The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
Abstract
For lithium iron phosphate battery, the ambient temperature and the flat open circuit voltage - state-of-charge (SOC) curve are two of the major issues that influence the accuracy of SOC estimation, which is critical for driving range estimation of electric vehicles and optimal charge control of batteries. To address these problems, this paper proposes a long short-term memory (LSTM) – recurrent neural network to model the sophisticated battery behaviors under varying temperatures and estimate battery SOC from voltage, current, and temperature variables. An unscented Kalman filter (UKF) is incorporated to filter out the noises and further reduce the estimation errors. The proposed method is evaluated using data collected from the dynamic stress test, federal urban driving schedule, and US06 test. Experimental results show that the proposed method can well learn the influence of ambient temperature and estimate battery SOC under varying temperatures from 0°C to 50°C, with root mean square errors less than 1.1% and mean average errors less than 1%. Moreover, the proposed method also provides a satisfying SOC estimation under other temperatures which have no data trained before.
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Cites
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Learning long-term dependencies with gradient descent is difficult
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A review on the key issues for lithium-ion battery management in electric vehicles
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Bayesian Filtering and Smoothing
Särkkä, S. (2013) · doi:10.1017/cbo9781139344203 · Cited by 1,784
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Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
Yang, F., Li, W., Li, C., & Miao, Q. (2019) · Energy · doi:10.1016/j.energy.2019.03.059 · Cited by 543
Self-citationYang, F., Wang, D., Zhao, Y., Tsui, K.-L., & Bae, S. J. (2018) · Energy · doi:10.1016/j.energy.2017.12.144 · Cited by 349
Self-citationA new neural network model for the state-of-charge estimation in the battery degradation process
Kang, L., Zhao, X., & Ma, J. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.01.066 · Cited by 325
Cited by
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Cui, Z., Wang, L., Li, Q., & Wang, K. (2022) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 384
A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
Ren, X., Liu, S., Yu, X., & Dong, X. (2021) · Energy · doi:10.1016/j.energy.2021.121236 · Cited by 375
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q. (2023) · Energy · doi:10.1016/j.energy.2022.125375 · Cited by 362
Cheng, G., Wang, X., & He, Y. (2021) · Energy · doi:10.1016/j.energy.2021.121022 · Cited by 258
Metadata
- Authors
- Yang, F., Zhang, S., Li, W., & Miao, Q.
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 201
- Pages
- 117664
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3019324730
- Cited by
- 449
- References
- 12
- FWCI
- 18.45
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
