State-of-Health Estimation of Lithium-Ion Batteries with Attention-Based Deep Learning
- Title
- State-of-Health Estimation of Lithium-Ion Batteries with Attention-Based Deep Learning
- Author
- 조인휘
- Keywords
- Lithium-ion battery; State of health; Gated recurrent unit; Attention
- Issue Date
- 2021-01
- Publisher
- Springer Science + Business Media
- Citation
- Advances in Intelligent Systems and Computing, v. 1295, page. 322-331
- Abstract
- Lithium-ion batteries are most commonly used in electric vehicles (EVs). The battery management system (BMS) assists in utilizing the energy stored in the battery more effectively through various functions. State of health (SOH) estimation is an essential function in a BMS. The accurate estimation of SOH can be used to calculate the remaining lifetime and ensure the reliability of batteries. In this paper, we propose a data-driven deep learning method that combines Gate Recurrent Unit (GRU) and attention mechanism for SOH estimation of lithium-ion batteries. Real-life datasets of batteries from NASA are used for evaluating our proposed model. The experimental results show that the proposed deep learning model has higher accuracy than conventional data-driven models.
- URI
- https://link.springer.com/chapter/10.1007/978-3-030-63319-6_28https://repository.hanyang.ac.kr/handle/20.500.11754/175517
- ISSN
- 2194-5357; 2194-5365
- DOI
- 10.1007/978-3-030-63319-6_28
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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