Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique
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Jiang et al.'s method achieved comparable performance to longer input windows with shorter ones, though the accuracy improvement was minimal.
“While the improvement in accuracy was minimal, this method achieved comparable performance to longer input windows with shorter ones.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [89]
Jiang et al. replaced the temperature variable with battery stress as input for Stack-LSTM, resulting in a mechanics-based SOC estimation approach.
“Jiang et al. explored the relationship between battery stress and SOC, replacing the temperature variable with battery stress as input for Stack-LSTM, resulting in a mechanics-based SOC estimation approach[89].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [89]
Cites
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 · see citing works
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394 · see citing works
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 · see citing works
State estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857 · see citing works
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 · see citing works
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 · see citing works
Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.094 · Cited by 493 · see citing works
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
Yang, F., Zhang, S., Li, W., & Miao, Q. (2020) · Energy · doi:10.1016/j.energy.2020.117664 · Cited by 452 · see citing works
A review on modeling of electro-chemo-mechanics in lithium-ion batteries
Zhao, Y., Stein, P., Bai, Y., Al-Siraj, M., Yang, Y., & Xu, B. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.12.011 · Cited by 432 · see citing works
Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W. (2020) · Energy · doi:10.1016/j.energy.2020.118000 · Cited by 423 · see citing works
Dai, H., Jiang, B., Hu, X., Lin, X., Wei, X., & PECHT, M. G. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110480 · Cited by 408 · see citing works
Self-citationA 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 · see citing works
Jiang, B., Zhu, J., Wang, X., Wei, X., Shang, W., & Dai, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119502 · Cited by 375 · see citing works
Self-citationTian, Y., Lai, R., Li, X., Xiang, L., & Tian, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114789 · Cited by 357 · see citing works
Tian, J., Xiong, R., Shen, W., & Lu, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116812 · Cited by 356 · see citing works
Wei, Z., Zhao, J., He, H., Ding, G., Cui, H., & Liu, L. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.229462 · Cited by 352 · see citing works
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach
How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.3004294 · Cited by 337 · see citing works
Xiong, R., Yu, Q., Wang, L. Y., & Lin, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.136 · Cited by 293 · see citing works
Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs
Feng, F., Hu, X., Hu, L., Hu, F., Li, Y., & Zhang, L. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.05.042 · Cited by 282 · see citing works
Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning
Tian, J., Xiong, R., Lu, J., Chen, C., & Shen, W. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.06.007 · Cited by 280 · see citing works
State of health and charge measurements in lithium-ion batteries using mechanical stress
Cannarella, J., & Arnold, C. B. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.003 · Cited by 278 · see citing works
Chen, C., Xiong, R., Yang, R., Shen, W., & Sun, F. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.06.273 · Cited by 255 · see citing works
Sensing as the key to battery lifetime and sustainability
Huang, J., Boles, S. T., & Tarascon, J. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00859-y · Cited by 218 · see citing works
A Review of Second-Life Lithium-Ion Batteries for Stationary Energy Storage Applications
Hu, X., Deng, X., Feng, W., Deng, Z., Lin, X., Teodorescu, R., & PECHT, M. G. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3175614 · Cited by 208 · see citing works
An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management
Hu, X., Jiang, H., Feng, F., & Liu, B. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114019 · Cited by 186 · see citing works
Fasahat, M., & Manthouri, M. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228375 · Cited by 174 · see citing works
Guo, S., & Ma, L. (2023) · Energy · doi:10.1016/j.energy.2022.125872 · Cited by 162 · see citing works
Ganguli, A., Saha, B., Raghavan, A., Kiesel, P., Arakaki, K., Schuh, A., Schwartz, J., Hegyi, A., Sommer, L. W., Lochbaum, A., et al. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.11.103 · Cited by 155 · see citing works
Ye, M., Guo, H., Xiong, R., & Yu, Q. (2017) · Energy · doi:10.1016/j.energy.2017.12.061 · Cited by 152 · see citing works
Xiao, F., Li, C., Fan, Y., Yang, G., & Tang, X. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106369 · Cited by 146 · see citing works
Battery capacity fading estimation using a force-based incremental capacity analysis
Samad, N. A., Kim, Y., Siegel, J. B., & Stefanopoulou, A. G. (2016) · Journal of The Electrochemical Society · doi:10.1149/2.0511608jes · Cited by 130 · see citing works
Mechanical methods for state determination of Lithium-Ion secondary batteries: A review
Popp, H., Koller, M., Jahn, M., & Bergmann, A. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101859 · Cited by 130 · see citing works
Zhu, R., Duan, B., Zhang, J., Zhang, Q., & Zhang, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115494 · Cited by 123 · see citing works
Jiang, B., Dai, H., Wei, X., & Xu, T. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113619 · Cited by 119 · see citing works
Self-citationMa, B., Yang, S., Zhang, L., Wang, W., Chen, S., Yang, X., Xie, H., Yu, H., Wang, H., & Liu, X. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.232030 · Cited by 117 · see citing works
State of charge estimation for lithium-ion pouch batteries based on stress measurement
Dai, H., Yu, C., Wei, X., & Sun, Z. (2017) · Energy · doi:10.1016/j.energy.2017.04.099 · Cited by 112 · see citing works
Self-citationJiang, B., Zhu, Y., Zhu, J., Wei, X., & Dai, H. (2022) · Energy · doi:10.1016/j.energy.2022.125802 · Cited by 111 · see citing works
Self-citationXiong, R., Wang, J., Shen, W., Tian, J., & Mu, H. (2021) · Engineering · doi:10.1016/j.eng.2020.10.022 · Cited by 98 · see citing works
A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine
Li, J., Ye, M., Wei, M., Xu, X., & Jiao, S. (2020) · IEEE Access · doi:10.1109/access.2020.3033451 · Cited by 92 · see citing works
Ee, Y.-J., Tey, K. S., Lim, K., Shrivastava, P., Adnan, S. B. R. S., & Ahmad, H. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102704 · Cited by 89 · see citing works
Fan, G. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228153 · Cited by 87 · see citing works
Peng, N., Zhang, S., & Xiongwen, Z. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102559 · Cited by 85 · see citing works
Wang, F., Zhao, Z., Ren, J., Zhai, Z., Wang, S., & Chen, X. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.230975 · Cited by 85 · see citing works
Peng, J., Jia, S., Yang, S., Kang, X., Yu, H., & Yang, Y. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104950 · Cited by 78 · see citing works
Tian, J., Xiong, R., Shen, W., & Wang, J. (2020) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-020-00467-x · Cited by 56 · see citing works
Movahedi, H., Tian, N., Fang, H., & Rajamani, R. (2021) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3068982 · Cited by 55 · see citing works
Bian, C., Yang, S., Liu, J., & Zio, E. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108401 · Cited by 55 · see citing works
Lithium-Sulfur Cell State of Charge Estimation Using a Classification Technique
Shateri, N., Shi, Z., Auger, D. J., & Fotouhi, A. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3045213 · Cited by 46 · see citing works
A novel state of charge estimation method for lithium-ion batteries based on bias compensation
Ouyang, T., Xu, P., Chen, J., Su, Z., Huang, G., & Chen, N. (2021) · Energy · doi:10.1016/j.energy.2021.120348 · Cited by 43 · see citing works
Ma, W., Lei, Y., Wang, X., & Chen, B. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.02.019 · Cited by 37 · see citing works
Self-citationModeling of thermo-mechanical stresses in Li-ion battery
Valentin, O., Thivel, P., Kareemulla, T., Cadiou, F., & Bultel, Y. (2017) · Journal of Energy Storage · doi:10.1016/j.est.2017.07.018 · Cited by 36 · see citing works
Li, G., Liu, C., Wang, E., & Wang, L. (2021) · Automotive Innovation · doi:10.1007/s42154-021-00134-4 · Cited by 33 · see citing works
Quan, R., Liu, P., Li, Z., Li, Y., Chang, Y., & Yan, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106263 · Cited by 26 · see citing works
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- Authors
- Jiang, B., Tao, S., Wang, X., Zhu, J., Wei, X., & Dai, H.
- Affiliations
- Tongji University (CN)
- Corresponding author
- Wang, Xueyuan
- Dai, Haifeng
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 278
- Pages
- 127890
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W4377101611
- Cited by
- 70
- References
- 53
- Citations by year
- 2026: 11 · 2025: 29 · 2024: 23 · 2023: 7
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of charge estimation, long short-term memory neural network, battery management systems, battery datasets, measurement noise robustness
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