The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks
Abstract
Abstract The state of charge (SOC) of a battery indicates its useable capacity. In the case of lithium-ion batteries, an accurate estimate improves their performance. With the recent tendency in the increased use of lithium-ion batteries in electric vehicles, the estimation of SOC has become even a more critical issue than before. In this paper, the combination of an Autoencoder neural network and a Long Short-Term Memory (LSTM) neural network is proposed for the estimation of the SOC of a battery with high precision. The Dynamic Stress Test (DST) drive cycle and the Federal Urban Driving Schedule (FUDS) drive cycle datasets are used to test the proposed algorithm at three different temperatures. To reveal the performance of the proposed method, the results are compared with several other methods from the literature. It is observed that the SOC estimation by the proposed method yields to a significantly better accuracy at all three temperatures.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
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
Medical Image Denoising Using Convolutional Denoising Autoencoders
Gondara, L. (2016) · 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw.2016.0041 · Cited by 737
A 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
Selvaraj, V., & Vairavasundaram, I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108777 · Cited by 220
Metadata
- Authors
- Fasahat, M., & Manthouri, M.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 469
- Pages
- 228375
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3035704836
- Cited by
- 170
- References
- 5
- FWCI
- 8.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fasahat, M., & Manthouri, M. (2020). State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2020.228375
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