Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management. (March 2023)

• Record Type: Journal Article
• Authors: Ye, Yiming; Zhang, Jiangfeng; Pilla, Srikanth; Rao, Apparao M.; Xu, Bin
• Is Part Of: Journal of energy storage. Volume 59(2023)
• Publication Date: 2023-03
• Subjects: Battery degradation -- Li-S battery -- Energy management strategy -- Plug-in hybrid vehicle; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.106546
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: The continuous increase in vehicle ownership has caused overall energy consumption to increase rapidly. Developing new energy vehicle technologies and improving energy utilization efficiency are significant in saving energy. Plug-in hybrid electric vehicles (PHEVs) present a practical solution to the arising energy shortage concerns. However, existing battery technologies restrict PHEV application as the most popular lithium-ion battery has a relatively high capital cost and degradation during service time. This paper studies the application of a new type of lithium‑sulfur (LiS) battery with bilateral solid electrolyte interphases in the PHEV. Compared with metals such as cobalt and nickel used in conventional lithium-ion batteries, sulfur utilized in LiS is cheaper and easier to manufacture. The high energy density of the new LiS battery also provides a longer range for PHEVs. In this paper, a PHEV propulsion system model is introduced, which includes vehicle dynamics, engine, electric motor, and LiS battery models. Dynamic programming is formulated as a benchmark energy management strategy to reduce energy consumption. Besides the offline global optimal benchmark from dynamic programming, the real-time performance of the LiS battery is evaluated by Q-learning and rule-based strategies. For a more comprehensive validation, both light-duty vehicles and heavy-duty vehicles are considered. Compared with lithium-ion batteries, the new LiS battery reduces the fuel consumption by up to 14.63 % and battery degradation by up to 82.37 %. Highlights: A new lithium-sulfur battery is implemented in plug-in hybrid electric vehicles. Reinforcement learning is applied in the vehicle energy management strategy. The results are validated by case studies on light-duty and heavy-duty vehicles with different energy management strategies.