Data-Driven Capacity Estimation for Lithium-Ion Batteries with Feature Matching Based Transfer Learning Method

Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell

Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328 · see citing works

Methods for interpreting and understanding deep neural networks

Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works

A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards

Chen, Y., Kang, Y., Zhao, Y., Wang, L., Liu, J., Li, Y., Liang, Z., He, X., Li, X., Tavajohi, N., et al. (2021) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2020.10.017 · Cited by 2,178 · see citing works

The development and future of lithium ion batteries

Blomgren, G. E. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0251701jes · Cited by 1,940 · see citing works

Noise Reduction in Speech Processing

Cohen, I., Huang, Y. A., Chen, J., & Benesty, J. (2009) · Springer topics in signal processing · doi:10.1007/978-3-642-00296-0 · Cited by 1,466 · see citing works

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

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

Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Zhu, J., Wang, Y., Huang, Y., Gopaluni, R. B., Cao, Y., Heere, M., Mühlbauer, M. J., Mereacre, L., Dai, H., Liu, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29837-w · Cited by 835 · see citing works

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

Schmalstieg, J., Käbitz, S., Ecker, M., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.012 · Cited by 755 · see citing works

A review of the state of health for lithium-ion batteries: Research status and suggestions

Tian, H., Qin, P., Li, K., & Zhao, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120813 · Cited by 654 · see citing works

Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus

Hu, X., Yuan, H. S., Zou, C., Li, Z., & Zhang, L. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2865664 · Cited by 536 · see citing works

Artificial Intelligence Applied to Battery Research: Hype or Reality?

Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 511 · see citing works

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Li, X., Yuan, C., Li, X., & Wang, Z. (2019) · Energy · doi:10.1016/j.energy.2019.116467 · Cited by 485 · see citing works

A Review of Lithium-Ion Battery for Electric Vehicle Applications and Beyond

Chen, W., Liang, J., Yang, Z., et al. (2019) · Energy Procedia · doi:10.1016/j.egypro.2019.01.783 · Cited by 453 · see citing works

Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications

Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., & Wang, Y. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128015 · Cited by 435 · see citing works

Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends

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

Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries

Tan, Y., & Zhao, G. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2946551 · Cited by 376 · see citing works

State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking

Weng, C., Feng, X., Sun, J., & Peng, H. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.07.126 · Cited by 324 · see citing works

State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression

Li, X., Yuan, C., & Wang, Z. (2020) · Energy · doi:10.1016/j.energy.2020.117852 · Cited by 323 · see citing works

Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

Ma, G., Xu, S., Jiang, B., Cheng, C., Yang, X. P. O., Shen, Y., Yang, T., Huang, Y., Ding, H., & Yuan, Y. (2022) · Energy & Environmental Science · doi:10.1039/d2ee01676a · Cited by 323 · see citing works

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

Luo, K., Chen, X., Zheng, H., & Shi, Z. (2022) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.06.049 · Cited by 292 · see citing works

Lithium-ion battery capacity estimation—A pruned convolutional neural network approach assisted with transfer learning

Li, Y., Li, K., Liu, X., Wang, Y., & Zhang, L. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116410 · Cited by 247 · see citing works

A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings

Cheng, C., Ma, G., Zhang, Y., Sun, M., Teng, F., Ding, H., & Yuan, Y. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.2971503 · Cited by 238 · see citing works

Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis

Wassiliadis, N., Adermann, J., Frericks, A., Pak, M., Reiter, C., Lohmann, B., & Lienkamp, M. (2018) · Journal of Energy Storage · doi:10.1016/j.est.2018.07.006 · Cited by 227 · see citing works

Cycle life testing and modeling of graphite/LiCoO2 cells under different state of charge ranges

Saxena, S., Hendricks, C., & PECHT, M. G. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.07.057 · Cited by 209 · see citing works

A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements

Li, Y., Li, K., Liu, X., Li, X., Zhang, L., Rente, B., Sun, T., & Grattan, K. T. V. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119787 · Cited by 205 · see citing works

A capacity model based on charging process for state of health estimation of lithium ion batteries

Li, X., Jiang, J., Wang, L. Y., Chen, D., Zhang, Y., & Zhang, C. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.05.109 · Cited by 195 · 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

Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

Liu, K., Peng, Q., Che, Y., Zheng, Y., Li, K., Teodorescu, R., Widanage, W. D., & Barai, A. (2022) · Advances in Applied Energy · doi:10.1016/j.adapen.2022.100117 · Cited by 172 · see citing works

A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries

Ma, G., Xu, S., Yang, T., Du, Z., Zhu, L., Ding, H., & Yuan, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3176925 · Cited by 160 · see citing works

A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

Yang, F., Song, X., Dong, G., & Tsui, K. (2019) · Energy · doi:10.1016/j.energy.2019.01.083 · Cited by 159 · 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

State of health prognostics for series battery packs: A universal deep learning method

Che, Y., Deng, Z., Li, P., Tang, X., Khosravinia, K., Lin, X., & Hu, X. (2022) · Energy · doi:10.1016/j.energy.2021.121857 · Cited by 116 · see citing works

An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

Jiang, B., Zhu, Y., Zhu, J., Wei, X., & Dai, H. (2022) · Energy · doi:10.1016/j.energy.2022.125802 · Cited by 111 · see citing works

Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging

Ruan, H., Wei, Z., Shang, W., Wang, X., & He, H. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120751 · Cited by 109 · see citing works

2010 Asia-Pacific Power and Energy Engineering Conference

(2010) · doi:10.1109/appeec15944.2010 · Cited by 105 · see citing works

Towards a business model for second-life batteries: Barriers, opportunities, uncertainties, and technologies

Júnior, C. A. R., Sanseverino, E. R., Gallo, P., Koch, D., Kotak, Y., Schweiger, H., & Zanin, H. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.12.019 · Cited by 96 · see citing works

Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning

Tao, S., Sun, C., Fu, S., Wang, Y., Ma, R., Han, Z., Sun, Y., Li, Y., Wei, G., Zhang, X., et al. (2023) · ACS Energy Letters · doi:10.1021/acsenergylett.3c01012 · Cited by 91 · see citing works

Experimental investigation and visualization on thermal runaway of hard prismatic lithium-ion batteries used in smart phones

Duh, Y., Lin, K. H., & Kao, C. (2018) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-018-7077-2 · Cited by 77 · see citing works

Driving to the future of energy storage: Techno-economic analysis of a novel method to recondition second life electric vehicle batteries

Horesh, N., Quinn, C. W., Wang, H., et al. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117007 · Cited by 76 · see citing works

Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Tang, X., Liu, K., Wang, X., Liu, B., Gao, F., & Widanage, W. D. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227118 · Cited by 75 · see citing works

Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

Kim, S. W., Oh, K., & Lee, S. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119011 · Cited by 66 · see citing works

Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

Ma, J., Xu, S., Shang, P., Ding, Y., Qin, W.-L., Cheng, Y., Lü, C., Su, Y., Chong, J., Jin, H., et al. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114490 · Cited by 65 · see citing works

Valve Regulated Lead Acid battery float service life estimation using a Kalman filter

Burgess, W. L. (2009) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.12.123 · Cited by 64 · see citing works

Fast Clustering of Retired Lithium-Ion Batteries for Secondary Life with a Two-Step Learning Method

Ran, A., Liang, Z., Chen, S., Cheng, M., Sun, C., Ma, F., Wang, K., Li, B., Zhou, G., Zhang, X., et al. (2022) · ACS Energy Letters · doi:10.1021/acsenergylett.2c01898 · Cited by 56 · see citing works

A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Li, L., Li, Y., Cui, W., Chen, Z., Wang, D., Zhou, B., & Hong, D. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231961 · Cited by 43 · see citing works

A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries

Pang, X., Zhong, S., Wang, Y., Yang, W. B., Zheng, W., & Sun, G. (2022) · The Chemical Record · doi:10.1002/tcr.202200131 · Cited by 23 · see citing works

A Novel Quick Screening Method for the Second Usage of Parallel-connected Lithium-ion Cells Based on the Current Distribution

He, K., Tao, S., Fu, S., et al. (2023) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/acbf7e · Cited by 17 · see citing works

State‐of‐health estimation of lithium‐ion batteries: A comprehensive literature review from cell to pack levels

Su, L., Xu, Y., & Dong, Z. (2024) · Energy Conversion and Economics · doi:10.1049/enc2.12125 · Cited by 86 · 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

Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks

Wang, Z., Zhao, X., Zhen, D., Pombo, J., Yang, W., Gu, F., & Ball, A. (2024) · Energy · doi:10.1016/j.energy.2024.132541 · Cited by 27 · see citing works

Using AI and ML techniques to Forecast COVID-19 cases with Real-time Data SetsPreprint

Khan, N., Tabassum, M., Alrusayni, N. K., et al. (2023) · doi:10.22541/au.167522044.41030133/v1

Forecasting of COVID-19 Cases Using AI and Real-Time DataSet

Khan, N., Alrusayni, N. K., Alkhodhairi, R. K., et al. (2023) · Internet of things · doi:10.1007/978-3-031-28631-5_5

Daily Peak Load Forecasting for Electricity Demand by Time series Models

Lee, J.-S., Sohn, H. G., & Kim, S. (2013) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2013.26.2.349 · Cited by 15 · see citing works

LSTM과 GRU 딥러닝 IoT 파워미터 기반의 단기 전력사용량 예측

이선민, 선영규, 이지영, et al. (2019) · 한국인터넷방송통신학회 논문지

Pork Price Prediction Using Bi-RNN-LSTM Artificial Neural Network

Chen, J., Lin, L., & Li, X. (2022) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd55127.2022.9820121 · Cited by 4 · see citing works