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

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Parameter Identification of Fractional-Order Battery Model Using Single-Objective Optimization

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Battery degradation prognosis and its health management based on grey iterative modeling

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Data-Driven Performance Prediction of Lead–Carbon Batteries: Integrating Experimental Validation and Reduced-Order Model-Guided Neural Networks

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Modeling Battery Degradation in Home Energy Management Systems Based on Physical Modeling and Swarm Intelligence Algorithms

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False Data Injection Attack Against State-of-health Estimation of Lithium Battery with Data Generation

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Data-Driven Models for Predicting the State of Health of Lithium-Ion Batteries

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Rapid state of health diagnosis of used lithium-ion batteries using mechanical and electrochemical measurements

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Historical data-driven self-learning control of battery charging with convex mapping constraints

Han, X., Li, K., Gao, F., & Zhou, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120430

Smart EV Battery Management with Machine Learning Algorithm for Cell Balancing and Fault Detection

Sridhar, B., Allirani, S., Thirumurugan, P., PraneshSharathi, D., Nishikant, N., & Sivaprasath, V. (2025) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces67310.2025.11337153

A PINN-Centric Approach to Battery SOH: Harmonizing LSTM Dynamics with Kalman Filter Precision

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Data-Driven Models to Forecast the Health and Charging States of Batteries

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SOH Prediction for Lithium-Ion Batteries Based on Partial CC Profiles and Feature Selection Optimization

Demidova, G., Poliakov, N. A., Arbuzina, A., Lukichev, D. V., Mamatov, A., & Anuchin, A. (2025) · International Conference on Control, Mechatronics and Automation (ICCMA) · doi:10.1109/iccma67641.2025.11369702

A review of AI techniques for intelligent electric vehicle battery management

Rajesh, M. (2026) · ARPN Journal of Engineering and Applied Sciences · doi:10.59018/1225231

A New Approach to Joint SoC and SoH Estimation for Li-Ion Batteries with Experimental Validation

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Lithium‐Ion Battery RUL Prediction Based on SHAP Feature Enhancement and Dual‐Channel Residual Attention Network

Zheng, B., Zhang, H., Zhao, W., & Chen, Y. (2026) · Quality and Reliability Engineering International · doi:10.1002/qre.70201

A Physics-Informed Dual-Stream Framework with Adaptive Cross-Modal Attention for Lithium-Ion Battery State of Health EstimationPreprint

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Statistical learning and causal inference for energy production

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Enhanced Battery Health Forecasting with Dynamic Thresholds Using Multi Input LSTM Networks

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A Time-Partitioned Dual-Layer LSTM Based on Route Spatiotemporal for Electric Bus Energy Prediction

Wang, Y., Wang, Y., Liu, S., Zhu, Y., Wang, B., Li, Y., Yao, G., & Zhong, W. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17040210

Long-Term Prediction for EV Battery Aging Trend: A Deep Learning Approach

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Machine Learning Framework for Optimising Regeneration of Lithium‐Ion Battery Cathode Materials

Alyoubi, M., Zoromba, M. S., Abdel-Aziz, M. H., Bassyouni, M. I., Mahmoud, E. R. I., Ali, I., & Abdelkader, A. M. (2026) · Energy Technology · doi:10.1002/ente.202502551

A Transformer-Based Autoencoder Framework for State of Health Estimation of Lithium-Ion Batteries Using Partial Charging SegmentsPreprint

Chen, L., Zhu, M., Yan, W., Wang, P., Wang, C., Luo, J., & Zhang, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6668095

29515 Future of AI-ML and Their Potential Impact on Deep Learning Applications in Medical Diagnostics and Imaging

Bhatt, H., Rana, S., & Mittal, M. (2026) · De Gruyter eBooks · doi:10.1515/9783112226223-015

Deep Learning for Lithium Battery Management Systems: A Systematic Literature Review

Mango, R., Du, C., & Olaifa, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-19137-3_10

Digital Twin of Grid-Connected Battery Storage System for Real-Time State-of-Charge Estimation

Kumari, N., Maurya, M., Sharma, A., Tran, B., & Alahakoon, D. (2026) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec67884.2026.11513142

A Probabilistic Physics-Aware Battery Health Management Approach for Inspection Drone Operations

Alcibar, J., Aguirre-Ortuzar, A., & Aizpurua, J. I. (2026) · Engineering asset management review · doi:10.1007/978-3-032-21502-4_11

Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data

Khedhiri, M., Slama, R., Redondo-Iglesias, E., & Trigui, R. (2026) · Batteries · doi:10.3390/batteries12060185

A Unified Framework for Lithium-Ion Battery Lifecycle Perception and State of Health EstimationPreprint

Tang, X., Xu, Z., Chen, N., Wang, Z., & Wang, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6835729

A Transformer-Based Autoencoder Framework for State of Health Estimation of Lithium-Ion Batteries Using Partial Charging SegmentsPreprint

Chen, L., Zhu, M., Yan, W., Wang, P., Wang, C., Luo, J., & Zhang, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6853889

Towards sustainable battery management: Developing battery health estimation models from only incomplete charging curve

Ma, L., Tian, J., Zhang, T., Guo, Q., Guo, R., Shen, W., Lu, Y., & Chung, C. Y. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.105277

Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning AlgorithmPreprint

Ni, N., & Sun, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6886311

Characterizing critical failure impact on LIB pack stability via multiphysics response

Deng, Y., Zheng, H., Zhang, T., & Huang, X. (2026) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2026.111084

Interface-Stabilized CsPbBr3/PbS Heterojunctions Enable Early and High-Precision Capacity Prediction in Lithium-Ion BatteriesPreprint

Nie, S., Mu, Z., Niu, Z., Gu, X., Huang, S. G., Zhu, L., & Wang, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6891406

Physics-informed dimensionality reduction: Battery SOH prediction via a single closed-loop capacity feature

Gao, J., Wang, P., Wang, X., Duan, W., Yue, Y., Yang, X., Miao, Z., & Cui, X. (2026) · Electrochimica Acta · doi:10.1016/j.electacta.2026.149318

Neural Network-Based Black Box Modelling for Real-Time Super Capacitor Health Monitoring

Kar, A. K., A.S.S.Murugan, Laxmi, M. M., Vijetha, Prasanna, S. G., & Kavitha, J. (2025) · International Conference on Emerging Engineering Technologies and Applications (IC-EETA) · doi:10.1109/ic-eeta66496.2025.11548381

Cross-chemistry battery state-of-health estimation from adaptive partial charging using interpretable physics-informed Efficient Kolmogorov-Arnold networksPreprint

Abebe, Z. Y., Xu, Y., Wei, Y., Li, J., & Jia, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6929718

Performance Assessment of a High-Capacity 120 Gbps Hybrid WDM-Based FSO Link in Weather-Impaired Environments

Ghazi, A., Saeed, M. M., Jalal, S. K., Abdulkareem, D. A., Hama, A. F., Ahmed, M. F., Al-dawoodi, A., & Mahmood, Z. N. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671801021

Novel Lithium-Ion Battery State-of-Health Prediction Method Based on Stage-Aware Learning Model in Electric Vehicle Applications

Liu, Y., Xia, J., Fan, D., Zhao, F., & Wang, B. (2026) · Energy & Fuels · doi:10.1021/acs.energyfuels.6c01419

SOH Prediction Using Impedance Pseudo-Image Features with Transformer-Based Data Enhancement

Xiao, K., Feng, Z., Zhang, Y., Yang, K., Meng, J., Song, Z., Guo, A., & Wang, R. (2026) · IEEE International Electrical and Energy Conference (CIEEC) · doi:10.1109/cieec69682.2026.11571984

Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning AlgorithmPreprint

Sun, P., & Ni, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7051898

Explainable AI-based prediction of lithium-ion battery aging: A comparative study at low and moderate temperatures

Hadji, F., Hadji, A., Belfennache, D. E., Yekhlef, R., Fatmi, M., Alanazi, F. K., Yaylacı, M., & Abualreish, M. J. A. (2026) · Journal of Electroanalytical Chemistry · doi:10.1016/j.jelechem.2026.120421

A novel physics-informed transformer-based hybrid model for satellite on-board battery health prognostics

ZHANG, R., Ruiqi, Y. E., Chongbin, G. U. O., Yang, D. U., Su, B., & Yifu, C. A. O. (2026) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2026.104368

Robust state-of-charge estimation for lithium-ion batteries under dynamic conditions using an enhanced adaptive extended Kalman filtering framework

Liu, C., Zhang, Q., & Chen, C. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123600

Interface-Stabilized CsPbBr3/PbS Heterojunctions Enable Early and High-Precision Capacity Prediction in Lithium-Ion BatteriesPreprint

Nie, S., Mu, Z., Niu, Z., Gu, X., Huang, S. G., Zhu, L., & Wang, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7181938

Review on the application of metaheuristic algorithms in neural network-based methods for lithium-ion battery state estimation and prediction

Liu, J., She, C., Bin, G., Xu, Y., & Zhou, L. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123737

Estimation of SoC, SoH, and RUL in battery management systems using artificial intelligence: A tertiary mapping study

Bastos, M. L., Calazans, M. A. A., dos Santos, M. C. M., Sousa, I. A. M., Borges, L. H. B., & Coelho, L. D. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123693

An adaptive Autoregressive model by particle filtering algorithm for prognostics of lithium-ion batteries

Cheng, J., Gu, Z., & Hu, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123697

Early‐Life State‐of‐Health Prediction of High‐Capacity Pouch Lithium‐Ion Batteries Utilizing Electrochemical Impedance Spectroscopy and Gaussian Process Regression

Tingbari, V. M., Lee, J., Oh, J., & Kim, J. (2026) · International Transactions on Electrical Energy Systems · doi:10.1155/etep/6412152

Efficient battery electrolyte design for electric aircraft driven by physics-augmented AI

Yi, B., Ma, X.-Y., Yang, X.-G., & Song, Z. (2026) · Joule · doi:10.1016/j.joule.2026.102601

End-of-Life Electric Vehicle Battery Disassembly Enabled by Intelligent and Human–Robot Collaboration Technologies

Li, W., Peng, Y., Zhu, Y., Pham, D. T., Nee, A. Y. C., & Ong, S. K. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_5

Deep ensemble learning for state-of-health estimation of lithium-ion batteries considering random sensor data unavailability

Qiu, Z., Tan, Q., Chen, C., & Wei, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123694

Impedance-based in-situ state of health and state of charge estimation for enhanced battery converter performance

Qiu, S., & Rajashekara, K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124028

Artificial intelligence in electric vehicles: A critical review of technological integration, grid interdependency, environmental sustainability, and policy frameworks

Mishra, A. K., Amar, T. A., Sahini, D., & Chaubey, A. K. (2026) · Hybrid Advances · doi:10.1016/j.hybadv.2026.100724

Interfacial design principles of two-dimensional graphene-based heterostructures for lithium- and sodium-ion battery anodes

Rehman, J., Shahzad, R., Zafer, T., Younis, U., & Dinh, V. A. (2026) · Journal of Materials Chemistry A · doi:10.1039/d6ta03712g

A Comparative Machine Learning-based State-of-Charge Estimation of Lithium-Ion Batteries using OCV-Corrected Reference Modeling and Multi-Domain Feature Engineering

Sudharshana, Bhat, S., & K, B. M. (2026) · International Conference on Intelligent and Sustainable AI Systems (ICOSAAS) · doi:10.1109/icosaas68663.2026.11648657

A feature-dimension-adaptive physics-informed neural network for cross-operational state of health prediction of lithium-ion batteries

Wang, Z., Wang, S., Yang, M., Sun, Z., Yang, Q., Guo, D., & Liu, Q. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124181

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Huang, T., Sun, B., Chen, Z., Zhao, C., Bakker, E., Lei, Y., Zhao, L., Wang, S., Liu, C., He, V. X., et al. (2026) · Advanced Intelligent Discovery · doi:10.1002/aidi.70153

Principles and Research Progress of Lithium Battery Management Systems for New Energy Vehicles

Peasah, G. M., Amadah, D. S., Yao, S., & Yang, J. (2026) · Journal of Power and Energy Engineering · doi:10.4236/jpee.2026.148001

Temperature-resilient state-of-health estimation for electric vehicles using domain-corrected fast-charging features

Kang, M., Park, S., 유동근, Ko, H., Kim, W., Kim, N., & Kang, B.-S. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.241430

Machine-Learning-Assisted Multi-Energy Coupling and Battery–Grid Coordination for Deep Decarbonization of Smart Integrated Energy Systems: Modeling, Optimization, and Applications

Tong, Y., Ma, H., & Du, F. (2026) · Batteries · doi:10.3390/batteries12090341 · Cited by 1

A multi-task general model for lithium-ion battery health management

Feng, F., Zhang, Y., Li, H., Xie, J., 范天娥, Chen, B., Chen, M., & Dong, L. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113398

Bayesian Analysis of Li-Ion Battery Cells Discharge Measurements

Andabak, L., Sarajčev, P., & Garma, T. (2026) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech70133.2026.11685552

Trustworthy AI-Guided Transfer Learning for Cross-Architecture Prediction of Solid Oxide Fuel CellsPreprint

Li, S., Li, J., Yang, Q., Zhou, W., & Yao, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7477809

Meta-learning based on Hypernetworks for State of Health prediction of lithium-ion batteries under data scarcity

Guo, Q., Grimaccia, F., Niccolai, A., & Tu, R. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128910

Modeling of non-linear dynamics and degradation characterization of lithium-ion batteries using delay-embedded dynamic mode decomposition

Labib, K. M., & Ahmed, S. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124567 · Cited by 2

Physics-informed entropy temporal neural network for cross-condition state-of-health estimation of lithium-ion batteries

Du, H., Ma, S., Xu, C., Xu, X., & Ma, S. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124660

Towards interpretable and trustworthy lithium-ion battery prognostics and health management with physics-informed machine learning

Wang, H., Zhu, T., Cao, Z., & Wen, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124442

A physics-informed framework with proximal policy optimization for lithium-ion battery state-of-health estimation

Xu, Z., Chen, N., Tang, X., Wang, Z., & Wang, Z. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124726

Acoustic Fingerprint Identification of Lithium-Ion Battery Thermal Runaway and Application in Fire Investigation

Yang, C., Tang, J., Chen, W., & Kong, S. (2026) · Safety · doi:10.3390/safety12050121

Translating frequency-resolved impedance into irreversible Joule heating under coupled thermal and cyclic ageing in batteriesPreprint

Berwal, P., & Yoh, J. J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7507388

Machine learning enabled cross-stage prediction of cathode performance using manufacturing parameters and measured electrode descriptors

Zhao, Z., Leung, C. L. A., & Huang, C. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124825

A physics-guided transformer for robust state of health estimation in real-world electric vehicles

Guo, L., Li, Y., & Zhang, W. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124747

Research on state of health prediction of lithium-ion batteries based on sparrow algorithm and dynamic physical constraint neural network

Lu, J., Huo, X., Guo, M., Dong, H., & Wang, K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124823

Artificial intelligence driven lithium-ion battery recycling: from materials recovery to intelligent circular energy systems

Aslam, J., Khan, M. A., Usman, A., Javed, M., & Usman, M. (2026) · Journal of Materials Science Materials Theory · doi:10.1186/s41313-026-00096-2

Cross-chemistry battery state-of-health estimation from adaptive partial charging using interpretable physics-informed Efficient Kolmogorov–Arnold networks

Abebe, Z. Y., Xu, Y., Wei, Y., Li, J., & Jia, B. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100928

Closing the learning loop in lithium–sulfur batteries through physics-informed and trustworthy artificial intelligence

Li, K. C., King-cheong Antony, L. A. M., Wong, C. H., & Wai Kwan, L. I. U. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100934

Physics-driven deep learning for inverse extraction of capacitive parameters and faradaic contribution in supercapacitors

Arfaoui, M., & Pech, D. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100935

Task-aware sparse EIS frequency selection for joint SOC classification and SOH estimationPreprint

Gholami, M. M., & Valujerdi, A. R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7576910

A Holistic Lifecycle-Oriented Analysis of High-Performance Lithium-Ion Batteries: Aging, Advanced Diagnostics, Thermal Management, and Digitalized Education

Boya, M., Fechtner, H., Popp, A., Brueggemann, T., Tekaya, K., Klewicz, N., Mostafa, A., & Schmuelling, B. (2026) · Energies · doi:10.3390/en19204765

Multi-Task Deep Learning Framework for Smartphone Battery Health Prediction and User-Centric Action Recommendation

Singh, J. P., Ghosh, D., Upadhyay, L., & Singh, J. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11717043

Physics-informed learning of health-aware equivalent circuit parameter evolution for lithium-ion batteries

Zhang, T., Yu, H., Yu, Y., Wang, Y., Wang, J., & Chen, L. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.125097

Physics informed time varying covariates and endogeneity aware survival learning for lithium ion battery life predictionPreprint

Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7601830

TinyBatteryNet: A compact neural architecture for embedded battery remaining useful life prediction across battery domains

Le, M., Vo, H. T., & Vu, H. H. T. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129032

Zero-shot state of health estimation from partial charging segments via a chemistry-gated graph transformerPreprint

Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7606527

Physics constrained residual diffusion for battery degradation trajectory generation and risk scenario analysisPreprint

Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7611886

Compact early-to-middle-cycle descriptors reveal empirical charge–discharge imbalance as a predictor of long-horizon lithium–sulfur battery degradation

Di, Z., Hu, X., Wong, C. H., Leung Yuk Frank, L. A. M., & Zakharov, A. P. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100941

State-of-health estimation of lithium-ion batteries from random local charging fragments: A reliability-aware local-global fusion framework

Zhang, C., Cao, Q., Qi, Z., Huang, Q., Qiu, Z., Zhang, J., & Ru, Q. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129134

Learning invariant causal features from partial-charge segments for domain-generalized battery state-of-health predictionPreprint

Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7641880

Artificial Intelligence for Lithium-Ion Battery Failure Analysis in Electric Vehicles: A Critical Review

Sang, J., Hong, Z., Yi, Z., Xu, W., Lin, S., Wan, X., Xu, W., & Zhao, H. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17100548

Accurate remaining useful life prediction of lithium-ion batteries via capacity curve modeling with attention fusion learning framework

Li, X., Li, J., Li, J., Wang, F., Han, Y., Liu, M., & Zhao, Y. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129061

In Situ Implantable CsPbBr3/ZnO/Ag2Te Multidimensional Heterostructure Sensor for Machine Learning-Driven State Prediction in Lithium Metal Batteries

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