An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
- Title
- An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
- Author
- 조인휘
- Keywords
- Lithium-ion battery; State of charge; Long Short-Term Memory; Gated Recurrent Unit; Encoder-Decoder
- Issue Date
- 2020-08
- Publisher
- Springer Science + Business Media
- Citation
- CSOC 2020. Advances in Intelligent Systems and Computing, vol 1224, Intelligent Algorithms in Software Engineering, page. 178-188
- Abstract
- A lithium-ion battery is rechargeable and is widely used in portable devices and electric vehicles (EVs). State-of-Charge (SOC) estimation is vital function in a battery management system (BMS) since high-accuracy SOC estimation ensures reliability and safety of electronic products using lithium-ion batteries. Unlike traditional SOC estimation methods deep learning based methods are data-driven methods that do not rely much on battery quality. In this paper, an Encoder-Decoder model which can compress sequential inputs into a vector used for decoding sequential outputs is proposed to estimate the SOC based on measured voltage and current. Compared with conventional recurrent networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the proposed model yields better accuracy of estimation. Models are validated on lithium-ion battery data set with dynamical stress testing (DST), Federal Urban Driving Schedule (FUDS), and US06 highway schedule profiles.
- URI
- https://link.springer.com/chapter/10.1007/978-3-030-51965-0_15https://repository.hanyang.ac.kr/handle/20.500.11754/170130
- ISBN
- 978-3-030-51964-3; 978-3-030-51965-0
- DOI
- 10.1007/978-3-030-51965-0_15
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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