An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
- Authors
- Cui, Shengmin; Yong, Xiaowa; Kim, Sanghwan; Hong, Seokjoon; Joe, Inwhee
- Issue Date
- Jul-2020
- Publisher
- Springer
- Keywords
- Encoder-Decoder; Gated Recurrent Unit; Lithium-ion battery; Long Short-Term Memory; State of charge
- Citation
- Advances in Intelligent Systems and Computing, v.1224 AISC, pp.178 - 188
- Indexed
- SCOPUS
- Journal Title
- Advances in Intelligent Systems and Computing
- Volume
- 1224 AISC
- Start Page
- 178
- End Page
- 188
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145399
- DOI
- 10.1007/978-3-030-51965-0_15
- ISSN
- 2194-5357
- 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.
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