Predicting the state of charge and health of batteries using data-driven machine learning

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094

A review on the key issues for lithium-ion battery management in electric vehicles

Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837

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

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

Factorization Machines

Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,584

A comparative study of equivalent circuit models for Li-ion batteries

Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179

Simulation and Optimization of the Dual Lithium Ion Insertion Cell

Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057

The high-throughput highway to computational materials design

Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

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

A comprehensive data-driven assessment scheme for power battery of large-scale electric vehicles in cloud platform

Wang, Y., Han, X., Xu, X., Pan, Y., Dai, F., Zou, D., Lu, L., & Ouyang, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107210 · Cited by 11

Li-ion battery aging model robustness: An analysis using univariate and multivariate techniques

Marchegiani, E., Ferracuti, F., Monteriù, A., Jin, L., Rossi, M., Comodi, G., & Ciabattoni, L. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108591 · Cited by 11

State of Health Prediction of Lithium-Ion Batteries Using Combined Machine Learning Model Based on Nonlinear Constraint Optimization

Liang, Y., Wang, S., Fan, Y., Hao, X., Liu, D., & Fernández, C. (2024) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ad18e1 · Cited by 12

An online evaluation model for mechanical/thermal states in prismatic lithium-ion batteries under fast charging/discharging

Huang, Z., Wang, H., Zou, W., Zhang, R., Wang, Y., Chen, J., & Wu, S. (2024) · Energy · doi:10.1016/j.energy.2024.131877 · Cited by 11

Onboard In-situ Warning and Detection of Li Plating for Fast-charging Batteries with Deep Learning

Wang, H., Song, Y., Sun, X., Mo, S., Chen, C., & Wang, J.-J. (2024) · Energy Storage Materials · doi:10.1016/j.ensm.2024.103585 · Cited by 14

A novel correlation-based approach for combined estimation of state of charge and state of health of lithium-ion batteries

Wu, Y., Wang, T., Huang, Y., Li, Z., Xu, L., Li, D., & Zhao, J. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112655 · Cited by 11

Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL

Phung, M. T., Nguyen, T.-C.-H., Akhtar, M. S., & Yang, O. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102380 · Cited by 11

Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance

Yamashita, A., Berg, S., & Figgemeier, E. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100409 · Cited by 11

A Brief Review of Key Technologies for Cloud-Based Battery Management Systems

Wu, D. Q., Zhihong, X., Wang, Q., Jin, Z., Xu, Y., Wang, C., & He, X. (2024) · Journal of Electronic Materials · doi:10.1007/s11664-024-11495-x · Cited by 11

Data-Driven State of Health and Functionality Estimation for Electric Vehicle Batteries Based on Partial Charge Health Indicators

Etxandi-Santolaya, M., Montes, T., Casals, L. C., Corchero, C., & Eichman, J. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3505434 · Cited by 11

Temperature-dependent performance prediction for cerium oxynitride solid-state symmetric supercapacitor using machine learning

Ghosh, S., Sibi, A., Priyanga, G. S., Dagdia, Z. C., & Thomas, T. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115562 · Cited by 11

Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network

Xi, H., Lv, T., Qin, J., Ma, M., Xie, J., Lu, S., & Liu, Z. (2025) · Applied Sciences · doi:10.3390/app15063011 · Cited by 13

Combining electrochemistry and data-sparse Gaussian process regression for lithium-ion battery hybrid modeling

Fogelquist, J., & Lin, X. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126458 · Cited by 11

Estimating the State of Health of Lead-Acid Battery Using Feed-Forward Neural Network

Selvabharathi, D., & Muruganantham, N. (2021) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126622500815 · Cited by 11

Seq2Seq regression learning-based multivariate and multistep SOC forecasting of BESS in frequency regulation service

Ardiansyah, A., Masood, Z., Choi, D., & Choi, Y. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100939 · Cited by 11

Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge

Wang, Y., Han, X., Lu, L., Chen, Y., & Ouyang, M. (2022) · Fractal and Fractional · doi:10.3390/fractalfract6110640 · Cited by 11

A Review and Perspective on the Main Machine Learning Methods Applied to Physical Sciences

Méndez-Gurrola, I. I., Ramírez-Reyes, A., & Gutiérrez, A. I. B. (2022) · Acta Polytechnica Hungarica · doi:10.12700/aph.19.10.2022.10.12 · Cited by 10

Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach

Yousefian, P., Sepehrinezhad, A., van Duin, A. C. T., & Randall, C. A. (2023) · APL Machine Learning · doi:10.1063/5.0158360 · Cited by 11

Data Analysis and Visualization Platform Design for Batteries Using Flask-Based Python Web Service

Liang, Z., Liang, Z., Zheng, Y., Liang, B., & Zheng, L. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12040187 · Cited by 11

Soft-hard heterostructure functional carbon materials: Synthesis, structure regulation, and applications in energy storage

Lv, T., Zhang, J., Yang, X., & Qiu, J. (2025) · Carbon · doi:10.1016/j.carbon.2025.120958 · Cited by 10

Machine Learning Aided Predictions for Capacity Fade of Li-Ion Batteries

Penjuru, N. M. H., Reddy, G. V., Nair, M. R., Sahoo, S., Mayank, Jiang, J. Z., Ahmed, J., Wang, H., & Roy, T. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac7102 · Cited by 10

Augmenting machine learning with human insights: the model development for B2B personalization

Yaghtin, S., & Mero, J. (2023) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-02-2023-0073 · Cited by 10

Machine learning assisted reactor and full process optimization design for alcohol oxidation

Zhang, Z., Zhang, D., Zhu, M., Zhao, H. J., Zhou, X., Yan, H., & Yang, C. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.121165 · Cited by 10

Physics-Informed Data-Driven Power Capacity Prediction of Lithium-Ion Battery Against Various Temperatures

Gao, G., Dong, G., Lou, Y., Sun, L. L., & Wei, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3549458 · Cited by 10

Unraveling the Smart Charging Technologies, Energy Sources, and Regulatory Standards for EVs

Arooj, A., Ahmed, Q. Z., Farooq, S., Umer, T., Aslam, N., & Alade, T. T. (2025) · IEEE Access · doi:10.1109/access.2025.3579428 · Cited by 10

Localized feature selection augmented dual-stream fusion network for state of health estimation of lithium-ion batteries

Wei, Z., Wu, M.-W., Ju, W., Zhang, X., Fei, K., He, Q., Shen, Z., Li, Z., & Zhao, Y. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.06.030 · Cited by 10

Machine learning for polyphenol-based materials

Jiang, S., Yang, P., Zheng, Y., Lu, X., & Xie, C. (2024) · Smart Materials in Medicine · doi:10.1016/j.smaim.2024.02.001 · Cited by 10

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

Wang, Y., & Jiang, B. (2024) · Batteries · doi:10.3390/batteries10070229 · Cited by 12

A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

Hu, X., & Sun, Y. (2020) · Electronics · doi:10.3390/electronics9122054 · Cited by 10

Modeling and Simulation of a Commercial Lithium-Ion Battery with Charge Cycle Predictions

Náfate, M. P. C., Mendoza, A. C., Gómez, C. J. V., & Espinosa-Moreno, F. (2022) · Sustainability · doi:10.3390/su142114035 · Cited by 11

Universal Battery Performance and Degradation Model for Electric AircraftPreprint

Bills, A., Sripad, S., Fredericks, W. L., Guttenberg, M., Charles, D., Frank, E., & Viswanathan, V. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12616169.v1 · Cited by 10

Overview of Machine Learning-Enabled Battery State Estimation Methods

Zhuge, Y., Yang, H., & Wang, H. (2023) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec43580.2023.10131605 · Cited by 9

Application of Machine Learning in Energy Storage: A Scientometric Research of a Decade

Ajibade, S.-S. M., Bashir, F. M., Dodo, Y. A., Dayupay, J. P., Calzada, L. M. D. L., & Adediran, A. O. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-48981-5_10 · Cited by 9

Physics-Informed Fractional-Order Recurrent Neural Network for Fast Battery Degradation with Vehicle Charging Snippets

Wang, Y., Wei, M., Dai, F., Zou, D., Chen, L., Han, X., Chen, Y., & Ji, C. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9020091 · Cited by 9

Estimate state of charge in lithium-ion batteries with unknown data

Hu, J., Li, X., Fang, Z., Cheng, J., Yi, L., & Zhang, Z. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125736 · Cited by 9

Nanoengineering of Boron‐Based Materials for Lithium Batteries: Advances, Challenges, and Prospects

Junfeng, H., Tabish, M., Ajmal, S., Kumar, A., Guo, J., Alam, M. M., & Yasin, G. (2025) · Small Methods · doi:10.1002/smtd.202500208 · Cited by 10

Online condition monitoring and state of health estimation method for lithium-ion batteries based on time-ratio features

Chen, G., Wang, C., Yang, Y., Zhang, X., Deng, W., & Liu, J. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104364 · Cited by 9

State of Health Prediction of Lithium-Ion Batteries Based on Dual-Time-Scale Self-Supervised Learning

Li, Y., Kang, L., Wang, X., Xie, D., & Wang, S. (2025) · Batteries · doi:10.3390/batteries11080302 · Cited by 10

SOC Prediction of Li-Ion Battery Based on EKF and CNN-BiLSTM-Attention

Zhu, M., Wang, Y., & Wang, D. (2025) · ACS Omega · doi:10.1021/acsomega.5c06451 · Cited by 9

Synergizing artificial intelligence and battery energy storage systems for a sustainable clean energy transition

Shukla, G. K., Shukla, A. K., Shukla, K., & Dixit, S. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120343 · Cited by 9

Unveiling Electrolyte Design Principles for Sodium-Ion Batteries Using Combinatorial Electrochemistry and Machine Learning-Assisted Analysis

Ono, M., Takahashi, M., Tamura, R., & Matsuda, S. (2026) · ACS Applied Energy Materials · doi:10.1021/acsaem.5c03028 · Cited by 9

Sustainable graphene-based energy storage device technology: Materials, methods, monitoring and digital twin

Priyadarshi, H., Ahmed, G., Mishra, D., Srivastava, A. K., McGee, R. C. V., Shrivastava, A., & Singh, K. (2024) · Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences · doi:10.1080/10408436.2024.2311413 · Cited by 9

A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures

Chen, G., Jiang, S., Xie, M., & Yang, F. (2022) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-yantai55411.2022.9942155 · Cited by 9

Battery data integrity and usability: Navigating datasets and equipment limitations for efficient and accurate research into battery aging

Gering, K. L., Shirk, M. G., Kim, S., Walker, C. M., Dufek, E. J., & Wang, Q. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1125175 · Cited by 8

Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations

Aksöz, A., Asal, B., Biçer, E., Oyucu, S., Gençtürk, M., & Golestan, S. (2024) · Energies · doi:10.3390/en17133117 · Cited by 10

Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective

Sharma, H., Katari, V., Sahu, K. K., & Singh, A. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad708f · Cited by 8

Novel state of charge estimation of lithium-ion batteries using empirical neural tangent kernel and unscented Kalman filter

Kang, J., Kim, T., & Kim, Y.-S. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115372 · Cited by 9

Adaptive engineering-assisted deep learning for battery module health monitoring across dynamic operations

Tang, A., Xu, Y., Tian, J., Zou, H., Liu, K., & Yu, Q. (2025) · Energy · doi:10.1016/j.energy.2025.135332 · Cited by 8

Challenges and prospects in real-world battery status prediction within Industry 4.0

Qu, X., Zhao, J., Pang, H., Fowler, M. W., & Burke, A. F. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100298 · Cited by 8

Life degradation monitoring using generated constant-current charging voltage curves for lithium-ion batteries

Zhao, F., Zhou, H., Liu, X., Cai, H., Chen, J., Zhu, Z., Niu, Y., Li, X., Hua, J., & Zheng, Y. (2025) · Energy · doi:10.1016/j.energy.2025.136053 · Cited by 8

Predicting lithium-ion battery state of charge with long short-term memory network enhanced extended Kalman filter

Yu, B., Wang, G., Zhu, E., Yao, S., & Zhou, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117849 · Cited by 10

Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms

Zheng, W., Wu, D., Yuan, C., Jiang, H., & Wang, S. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238192 · Cited by 8

Interpretable Machine Learning for Solid-State Batteries

Ye, X., Cheng, Y., Lan, X., You, Y., Peng, J., Liang, G., Guo, X. Y., Zhao, C., Park, H. S., Sun, Y., et al. (2026) · ACS Nano · doi:10.1021/acsnano.5c21738 · Cited by 8

In-situ coordination defects in metal oxides: chemical perspectives and operando characterization for high-performance multivalent-ion energy storage

Solangi, N. H., Neiber, R. R., Kumar, J., Zagad, S., Jiao, J.-M., Guo, M., Lu, X.-M., Charles, V., & Lu, J. (2026) · Coordination chemistry reviews · doi:10.1016/j.ccr.2026.217793 · Cited by 8

Sparse data machine learning for battery health estimation and optimal design incorporating material characteristics

Zhang, J., & Lu, W. D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118165 · Cited by 8

Battery Materials Discovery and Smart Grid Management using Machine Learning

Wong, A. J. Y., Zhou, X., Lum, Y., Yao, Z., Chua, Y. C., Wen, Y., & Seh, Z. W. (2022) · Batteries & Supercaps · doi:10.1002/batt.202200309 · Cited by 8

State of charge estimation of lithium-ion batteries with non-negligible outlier observations based on Student’s-T filter

Yun, Z., Qin, W., & Shi, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105825 · Cited by 8

Sensor data fusion in electrochemical applications: An overview and its application to electrochlorination monitoring

Ross, E. A., Wagterveld, R. M., Stigter, J. D., Mayer, M. J. J., & Keesman, K. J. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108128 · Cited by 8

Machine Learning Application in Battery Prediction: A Systematic Literature Review and Bibliometric Study

Kamul, A., Hananda, N., Harito, C., Djuana, E., Elwirehardja, G. N., Pardamean, B., GUNAWAN, F. E., Budiman, A. S., Asrol, M., Redi, A. A. N. P., et al. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202338801021 · Cited by 8

Second Life for Lithium-Ion Traction Batteries

Guenther, L. H., Klein, V., Loef, G., Pohl, A., Okube, H., Winterbur, R., Röwer, G., Wegener, B., Goertz, R., & Bracke, S. (2024) · Sustainability · doi:10.3390/su16177288 · Cited by 8

Using Genetic Programming to Learn Behavioral Models of Lithium Batteries

Capua, G. D., Bourelly, C., Stefano, C. D., Fontanella, F., Milano, F., Molinara, M., Oliva, N., & Porpora, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30229-9_30 · Cited by 8

Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Reis, M. S., & Jiang, B. (2022) · Chemical Engineering Science · doi:10.1016/j.ces.2022.117510 · Cited by 8

RecyBat24: a dataset for detecting lithium-ion batteries in electronic waste disposal

Acaro, X., Scudo, F. L., Cappuccino, G., & Dodaro, C. (2025) · Scientific Data · doi:10.1038/s41597-025-05211-5 · Cited by 11

State of health estimation for lithium-ion batteries based on recurrence plot analysis using charging voltage curves

Zou, W., Hu, Z., Yu, K., Zhang, H., Lu, H., & Mao, L. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116804 · Cited by 7

Variational Bayesian and Multikernel Correntropy-Based Unscented Kalman Filter for Battery SOC Estimation

Tan, Y., Dang, L., Dou, H., Liu, M., & Chen, B. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3605227 · Cited by 7

A load-balanced deformable expert network for lithium-ion battery remaining useful life prediction

Dong, X. M., Yao, M., & Xiang, S. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119587 · Cited by 7

Real-time artificial intelligence for solid-state lithium metal batteries

Wang, M., Wu, Y., Cao, Y., Li, G., Miao, X., & Li, X. (2025) · Nature Communications · doi:10.1038/s41467-025-66079-y · Cited by 7

Intelligent Battery Systems: System‐Level Integration of Data‐Driven Learning, Optimisation and Predictive Management

Ametefe, D. S., Sukor, N. S. A., John, D., Ametefe, G. D., Aliu, A. A., Owen, M. M., & Salaudeen, T. O. (2026) · Battery energy · doi:10.1002/bte2.70099 · Cited by 8

An Accurate and Interpretable Lifetime Prediction Method for Batteries using Extreme Gradient Boosting Tree and TreeExplainer

Jiang, F., He, Y., Gao, D., Zhou, Y., Liu, W., Yan, L., & Peng, J. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00164 · Cited by 7

An open access tool for exploring machine learning model choice for battery life cycle prediction

Schauser, N. S., Lininger, C. N., Leland, E. S., & Sholklapper, T. Z. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.1058999 · Cited by 7

Prediction of Lithium Battery Health State Based on Temperature Rate of Change and Incremental Capacity Change

Zhang, T., Wang, Y., Ma, R., Zhao, Y., Shi, M., & Qu, W. (2023) · Energies · doi:10.3390/en16227581 · Cited by 7

Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective

Liu, K., Wang, T., Liu, X., & Peng, H. (2024) · Batteries & Supercaps · doi:10.1002/batt.202300596 · Cited by 7

State-of-Charge Prediction of Degrading Li-ion Batteries Using an Adaptive Machine Learning Approach

Babaeiyazdi, I., Rezaei‐Zare, A., & Shokrzadeh, S. (2022) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm48719.2022.9916995 · Cited by 7

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

Zixuan, L., Qu, D., Fan, L., & Shuo, Y. (2023) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.3734 · Cited by 7

A Novel Internal Resistance Curve Based State of Health Method to Estimate Battery Capacity Fade and Resistance Rise

Zhang, J., & Zhang, X. (2020) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec48692.2020.9161501 · Cited by 7

The Investigation of Artificial Intelligence Methods for Identifying States and Analyzing System Transitions Between States

Melnykova, N., Melnykov, V., Шаховська, Н. Б., & Lysa, N. (2020) · IEEE International Conference on Computer Sciences and Information Technologies (CSIT) · doi:10.1109/csit49958.2020.9321841 · Cited by 7

A fast solver for the pseudo-two-dimensional model of lithium-ion batteriesPreprint

Han, R., Macdonald, C. B., & Wetton, B. T. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09251 · Cited by 7

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang, Y., Li, R., Sun, H., & Zhang, X. (2024) · Batteries · doi:10.3390/batteries10050154 · Cited by 6

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Altinpulluk, N. B., Altinpulluk, D., Ramanan, P., Paulson, N. H., Qiu, F., Babinec, S., & Yildirim, M. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102215 · Cited by 6

Based on NARXNN-PF state of charge estimation for lithium batteries

Haizhong, C., Huiheng, H., Liu, F., & Shen, X. (2025) · Ionics · doi:10.1007/s11581-025-06088-5 · Cited by 6

Extract features from lithium-ion battery electrochemical impedance spectra and estimate state of health based on improved convolutional autoencoder-temporal convolutional network

Lou, C., Wang, S., Li, Z., & Wang, K. (2025) · Ionics · doi:10.1007/s11581-025-06226-z · Cited by 6

Data-Driven Battery Health Prognosis Using Scalable Deep Recurrent Structure and Partial Fast-Charging Profiles

Chen, H., Dong, G., Wang, Y., Yu, J., Wu, L., & Lou, Y. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3574748 · Cited by 7

Fuzzy physics-informed thermal diagnostics via non-invasive frequency-domain signature distillation in high-energy-density batteries

Lin, Z., Yang, H. J., Li, C., Wang, S., Cong, W., Li, T., Lin, Q., Sun, K., & Cheng, F. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.08.018 · Cited by 6

State of charge estimation for LiFePO4 power battery based on multi-dimensional operating features

Pang, F., Zhang, F., Li, C., Wu, R., & Xie, Y. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106756 · Cited by 7

Probabilistic State of Health Prediction for Lithium-Ion Batteries Based on Incremental Capacity and Differential Voltage Curves

Wang, Q., Yan, H., Wang, Y., Yang, Y., Liu, X., Zhu, Z., Huang, G., & Huang, Z. (2025) · Energies · doi:10.3390/en18205450 · Cited by 6

State of charge estimation approach based on physics-data fusion model and adaptive Kalman filter for lithium battery

Huang, C., Sun, J., & Wang, X. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119291 · Cited by 7

Lithium-Ion Battery Thermal Event and Protection: A Review

Chang, C., Gorin, C. F., Zhu, B., Beaucarne, G., Guo, J., & Yoshida, S. (2023) · SAE International Journal of Electrified Vehicles · doi:10.4271/14-13-03-0019 · Cited by 8

Data Science-Based Battery Manufacturing Management

Liu, K., Wang, Y., & Lai, X. (2022) · Green energy and technology · doi:10.1007/978-3-031-01340-9_3 · Cited by 6

Electrochemical modeling in a building blocks’ way

Yang, B., Wang, D., Xu, S., Chen, S., & Yu, B. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.140419 · Cited by 6

A Machine Learning Framework for Li-Ion Battery Lifetime Prognostics

Fouka, A., Lepenioti, K., Bousdekis, A., & Mentzas, G. N. (2022) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa56318.2022.9904407 · Cited by 6

Deep sorting of reused batteries for enabling long-term consistency grouping with unknown prior conditions

Wang, S., Gao, F., & Tian, H. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102657 · Cited by 5

Bridging lab to industry: Deep learning cuts testing time for lithium metal battery health models

Yu, Q., Wang, C., Li, J., Fan, L., Liu, S., & Xiong, R. (2025) · Energy storage materials · doi:10.1016/j.ensm.2025.104748 · Cited by 6

From partial charging to physics-aligned health prediction: A dual-path neural framework bridging features and degradation signatures

Guo, S., Wang, Y., Cui, Y., Deng, L., Zhao, L., Li, J., & Wang, Z. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118493 · Cited by 5

Battery state of charge estimation from electrochemical impedance spectroscopy data based on machine learning with fractional order model parameters and distribution relaxation time features

Du, L., Shen, X., Li, X., Li, X., & Wei, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119754 · Cited by 6

Machine-learning prediction of metal sulfide photocatalysts for sacrificial hydrogen evolution under visible light irradiation

Yamaguchi, Y., Kakami, F., Baba, R., Takahara, W., Harashima, Y., Takayama, T., Fujii, M., & Kudo, A. (2026) · Journal of Materials Chemistry A · doi:10.1039/d5ta06041a · Cited by 5

Geometry-aware curation for data-efficient battery state-of-health estimation of large-scale electric vehicles

Zhang, R., Zhu, J., Cai, J., Jiang, B., Chen, Z., Li, D., Li, Y., Wei, C., Jiang, Y., Schade, W., et al. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128284 · Cited by 5

Cross‐Domain State‐of‐Charge Estimation of Li‐Ion Batteries Based on Source Domain Selection and Attention Mechanism

Zhang, W., Hu, J., Lin, B., Liu, D., Wang, M., Mu, D., Lin, C., & Lu, Y. D. (2024) · International Journal of Energy Research · doi:10.1155/2024/8837186 · Cited by 6

Modeling Considerations for Developing Deep Space Autonomous Spacecraft and Simulators

Agia, C., Vila, G. C., Bandyopadhyay, S., Bayard, D. S., Cheung, K., Lee, C. H., Wood, E. L., Aenishanslin, I., Ardito, S., Fesq, L. M., et al. (2024) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero58975.2024.10521055 · Cited by 5

Behavioral Models for Lithium Batteries Based on Genetic Programming

Capua, G. D., Porpora, F., Milano, F., Oliva, N., & Maffucci, A. (2024) · IEEE Access · doi:10.1109/access.2024.3434716 · Cited by 5

Data-driven assessment of lithium-ion battery degradation using thermal patterns from computer vision

Li, Z., Tu, H., Wang, H., Hu, L., Chen, S., Yan, R., & He, X. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.12.063 · Cited by 5

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

A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries

Akbar, K., Zou, Y., Awais, Q., Baig, M. J. A., & Jamil, M. (2022) · Electronics · doi:10.3390/electronics11081216 · Cited by 57

Data-driven Capacity Estimation of Lithium-Ion Batteries Using Machine Learning Methods

Nafeh, R. (2023) · AMS Degree Thesis (University of Bologna)

Machine learning of materials design and state prediction for lithium ion batteries

Mao, J., Miao, J., Lü, Y., & Tong, Z. (2021) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2021.04.009 · Cited by 62

State Of Charge Estimation in Lithium-Ion Batteries via Machine Learning

Mashkov, V. P., Karova, M., & Penev, I. P. (2022) · International Conference Automatics and Informatics (ICAI) · doi:10.1109/icai55857.2022.9960064 · Cited by 3