A Novel Deep Learning Framework for State of Health Estimation of Lithium-Ion Battery

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works

Learning long-term dependencies with gradient descent is difficult

Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542 · see citing works

Degradation diagnostics for lithium ion cells

Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.011 · Cited by 1,945 · see citing works

LSTM Fully Convolutional Networks for Time Series Classification

Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017) · IEEE Access · doi:10.1109/access.2017.2779939 · Cited by 1,358 · see citing works

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326 · see citing works

Multivariate LSTM-FCNs for time series classification

Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.014 · Cited by 1,138 · 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

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765 · see citing works

Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

Wei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643 · see citing works

A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation

Li, J., Adewuyi, K., Lotfi, N., Landers, R. G., & Park, J. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.01.011 · Cited by 623 · see citing works

State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

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

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter:

Li, Y., Abdel-Monem, M., Gopalakrishnan, R., Berecibar, M., Nanini-Maury, E., Omar, N., van den Bossche, P., & Van Mierlo, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.10.092 · Cited by 531 · see citing works

Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks

Li, X., Zhang, L., Wang, Z., & Dong, P. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2018.12.011 · Cited by 445 · see citing works

Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy

Galeotti, M., Cinà, L., Giammanco, C., Cordiner, S., & Di Carlo, A. (2015) · Energy · doi:10.1016/j.energy.2015.05.148 · Cited by 412 · see citing works

State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

Li, X., Wang, Z., Zhang, L., Zou, C., & Dorrell, D. D. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.10.069 · Cited by 388 · see citing works

A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

Dai, H., Zhao, G., Lin, M., Wu, J., & Zheng, G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2880703 · Cited by 325 · see citing works

Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model

Zheng, L., Zhang, L., Zhu, J. G., Wang, G., & Jiang, J. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.016 · Cited by 303 · see citing works

Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine

Wang, Y., Sun, Z., & Chen, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113707 · Cited by 277 · see citing works

A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity

Chen, L., Lü, Z., Lin, W., Li, J., & Pan, H. (2017) · Measurement · doi:10.1016/j.measurement.2017.11.016 · Cited by 276 · see citing works

Battery state of charge estimation using a load-classifying neural network

Tong, S., Lacap, J., & Park, J. W. (2016) · Journal of Energy Storage · doi:10.1016/j.est.2016.07.002 · Cited by 234 · see citing works

Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

Deng, Y., Ying, H., Jiaqiang, E., Zhu, H., Wei, K., Chen, J., Zhang, F., & Liao, G. (2019) · Energy · doi:10.1016/j.energy.2019.03.177 · Cited by 222 · see citing works

State of health estimation of lithium-ion batteries based on the constant voltage charging curve

Wang, Z., Zeng, S., Guo, J., & Qin, T. (2018) · Energy · doi:10.1016/j.energy.2018.11.008 · Cited by 220 · see citing works

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Zhang, H., Miao, Q., Zhang, X., & Liu, Z. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.12.036 · Cited by 217 · see citing works

State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application

Berecibar, M., Garmendia, M., Gandiaga, I., Crego, J., & Villarreal, I. (2016) · Energy · doi:10.1016/j.energy.2016.02.163 · Cited by 213 · see citing works

Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator

Wang, Y., Liu, C., Pan, R. N., & Chen, Z. (2017) · Energy · doi:10.1016/j.energy.2017.01.044 · Cited by 204 · see citing works

A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter

Yan, W., Zhang, B., Zhao, G., Tang, S., Niu, G., & Wang, X. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2842782 · Cited by 203 · see citing works

A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries

Wang, Y., Zhang, C., & Chen, Z. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.081 · Cited by 200 · see citing works

A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter

Ye, M., Guo, H. N., & Cao, B. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.133 · Cited by 175 · see citing works

Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine

Meng, J., Cai, L., Luo, G., Stroe, D., & Teodorescu, R. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.07.025 · Cited by 168 · see citing works

Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds

Downey, A. R. J., Lui, Y. H., Hu, C. K., Laflamme, S., & Hu, S. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.09.018 · Cited by 141 · see citing works

State of Charge Estimation of Vanadium Redox Flow Battery Based on Sliding Mode Observer and Dynamic Model Including Capacity Fading Factor

Xiong, B., Zhao, J., Su, Y., Wei, Z., & Skyllas‐Kazacos, M. (2017) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2699288 · Cited by 133 · see citing works

Diagnosis and prognosis of degradation in lithium-ion batteries

Birkl, C. R. (2017) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-x55vvezkm · Cited by 133 · see citing works

An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

Liu, D., Yin, X., Song, Y., Liu, W., & Peng, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2854224 · Cited by 131 · see citing works

Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition

Zhao, Z., Zheng, Y., Zhang, Z., Wang, H., Zhao, Y., & Li, C. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1477 · Cited by 100 · see citing works

Neocognitron

Fukushima, K. (2007) · Scholarpedia · doi:10.4249/scholarpedia.1717 · Cited by 54 · see citing works

Degradation Diagnostics for Commercial Lithium-Ion Cells Tested at − 10°C

Birkl, C. R., McTurk, E., Zekoll, S., et al. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.1401712jes · Cited by 44 · see citing works

Multiple-grained velocity prediction and energy management strategy for hybrid propulsion systems

Wang, Y., Li, X., Wang, L., et al. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100950 · Cited by 43 · see citing works

pyGPs - A Python Library for Gaussian Process Regression and Classification

Neumann, M., Huang, S., Marthaler, D. E., et al. (2015) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 19 · see citing works

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441 · see citing works

A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction

Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351 · see citing works

Robust State of Health Estimation of Lithium-Ion Batteries Using Convolutional Neural Network and Random Forest

Yang, N., Song, Z., Hofmann, H., & Sun, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2021.103857 · Cited by 276 · see citing works

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao, L., Xu, S., Tang, A., Zhou, F., Hou, J., Xiao, Y., & Fu, Z. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12030113 · Cited by 237 · see citing works

Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning

Zhu, Y., Jiang, B., Zhu, J., Wang, X., Wang, R., Wei, X., & Dai, H. (2023) · Energy · doi:10.1016/j.energy.2023.129283 · Cited by 59 · see citing works

State of health estimation of lithium-ion battery based on CNN–WNN–WLSTM

Yao, Q., Song, X., & Xie, W. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01300-3 · Cited by 53 · see citing works

Battery state of health estimation under fast charging via deep transfer learning

Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works

Data driven health and life prognosis management of supercapacitor and lithium-ion battery storage systems: Developments, implementation aspects, limitations, and future directions

Hossain Lipu, M. S., Rahman, M. S. A., Mansor, M., Rahman, T., Ansari, S., Fuad, A. M., & Hannan, M. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113172 · Cited by 41 · see citing works

Deep learning powered rapid lifetime classification of lithium-ion batteries

Fei, Z., Zhang, Z., Yang, F., & Tsui, K.-L. (2023) · eTransportation · doi:10.1016/j.etran.2023.100286 · Cited by 36 · see citing works

Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves

Beltran, H., Sansano, E., & Pecht, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2022.106346 · Cited by 29 · see citing works

Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey

Mei, P., Karimi, H. R., Xie, J., Chen, F., Ou, L., Yang, S., & Huang, C. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114857 · Cited by 29 · see citing works

SOH Prediction for Lithium-Ion Batteries Based on SSABP-MLR

Zheng, X., Su, N., & Huang, W. (2024) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-97-1072-0_58 · Cited by 2 · see citing works

State of health estimation for lithium-ion batteries with Bayesian optimized Gaussian process regression

Ye, L.-H., He, Z., Ke, C.-L., Cheng, X., Zhao, X., Zhang, Z.-X., & Shi, A.-P. (2025) · Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering · doi:10.1177/09544070241287254 · Cited by 2 · see citing works

Implementing convolutional neural network model for prediction in medical imaging

Pandey, R., Sahai, A., & Kashyap, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824054-0.00024-1 · Cited by 8 · see citing works

Leaf Features Extraction for Plant Classification using CNN

Prasad, M. P. S., & Agniraj, S. (2021) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-807 · Cited by 7 · see citing works

Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning

Zhao, X., Qi, S., Zhang, B., Ma, H., Qian, W., Yao, Y., & Sun, J. (2019) · Journal of X-Ray Science and Technology · doi:10.3233/xst-180490 · Cited by 63 · see citing works

Automated Fruit Classification Using Deep Convolutional Neural Network

Orquia, J. J. D., & Bibangco, E. J. P. (2020) · Philippine Social Science Journal · doi:10.52006/main.v3i2.188 · Cited by 4 · see citing works

Battery health and performance monitoring system: a closer look at state of health (SoH) assessment methods of a Lead-Acid battery

Selvabharathi, D., & Muruganantham, N. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v18.i1.pp261-267 · Cited by 10 · see citing works