The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach
Abstract
Abstract State of charge (SOC) estimation constitutes a critical task of battery management systems. Conventional SOC estimation methods designed for dynamic profiles have difficulties in estimating SOC for LiFePO4 batteries due to their flat open circuit voltage characteristics in the middle range of SOC. In this study, a deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input. This method enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range. Thus, it can be used to calibrate the SOC estimation for the Ampere-hour counting method. We also demonstrate that by incorporating the DNN into a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved. In the case of significant disturbances, the method still maintains a root mean square error of 0.385%. Moreover, the trained DNN can quickly adapt to various scenarios, including different ageing states and battery types charged at different rates, thanks to the transfer learning nature. Compared with developing a new DNN, transfer learning can provide more accurate estimation results at less training costs. By only fine-tuning one layer of the pre-trained DNN, the root mean square error can be less than 3.146% and 2.315% for aged batteries and different battery types, respectively. When more layers are fine-tuned, superior performance can be achieved.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Lithium-ion battery fast charging: A review
Tomaszewska, A., Chu, Z., Feng, X., O'Kane, S., Liu, X., Chen, J., Ji, C., Endler, E., Li, R., Liu, L., et al. (2019) · eTransportation · doi:10.1016/j.etran.2019.100011 · Cited by 1,592
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
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
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.12.051 · Cited by 879
Predicting the state of charge and health of batteries using data-driven machine learning
Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793
Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.010 · Cited by 574
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
Cited by
Review on the Battery Model and SOC Estimation Method
Zhou, W., Zheng, Y., Pan, Z., & Lu, Q. (2021) · Processes · doi:10.3390/pr9091685 · Cited by 394
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
Review of battery state estimation methods for electric vehicles - Part I: SOC estimation
Demirci, O., Taskin, S., Schaltz, E., & Acar Demirci, B. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111435 · Cited by 312
Manoharan, A., Begam, K. M., Aparow, V. R., & Sooriamoorthy, D. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105384 · Cited by 293
Yu, Q., Huang, Y., Tang, A., Wang, C., & Shen, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3252164 · Cited by 213
Self-citation
Metadata
- Authors
- Tian, J., Xiong, R., Shen, W., & Lu, J.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 291
- Pages
- 116812
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3139303317
- Cited by
- 352
- References
- 11
- FWCI
- 18.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tian, J., Xiong, R., Shen, W., & Lu, J. (2021). State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach. Applied Energy. https://doi.org/10.1016/j.apenergy.2021.116812
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