SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture
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The convolutional layer structure of this method accommodates variable-length input data and produces SOC estimation of corresponding lengths.
“The convolutional layer structure of this method accommodates variable-length input data and produces SOC estimation of corresponding lengths.”
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 [117]
Among CNN-EDs models such as U-Net, DeepLab v3+, and Mask R-CNN, only U-Net has been used for SOC estimation.
“Among CNN-EDs models such as U-Net, DeepLab v3+, and Mask R-CNN, only U-Net has been used for SOC estimation[117].”
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 [117]
Fan et al. was the only one to apply the U-Net architecture, originally designed for image segmentation, to SOC estimation.
“The work by Fan et al. was the only one to apply the U-Net architecture, originally designed for image segmentation to SOC estimation, as demonstrated in Figure 7B[117].”
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 [117]
Cites
Nonlinear total variation based noise removal algorithms
Rudin, L., Osher, S., & Fatemi, E. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 17,089 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977 · see citing works
How, D. N. T., Hannan, M. A., Lipu, M. S. H., & Ker, P. J. (2019) · IEEE Access · doi:10.1109/access.2019.2942213 · Cited by 823 · see citing works
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
Charkhgard, M., & Farrokhi, M. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2043035 · Cited by 779 · see citing works
Lee, S., Kim, J., Lee, J.-M., & Cho, B. H. (2008) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.08.103 · Cited by 699 · see citing works
Tran, M.-K., Mathew, M., Janhunen, S., Panchal, S., Raahemifar, K., Fraser, R., & Fowler, M. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103252 · Cited by 564 · 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
Review of Battery Management Systems (BMS) Development and Industrial Standards
Gabbar, H. A., Othman, M. I. A., & Abdussami, M. R. (2021) · Technologies · doi:10.3390/technologies9020028 · Cited by 484 · 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
Panchal, S., Khasow, R., Dinçer, İ., et al. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.05.010 · Cited by 399 · see citing works
Meng, J., Luo, G., Ricco, M., Swierczynski, M. J., Stroe, D., & Teodorescu, R. (2018) · Applied Sciences · doi:10.3390/app8050659 · Cited by 371 · see citing works
Tian, 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
Li, D., Zhang, Z., Liu, P., Wang, Z., & Zhang, L. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3008194 · Cited by 348 · 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
Rahman, M. A., Anwar, S., & Izadian, A. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.12.083 · Cited by 291 · see citing works
A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries
Song, Y., Liu, D., Liao, H., & Peng, Y. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114408 · Cited by 266 · see citing works
Hannan, M. A., Lipu, M. S. H., Hussain, A., Ker, P. J., Mahlia, T. M. I., Mansor, M., Ayob, A., Saad, M. H. M., & Dong, Z. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-61464-7 · Cited by 244 · see citing works
State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network
Yang, F., Song, X., Xu, F., & Tsui, K.-L. (2019) · IEEE Access · doi:10.1109/access.2019.2912803 · Cited by 234 · see citing works
Xu, L., Wang, J., & Chen, Q. (2011) · Energy Conversion and Management · doi:10.1016/j.enconman.2011.06.003 · Cited by 226 · see citing works
Panasonic 18650PF Li-ion Battery Data
Kollmeyer, P. J. (2018) · Data Archiving and Networked Services (DANS) · doi:10.17632/wykht8y7tg.1 · Cited by 217 · see citing works
Chen, X., Shen, W., Dai, M., Cao, Z., Jin, J., & Kapoor, A. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2427659 · Cited by 207 · see citing works
Qian, C., Xu, B., Chang, L., Sun, B., Feng, Q., Yang, D., Ren, Y., & Wang, Z. (2021) · Energy · doi:10.1016/j.energy.2021.120333 · Cited by 193 · see citing works
Online Health Diagnosis of Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network
Khaleghi, S., Karimi, D., Beheshti, S. H., Hosen, M. S., Behi, H., Berecibar, M., & Van Mierlo, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116159 · Cited by 161 · see citing works
State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting
Yang, N., Zhang, X., & Li, G. (2014) · Electrochimica Acta · doi:10.1016/j.electacta.2014.11.011 · Cited by 147 · see citing works
Kim, M., Chun, H., Kim, J., et al. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113644 · Cited by 134 · see citing works
Chen, Z., Zhao, H., Shu, X., Zhang, Y., Shen, J., & Liu, Y. (2021) · Energy · doi:10.1016/j.energy.2021.120630 · Cited by 118 · see citing works
Guo, Y., Yang, Z., Liu, K., Zhang, Y., & Feng, W. (2020) · Energy · doi:10.1016/j.energy.2020.119529 · Cited by 100 · see citing works
The State and Parameter Estimation of an Li-Ion Battery Using a New OCV-SOC Concept
Lee, S. J., Kim, J. H., Lee, J. M., & Cho, B. (2007) · IEEE Power Electronics Specialists Conference (PESC) · doi:10.1109/pesc.2007.4342462 · Cited by 94 · see citing works
Purohit, K., Srivastava, S., Nookala, V., Joshi, V., Shah, P., Sekhar, R., Panchal, S., Fowler, M. W., Fraser, R. A., Tran, M., et al. (2021) · Applied System Innovation · doi:10.3390/asi4040078 · Cited by 91 · see citing works
Tran, M., Cunanan, C., Panchal, S., Fraser, R. A., & Fowler, M. W. (2021) · Processes · doi:10.3390/pr9122263 · Cited by 91 · see citing works
An, F., Jiang, J., Zhang, W., Zhang, C., & Fan, X. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3125194 · Cited by 61 · see citing works
Self-citationChen, L., Wang, H., Chen, J., An, J., Ji, B., Lyu, Z., Cao, W., & Pan, H. (2020) · International Journal of Energy Research · doi:10.1002/er.5464 · Cited by 49 · see citing works
State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network
Zhang, C., Chen, S.-R., Gao, H., Xu, K.-J., & Yang, M.-Y. (2018) · Batteries · doi:10.3390/batteries4040069 · Cited by 43 · see citing works
Self-citation
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Yu, H., Zhang, L., Wang, W., Li, S., Chen, S., Yang, S., Li, J., & Liu, X. (2023) · Energy · doi:10.1016/j.energy.2023.127846 · Cited by 96 · see citing works
Yao, J., & Kowal, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100585 · Cited by 30 · see citing works
Gu, C., Ni, L., Tian, X., & Fan, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116465 · Cited by 24 · see citing works
Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works
Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works
- Authors
- Fan, X., Zhang, W., Zhang, C., Chen, A., & An, F.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Zhang, Weige
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 256
- Pages
- 124612
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W4283385419
- Cited by
- 180
- References
- 35
- Citations by year
- 2026: 32 · 2025: 53 · 2024: 47 · 2023: 38 · 2022: 10
- FWCI
- 10.09
- 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 (51977007)
- Natural Science Foundation of Beijing Municipality (3212033)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- state of charge estimation, lithium-ion batteries, U-Net, convolutional neural networks, edge effects, total variation loss, temperature variation, electric vehicles
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