DRL) algorithms have been employed in the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs). Investigation of the real-time applicability of DRL algorithms as an EMS is critical in terms of training time, fuel savings, and state-of-charge (SOC) sustainability. To this end, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm that is an improved version of the deep deterministic policy gradient (DDPG) algorithm for HEV fuel savings. Compared to the existing Q-learning-based reinforcement learning and the deep Q-network-based and DDPG-based deep reinforcement algorithms, the proposed TD3 provides stable training efficiency, promising fuel economy, and a lower variation range of SOC charge sustainability under various drive cycles. © 2023 IEEE.
Açıklama
5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 8 June 2023 through 10 June 2023 -- Istanbul -- 190025
Anahtar Kelimeler
actor-critic network, Deep reinforcement learning, energy management, hybrid electric vehicles, TD3 algoritm
Kaynak
HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
