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

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Machine Learning Approaches for Designing Electrode Materials for Lithium‐Ion Batteries

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Aging feature Extracting Strategy Considering Battery Usage Conditions and Optimized ELM-Based Accurate SOH Estimation

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An Indirect Prediction Method based on Feature Extraction of Lithium-ion Batteries Remaining Useful Life

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Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition

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Prediction of State of Charge (SOC) of Electric Vehicle (EV) Batteries using Machine Learning Technique

Selva Vengatesh, M., Nishanthy, J., Charles Raja, S., & Arul Jeyaraj, K. (2023) · International Conference on Energy, Materials and Communication Engineering (ICEMCE) · doi:10.1109/icemce57940.2023.10434077 · Cited by 1

Clustering Features of Lithium-Ion Battery Capacity Degradation Curve

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A Method Based on Time-frequency Disentanglement and LSTM for Remaining Useful Life Prediction of Lithium-Ion Battery

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Predicting the surface roughness of an electrodeposited copper film using a machine learning technique

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Remaining Useful Life Prediction of Li-Ion Batteries by Multi-modal Attention Fusion Network

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Multi-category health indicator fusion-based state of health forecast of lithium-ion batteries with high generality to partial discharging profiles

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A Scalable Recurrent Structure With Fast Transfer Learning for Lithium-Ion Battery State of Charge Estimation at Different Ambient Temperatures

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A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction

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Deep-learning-assisted high-throughput discovery of metallophilic MA 2 Z 4 nanomaterials

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State of charge estimation of lithiumion batteries with heavy-tailed noise based on multivariate laplace robust filter

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L-TKAN:A fast and accurate Laplacian radial basis function-Based Temporal Kolmogorov–Arnold Network for state of charge estimation of lithium-ion batteries

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Data‐Driven Machine Learning Model for Battery Life Prediction Across Electrode Materials

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Impact of Input Data Augmentation on Lithium-Ion Battery SOC Estimation: Comparison Between LSTM and GRU

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Attention-Based Multimodal Transformer-LSTM Fusion Networks for Enhanced Lithium-ion Battery State-of-Charge Prediction with Aging Pattern Consideration

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Lifecycle management framework for used electric vehicle batteries

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Few-Shot Battery State-of-Health Estimation via Meta-Learning and Cross-Domain Adaptation

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A lightweight hierarchical temporal convolution network for robust state of health estimation of lithium-ion batteries

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Physics-constrained deep learning with conformal prediction for early knee-point prediction in lithium-ion batteries

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Chemometric Models and Electrochemical Techniques for Studying the Health of Li-Ion Batteries: A Review

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Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra

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Observer-guided deep learning for joint state of charge and state of health estimation of lithium-ion batteries

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Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation

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State of health estimation of lithium-ion batteries based on real-world charging behavior analysis and phase space topology of fragmented data

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Development of a hybrid approach for real-time SOC estimation in renewable energy systems

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Sparse electrochemical impedance spectroscopy for online battery SOH monitoring

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Artificial intelligence for energy storage systems: State of the art and future perspectives

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Closing the loop with generative AI and automated experimentation in electrochemical energy innovation

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Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

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Variability Analysis of Battery EIS Measurements

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Artificial intelligence-driven electrolyte chemistry for lithium-ion batteries: mechanisms, progress and strategic perspectives

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Investigation of battery-aging dynamics for electric vehicle management under real-world driving stress

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Disruptive Generational Leap: An Embedded AI Battery Management System for Power and Energy Storage Systems

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Machine learning-assisted health management of retired batteries

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A scalable multi-source aging coupling framework for high-fidelity electrochemical and thermal modeling of lithium-ion batteries

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Application of a hybrid between Residual RNN and Regression Methods in Predicting Battery State of Health in Autonomous Aircrafts

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On Learning Data-Driven Models For In-Flight Drone Battery Discharge Estimation From Real Data

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Li-ion Battery Energy Storage Management System for Solar PV

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Practical battery State of Health estimation using data-driven multi-model fusion

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Predicting Remaining Discharge Time for Lithium-ion Batteries based on Differential Model Decomposition

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SOC Estimation of Supercapacitor Balancing Systems: A Switching Kalman Filter Approach

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A simulation study of touch-free automatic alcohol-based handrub dispensers on hand hygiene disruption in healthcare settings

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Optimization of the deep extreme learning machine using the seagull algorithm for state of health prediction of lithium batteries

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Digital Twin Technology as Facilitator to Sustainable and Energy-Efficient Ecosystem

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Predictive System for Early Failure Detection in EV Battery Bank Cells

Bairwa, B., Chandrika, B. C., Monisha, H. A., Gowda, N. B., & Ullas, B. (2025) · International Conference on Advances in Renewable Energy & Electric Vehicles (AREEV) · doi:10.1109/areev64307.2025.10925196 · Cited by 1

Accurate classification of materials with elEmBERT: Element embeddings for chemical benchmarks

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Machine Learning and Genetic Programming-based behavioral modeling approaches of Li-ion Batteries

Capua, G. D., Molinara, M., Maffucci, A., Porpora, F., Femia, N., & Oliva, N. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11043719 · Cited by 1

Early Prediction of Battery Cycle Life Using Machine Learning

Liu, W. (2021) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 1

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A Review on The Application of Machine Learning To Predict The Battery State That Enables A Smart, Low-Cost, Self-Sufficient Drying And Storage System for Agricultural Purposes

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A Flexible Battery Capacity Estimation Method Based on Partial Voltage Curves and Polynomial FittingPreprint

Cao, M., Liu, Y., Zhang, T., Wang, Y., Zhu, W., & Shi, Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4199925 · Cited by 1

A Review of the Convergence Between Explainable Artificial Intelligence and Multi-Objective OptimizationPreprint

Monteiro, W. R., & Reynoso-Meza, G. (2022) · doi:10.36227/techrxiv.21707261.v1 · Cited by 2

State of Health Estimation for Lithium-ion Batteries Based on Mechanism Fundamental Learning under Variable Charging StrategiesPreprint

Zhang, C., Li, S., Shang, Y., Duan, B., Liu, K., Bi, Z., Zhao, G., Li, C., & Wang, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3486377/v1 · Cited by 1

Machine Learning‐Based Assessment and Optimization of Electrode Materials for Supercapacitors

Moharana, S., Sahu, B. B., Meher, J. K., Nayak, R., Mahaling, R. N., Dhandapani, K., & Deshmukh, K. (2024) · doi:10.1002/9783527838851.ch25 · Cited by 1

Renewable Energy System Protection and Coordination

Araneo, R., & Mitolo, M. (2021) · doi:10.1002/9781119625056.ch5

LSTM-based Multi-Step SOC Forecasting of Battery Energy Storage in Grid Ancillary ServicesPreprint

Ardiansyah, A., Kim, Y., & Choi, D. (2021) · doi:10.36227/techrxiv.15124416

Data-based Polymer-Unit Fingerprint (PUFp): A Newly Accessible Expression of Polymer Organic Semiconductors for Machine LearningPreprint

Ye, C., Zhang, X., Wei, G., Sheng, Y., Yang, J., & Zhang, W. (2022) · Research Square · doi:10.21203/rs.3.rs-1813671/v1

Developing an Inference Engine for Estimating State of Charge of the Lead Acid Battery

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The Application of Novel Functional Materials to Machine Learning

Khan, H. R., Khan, F. S., & Akhtar, J. (2023) · Springer eBooks · doi:10.1007/978-981-99-0393-1_5

Knowledge-based and data-driven behavioral modeling techniques in engagement simulation

Zhu, Z. H., Wang, T., Sarjoughian, H. S., Wang, W., & Zhao, Y. (2023) · SIMULATION · doi:10.1177/00375497231177123

Data-driven approaches enabling the screening and design of promising materials for energy storage batteries

ZHANG, Q., Peng, C., & Xue, D. (2023) · Scientia Sinica Technologica · doi:10.1360/sst-2023-0157

hILDe: AI-Empowered Monitoring System for Vanadium Redox Flow Batteries

Kiefer, G.-L., Ndiaye, A., Deru, M., Brandherm, B., Gerart, L., Schulte, S., Groß, B., Durneata, D., & Hempelmann, R. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-36001-5_63

A Predictive and Analytical Approach for Estimating the Remaining Operational Time of Electric Vehicle BatteriesPreprint

Jafari, S., & Byun, Y.-C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4525452

State of Charge Estimation of Lithium Iron Phosphate Battery Using Bidirectional Long Short-Term Memory

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Efficient estimating and clustering Lithium-ion batteries with a deep-learning approachPreprint

He, X., Sun, Z., He, W., Li, D., Geng, X., Yang, D., Wang, H., Hu, L., & Tu, H. (2024) · Research Square · doi:10.21203/rs.3.rs-3927899/v1

An Assessment of Accurate Range Estimator for Electric Vehicle Applications

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Research on Estimating the State of Heath of Retired Power Batteries Based on the Distribution of Relaxation Times MethodPreprint

Zhang, L., Hong, X., Xu, W., & Ruan, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4753883

Remaining Useful Life Prediction of Ev Lithium-Ion Batteries Based on Vmd Decomposition and Bo-GruPreprint

Tang, W., Chen, J., & Chen, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4753828

Extract Features from Lithium-Ion Battery Electrochemical Impedance Spectra and Estimate State of Health Based on Improved Convolutional Autoencoder-Temporal Convolutional NetworkPreprint

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Time Series-Based Framework for Global Temperature Forecasting Using Fb-Prophet

Chhabra, A., Sharma, V., Sharma, A., & Singh, S. (2025) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-40905-9_16

Remaining Useful Life Prediction of Railway Balise Based on Wiener Process Under Varying Temperature Conditions

Li, Z., Zhao, Z., Liu, J., Cai, B., & Lu, D. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920228

Battery State of Charge Estimation Based on Improved Neural Network

Zhang, H. (2025) · Journal of Computer Science and Artificial Intelligence · doi:10.54097/9ervp336

Exploring the Potential of Second-Life Batteries for Mobile Charging Infrastructure: A Review

Gautam, M., Wu, D., & Fang, H. (2024) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias55788.2024.11023700

Performance Enhancement of Li Ion Battery through SOC Prediction at Various Temperature: A Machine Learning-based Approach

Mishra, A., Kumar, S., & Verma, V. (2024) · International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED) · doi:10.1109/sustained63638.2024.11073999

SHAP-Enabled Hybrid Physics XGBoost Model for State-of-Power Estimation in Lithium-Ion Batteries for Intelligent Battery Management SystemsPreprint

Kumar, S., Vinod Kumar, D. M., & Deshmukh, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5462179

Decoupling complex cell aging with explainable machine learning: a perspective

Ramesh, H. N., Sun, S., & Cao, G. (2026) · Journal of Physics Energy · doi:10.1088/2515-7655/ae668c

“High-Precision State-of-Charge Estimation for Aged LiFePO₄ Batteries Using a Compact Data-Driven Model for Embedded EV ApplicationsPreprint

Vyskočil, J., Chlad, L., Vlček, M., & Jílek, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5555058

Lithium-ion Battery Life Prediction Based on Time-Series Convolutional-Bi-Directional Gated Cyclic Networks

Su, Y., Liu, Z., Lv, S., Li, S., & Wang, X. (2025) · International Symposium on Semiconductor and Electronic Technology (ISSET) · doi:10.1109/isset66828.2025.11184933

A density-based manifold learning framework for reconstructing high-dimensional dynamical systems with outliers

Xue, Q., Du, L., Jiang, F., Zhang, C.-L., & Grebogi, C. (2025) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2025.134990

Physics-Informed Neural Networks for Real-Vehicle Li-ion Battery SOH Estimation

Yang, Y., Sun, Y., Chen, X., Zhuo, S., Wang, S., & Jiang, F. (2025) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon58223.2025.11221698 · Cited by 1

Predicting State of Charge of Energy Storage Resources Based on Machine Learning

Ma, J., & Du, P. (2025) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm52009.2025.11225341

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