Uncorrelated Sparse Autoencoder With Long Short-Term Memory for State-of-Charge Estimations in Lithium-Ion Battery Cells
Abstract
For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant to long-term SOC estimation due to complex battery cell characteristics such as aging. In this paper, we propose the Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses the challenging task of long-term SOC estimation given a limited initial history of a cell’s charge-discharge behavior. USAL uses a multi-task learning strategy to harness the advantages of sparse autoencoders and Long Short-term Memory (LSTM) networks by enforcing correlation penalties. The USAL simultaneously learns to (i) generate a latent space of informative SOC encodings from commonly measured cell characteristics, (ii) penalize for high multicollinearity between encodings, and (iii) identify non-trivial long and short temporal correlations between the encodings using LSTM cells. USAL outperforms benchmarked models in our experiments when trained on five initial charge-discharge cycles across multiple battery cells using three publicly available accelerated aging datasets. Note to Practitioners—This paper proposes USAL, a custom-built deep neural network to address the challenging task of long-term SOC estimations in battery cells. Long-term SOC estimation involves estimating SOC for cycles near End-Of-Life (EOL) given some initial charge-discharge cycles. Three fundamental steps involved in long-term SOC estimations using USAL are (i) exploiting a multi-task learning strategy to learn efficient encodings given limited training data, (ii) penalizing these encodings for high correlations to efficiently transform measured inputs into a space of informative features, and (iii) mapping of aging-related trends to support long-term SOC estimations. USAL is designed to be a data-driven SOC estimation method that is (i) capable of alerting the user to a faulty cell when integrated into a real-life Battery Management System (BMS) and (ii) identifying the relative quality of a battery cell from only a few initial charge-discharge cycles.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Savargaonkar et al. proposed a SOC estimator based on uncorrelated sparse AE with LSTM (USAL).
“Savargaonkar et al. proposed a SOC estimator based on uncorrelated sparse AE with LSTM (USAL)[121].”
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 [121]
Cites
Data-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005 · see citing works
(2011) · Cited by 1,258 · see citing works
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2017) · IEEE Access · doi:10.1109/access.2017.2780258 · Cited by 1,022 · see citing works
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2458702 · Cited by 854 · see citing works
He, H., Xiong, R., Zhang, X., Sun, F., & Fan, J. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2132812 · Cited by 825 · see citing works
Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818 · see citing works
The State of Charge Estimating Methods for Battery: A Review
Chang, W.-Y. (2013) · ISRN Applied Mathematics · doi:10.1155/2013/953792 · Cited by 650 · see citing works
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 · see citing works
He, W., Williard, N., Chen, C., & Pecht, M. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.04.059 · Cited by 539 · see citing works
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
Gu, J., Sun, C., & Zhao, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01502 · Cited by 496 · 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
A review of state-of-charge indication of batteries by means of a.c. impedance measurements
Rodrigues, S., Munichandraiah, N., & Shukla, A. K. (2000) · Journal of Power Sources · doi:10.1016/s0378-7753(99)00351-1 · Cited by 438 · see citing works
A deep learning method for online capacity estimation of lithium-ion batteries
Shen, S., Sadoughi, M., Chen, X., Hong, M., & Hu, C. K. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100817 · Cited by 399 · see citing works
k-Sparse AutoencodersPreprint
Makhzani, A., & Frey, B. J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5663 · Cited by 358 · 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
Bian, C., He, H., & Yang, S. (2020) · Energy · doi:10.1016/j.energy.2019.116538 · Cited by 284 · see citing works
Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression
Sahinoglu, G. O., Pajovic, M., Şahinoğlu, Z., Wang, Y., Orlik, P. V., & Wada, T. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2764869 · Cited by 279 · see citing works
Understanding ageing in Li-ion batteries: a chemical issue
Palacín, M. R. (2018) · Chemical Society Reviews · doi:10.1039/c7cs00889a · Cited by 241 · 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
Life prediction model for grid-connected Li-ion battery energy storage system
Smith, K. A., Saxon, A., Keyser, M. A., Lundstrom, B., Cao, Z., & Roc, A. (2017) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2017.7963578 · Cited by 213 · see citing works
Dong, G., Liao, G., Liu, H., & Kuang, G. (2018) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2018.2853555 · Cited by 191 · see citing works
Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells
Diao, W., Saxena, S., & PECHT, M. G. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.226830 · Cited by 188 · see citing works
Pinaya, W. H. L., Vieira, S., Garcia‐Dias, R., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00011-0 · Cited by 185 · 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
A critical review on self-adaptive Li-ion battery ageing models
Lucu, M., Martinez-Laserna, E., Gandiaga, I., & Camblong, H. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.08.064 · Cited by 166 · see citing works
State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks
Wei, M., Ye, M., Li, J. B., Wang, Q., & Xu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3031340 · Cited by 148 · see citing works
Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles
Hu, X., Sun, F., & Zou, Y. (2013) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2013.01.001 · Cited by 121 · see citing works
Oyewole, I., Chehade, A., & Kim, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118726 · Cited by 116 · see citing works
Self-citationRidge Regression and multicollinearity: An in-depth review
Schreiber-Gregory, D. N. (2018) · Model Assisted Statistics and Applications · doi:10.3233/mas-180446 · Cited by 94 · see citing works
Vidal, C., Kollmeyer, P. J., Chemali, E., & Emadi, A. (2019) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2019.8790543 · Cited by 93 · see citing works
Lucu, M., Martinez-Laserna, E., Gandiaga, I., Liu, K., Camblong, H., Widanage, W. D., & Marco, J. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101410 · Cited by 87 · see citing works
Study on the economic and environmental benefits of different EV powertrain topologies
Wang, B., Xu, M., & Yang, L. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.05.077 · Cited by 60 · see citing works
Sankaran, A., Mayank Vatsa, A., Singh, R., & Majumdar, A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.005 · Cited by 57 · see citing works
Sun, D., Yu, X., Zhang, C., Wang, C., & Huang, R. N. (2020) · International Journal of Energy Research · doi:10.1002/er.5690 · Cited by 47 · see citing works
Conditional Gaussian mixture model for warranty claims forecasting
Chehade, A., Savargaonkar, M., & Krivtsov, V. V. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108180 · Cited by 41 · see citing works
Self-citationMa, L., Wang, Z., Yang, F., Cheng, Y., Lu, C., Tao, L., & Zhou, T. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228691 · Cited by 28 · see citing works
Savargaonkar, M., Chehade, A., & Hussein, A. A.-H. (2022) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2022.3170842 · Cited by 23 · see citing works
Self-citationHannan, M. A., How, D. N. T., Mansor, M., Lipu, M. S. H., Ker, P. J., & Muttaqi, K. M. (2020) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias44978.2020.9334824 · Cited by 20 · see citing works
A Long Short-Term Memory Network for Online State-of-Charge Estimation of Li-ion Battery Cells
Shi, Z., Savargaonkar, M., Chehade, A., & Hussein, A. A.-H. (2020) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec48692.2020.9161487 · Cited by 16 · see citing works
Self-citationChehade, A., & Hussein, A. A.-H. (2019) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2019.8790463 · Cited by 13 · see citing works
Self-citationSavargaonkar, M., & Chehade, A. (2020) · IEEE Transportation Electrification Conference & Expo (ITEC) · doi:10.1109/itec48692.2020.9161464 · Cited by 11 · see citing works
Self-citationA Hybrid Long Short-Term Memory Network for State-of-Charge Estimation of Li-ion Batteries
Oyewole, I., Savargaonkar, M., Chehade, A., & Kim, Y. (2021) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec51675.2021.9490188 · Cited by 8 · see citing works
Self-citationSparse Autoencoded Long Short-Term Memory Network for State-of-Charge Estimations
Savargaonkar, M., Oyewole, I., & Chehade, A. (2021) · IEEE Transportation Electrification Conference & Expo (ITEC) · doi:10.1109/itec51675.2021.9490070 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Tang, A., Huang, Y., Liu, S., Yu, Q., Shen, W., & Xiong, R. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121578 · Cited by 112 · see citing works
Haraz, A., Abualsaud, K., & Massoud, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3486989 · Cited by 80 · see citing works
Yang, S., Wang, J., Dong, K. Y., Dong, X., Wang, K., & Fu, X. (2024) · Energy · doi:10.1016/j.energy.2024.131539 · Cited by 66 · see citing works
Sorouri, H., Oshnoei, A., Che, Y., & Teodorescu, R. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113604 · Cited by 62 · see citing works
Sun, D., Guo, D., Lu, Y., Chen, J., Lu, Y., Han, X., Feng, X., Lu, L., Wang, H., & Ouyang, M. (2024) · Energy & Environmental Science · doi:10.1039/d4ee03063j · Cited by 63 · see citing works
Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach
Pooyandeh, M., & Sohn, I. (2023) · Mathematics · doi:10.3390/math11234865 · Cited by 55 · see citing works
Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study
Massaoudi, M., Abu‐Rub, H., & Ghrayeb, A. (2024) · IEEE Open Journal of Industry Applications · doi:10.1109/ojia.2024.3354899 · Cited by 41 · see citing works
Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation
Hussein, H. M., Esoofally, M., Donekal, A., Rafin, S. M. S. H., & Mohammed, O. A. (2024) · Batteries · doi:10.3390/batteries10030089 · Cited by 42 · see citing works
LiFePO 4 Battery SOC Estimation Under OCV–SOC Curve Error Based on Adaptive Multimodel Kalman Filter
Paizulamu, D., Cheng, L., Xu, H., Zhuang, Y., Qi, N., & Ci, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3558298 · Cited by 26 · see citing works
A decade of machine learning in lithium-ion battery state estimation: a systematic review
Al-Hashimi, Z., Khamis, T., Kouzbary, M. A., Arifin, N., Mokayed, H., & Osman, N. A. A. (2025) · Ionics · doi:10.1007/s11581-024-06049-4 · Cited by 27 · see citing works
Hong, S., Kang, M., Kim, J., & Baek, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108421 · Cited by 22 · see citing works
Haraz, A., Abualsaud, K., & Massoud, A. M. (2025) · IEEE Access · doi:10.1109/access.2025.3539792 · Cited by 21 · 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
The Lithium-Ion Battery Temperature Field Prediction Model Based on CNN-Bi-LSTM-AM
Wang, B., Chen, Z., Zhang, P., Deng, Y., & Li, B. (2025) · Sustainability · doi:10.3390/su17052125 · Cited by 19 · see citing works
Chen, J., Li, K., Liu, W., Yin, C., Zhu, Q., & Tang, H. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115888 · Cited by 15 · see citing works
Selective Deep Autoencoder for Unsupervised Feature Selection
Hassanieh, W., & Chehade, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29123 · Cited by 15 · see citing works
Self-citationImanloozadeh, A., Nazififard, M., & Hashemi‐Dezaki, H. (2024) · Energy Science & Engineering · doi:10.1002/ese3.1774 · Cited by 14 · see citing works
Optimizing Microgrid Energy Management: Intelligent Techniques
Meydani, A., Shahinzadeh, H., Nafisi, H., & Gharehpetian, G. B. (2024) · International Electrical Power Distribution Conference (EPDC) · doi:10.1109/epdc62178.2024.10571716 · Cited by 12 · see citing works
Kaushal, J., & Chowdhury, S. D. (2025) · International Journal of Ambient Energy · doi:10.1080/01430750.2024.2446524 · Cited by 12 · see citing works
Fuzzy information granulation for capacity efficient prediction in lithium-ion battery
Ouyang, T., Wang, C., Jin, S., & Su, Y. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115241 · Cited by 10 · see citing works
MF-ID: A Benchmark and Approach for Multi-Category Fine-Grained Intrusion Detection
Han, F., Ye, P., Li, K., Duan, S., & Wang, L. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3396085 · Cited by 9 · see citing works
Zhang, Z., Ding, T., Mu, C., Huang, Y., Liu, J., & Wang, Y. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3408893 · Cited by 9 · see citing works
Zhao, Z., Tian, E., & Wang, L. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3571457 · Cited by 8 · see citing works
Correlation-aware fusion model for robust SOH estimation in lithium-ion batteries
Gong, Y., Lu, X., Su, Z., Huang, Z., Quan, Y., Zhang, Z., & Ouyang, T. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2025.120018 · Cited by 7 · see citing works
Chehade, A., Hassanieh, W., & Krivtsov, V. V. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110107 · Cited by 6 · see citing works
Self-citationSelf-Organizing Stacked Type-2 Fuzzy Neural Network With Rule Generalization
Han, H., Sun, C., Wu, X., Yang, H., & Zhao, D. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3544495 · Cited by 5 · see citing works
Yang, J., Zhang, M., & Wang, X. (2025) · Energy · doi:10.1016/j.energy.2025.135648 · Cited by 6 · see citing works
Chen, L., Zu, Z., Ma, H., Lopes, A. M., & Chen, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3647502 · Cited by 4 · see citing works
Graph neural network modeling of lithium-ion battery capacity with physics-guided features
Xu, W., Mao, R., Han, P., Yuan, N., Li, Y., Guo, Y., & Zhang, H. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121627 · Cited by 4 · see citing works
Chelbi, M., Hassanieh, W., Hussein, A. A.-H., & Chehade, A. (2023) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce53617.2023.10362886 · Cited by 4 · see citing works
Self-citationZhang, S., Du, S., Tan, S., & Dong, F. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3579438 · Cited by 4 · see citing works
Disentangled latent spaces for reduced order models using deterministic autoencoders
Schwarz, H., Lin, P. P., Zemke, J. M., & Rung, T. (2025) · Computers & Fluids · doi:10.1016/j.compfluid.2025.106837 · Cited by 4 · see citing works
Fricke, K., do Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3622509 · Cited by 4 · see citing works
Orthogonal Autoencoders for Long-Term State-of-Charge Forecasting of Li-ion Battery Cells
Hassanieh, W., Savargaonkar, M., & Chehade, A. (2023) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec55900.2023.10186948 · Cited by 4 · see citing works
Self-citationLiu, K., Wang, S., Liang, H., Shi, Z., Li, M., Yu, C., Zhou, K., Gong, X., Yuan, G., & Qi, C. (2023) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ace65b · Cited by 3 · see citing works
Anggraeni, D., Sudiarto, B., Fitrianingsih, E., & Priambodo, P. S. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14110307 · Cited by 1 · see citing works
Li, J., Gao, Z., Gao, X., & Wang, Y. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115109 · Cited by 1 · see citing works
Bai, Y., & Feng, H. (2024) · International Conference on Smart Grids and Energy Systems (SGES) · doi:10.1109/sges63808.2024.10824105 · Cited by 1 · see citing works
Assessing the Impact of Renewable Energy Sources on Energy Economics
Karapidakis, E. S., Katsigiannis, Y. A., Blazakis, K. V., Nikologiannis, M., MATALLIOTAKIS, G., Stavrakakis, G. S., VENIANAKIS, N., & Bonfini, P. (2025) · doi:10.1002/9781394401604.ch10
Yuan, N., Mao, R., Han, P., Xu, W., Li, Y., & Zhang, H. (2025) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin64977.2025.11279135
Duan, B., Sun, H., Liu, X., Li, Y., Liu, K., Li, C. L., & Kang, Y. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3697112
Groudsorn, P., Kruthun, O., Ding, Z., & Sarikprueck, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7160979
Online Parameter Identification of Li-Ion Battery Balancing Systems: A Switching System Approach
Li, H., Lv, T., Zhu, R., Wu, Y., & Liu, W. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3716501
Lakshmaiya, N. (2026) · Journal of Industrial and Engineering Chemistry · doi:10.1016/j.jiec.2026.08.032
Explainable Hybrid Deep Learning for Lithium-Ion Battery Health Assessment and Lifetime Prediction
Hartomo, K. D., Radjawane, S., Purnomo, H. D., Yugopuspito, P., Toba, H., Virginia, G., & Arthur, C. (2026) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia70925.2026.11717281
- Authors
- Savargaonkar, M., Oyewole, I., Chehade, A., & Hussein, A. A.
- Affiliations
- University of Michigan (US)
- University of Central Florida (US)
- Venue
- IEEE Transactions on Automation Science and Engineering
- Published
- 2024-01
- Volume
- 21
- Issue
- 1
- Pages
- 15-26
- ISSN
- 1545-5955 (ISSN-L)
- 1558-3783
- OpenAlex
- W4313188566
- Cited by
- 45
- References
- 45
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 14 · 2023: 7
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Automation Science and Engineering (publishedVersion)
- 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, lithium-ion batteries, sparse autoencoders, long short-term memory, battery degradation, end-of-life batteries, multi-task learning, battery management systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
