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

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Optimization of battery state of charge estimation method by correcting available capacity

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Battery state estimation methods

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Lithium-Ion Battery Aging Analysis of an Electric Vehicle Fleet Using a Tailored Neural Network Structure

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Modelling Data-Driven Digital Twins of EV Batteries for Predictive Analytics

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AI-Driven Battery State-of-Charge Estimation using Electrochemical Impedance Spectroscopy

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Accurate Prediction of Electrochemical Degradation Trajectory for Lithium‐Ion Battery Using Self‐Discharge

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Design of Low-Resistance Composite Electrolytes for Solid-State Batteries Based on Machine Learning

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A Comparison of Baseline Models and a Transformer Network for SOC Prediction in Lithium-Ion Batteries

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

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Robust Fuzzy Entropy-Based SOH Estimation for Different Lithium-Ion Battery Chemistries

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Secondary Structural Ensemble Learning Cluster for Estimating the State of Health of Lithium-Ion Batteries

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ShotAdpt: Few-Shot Early Prediction of Battery Cycle Life With Adaptive Feature Reduction

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Embedded Sensing: The Neural Frontier and Early-Warning Revolution in Battery Safety Monitoring

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Life prediction models for lithium-ion batteries: A multidimensional analysis from aging mechanism to model application

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Explainable AI for remaining useful life prediction in industrial systems: a survey

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MI-SOH: a multi-indicator feature dependency model for lithium-ion battery state-of-health Estimation

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ConTriFormer: triggers-guided contextual informer for remaining useful life prediction of rolling bearings

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Improving diagnostics and prognostics of implantable cardioverter defibrillator batteries with interpretable machine learning models

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The explainable uncertainty in degradation process: a discovery from non-accelerated batteries degradation experiment

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Machine learning for solid mechanics

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Data-Driven Estimation of Li-Ion Battery Health using a Truncated Time-based Indicator and LSTM

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DAE-Transformer-based Remaining Useful Life Prediction for Lithium-Ion Batteries in Energy Storages

Huang, B., Zeng, Z., Zhou, Y., Liu, J., Zheng, Q., Wang, L., Feng, Z., Sun, S., & Pan, Z. (2023) · International Conference on New Energy and Power Engineering (ICNEPE) · doi:10.1109/icnepe60694.2023.10429188 · Cited by 4

State of health as a function of voltage hysteresis in Li-ion battery half-cells

Hodakovska, J., Britala, L., Mezulis, A., Grīnberga, L., Bajārs, G., & Kučinskis, G. (2024) · Journal of Solid State Electrochemistry · doi:10.1007/s10008-024-05944-0 · Cited by 4

Prior-based patch-level representation learning for electric vehicle battery state-of-charge estimation across a wide temperature scope

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OSE: On-Site State-of-Health Estimation for Li-Ion Battery Using Real-Time Field Data

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Prediction Model of Capacity Degradation in Lithium-Ion Batteries Based on Fatigue Damage Theory and Electrochemical Impedance Spectroscopy

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Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model

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Modeling of Traction Batteries for Rail Applications Using Artificial Neural Networks

Bauer, R., Reimann, S., & Gratzfeld, P. (2021) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec51675.2021.9490180 · Cited by 4

Generalized State of Health Estimation Approach based on Neural Networks for Various Lithium-Ion Battery Chemistries

Bockrath, S., & Pruckner, M. (2023) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3575813.3595207 · Cited by 4

A Behavioral Model for Lithium Batteries based on Genetic Programming

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An Analytical Model for Lithium-Ion Batteries Based on Genetic Programming Approach

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Conversion of Natural Biowaste into Energy Storage Materials and Estimation of Discharge Capacity through Transfer Learning in Li-Ion Batteries

Nanthagopal, M., Mouraliraman, D., Han, Y.-R., Ho, C. W., Obregon, J., Jung, J., & Lee, C. W. (2023) · Nanomaterials · doi:10.3390/nano13222963 · Cited by 4

A coarse-to-fine ensemble method for capacity prediction of lithium-ion batteries in production

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A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

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Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery

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DeepDeg: Forecasting and explaining degradation in novel photovoltaicsPreprint

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Framework for Selecting the Most Effective State of Health Method for Second-Life Lithium-Ion Batteries: A Scientometric and Multi-Criteria Decision Matrix Approach

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A method for predicting lithium-ion battery capacity using an enhanced LightGBM model

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Training-free state of health and state of charge estimation of batteries using the theory of evidence

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Large battery model for multi-state co-estimation and intelligent recommendation using mixed data sources

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Suppressing dendrites in lithium metal anodes: A review of passivation layers and multiscale computational approaches

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Ensemble Learning-Based Battery Health Estimation: A Comparative Study of RUL, SOC, and SOH Prediction Using Machine Learning Models

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Multiscale dilated attention convolutional neural network for remaining useful life prediction of rolling bearings in noisy environments

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Early deployment of deep learning models for lithium-ion battery state-of-health prediction with limited initial data

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A prospective circular economy future with intelligent AI-driven metals end-of-life processing

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Smart battery with intelligent management system: A lifespan perspective

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A multimodal educational robots driven via dynamic attention

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Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling

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U + LSTM-F: A data-driven growth process model of rice seedlings

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State of Charge Prediction of EV Li-ion Batteries with Machine Learning: A Comparative Analysis

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Gaussian Process Regression for State of Charge Prediction: Overcoming Temperature Variability in Li-ion Battery Applications

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Sensorless temperature estimation of lithium-ion batteries by integrating physics with the state-dependent model

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Prediction of State of Charge (SOC) of Battery Electric Vehicle

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Accelerating the Design of Automotive Catalyst Products Using Machine Learning

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Rapid High-Precision Diagnosis of the Capacity and Internal Resistance of Lithium-Ion Batteries Using Impedance Measurements

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Two-dimensional Modeling and Analysis of Lithium-ion Cell for Electric Vehicle Application

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A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

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Battery Capacity Trajectory Prediction with Multi-output Gaussian Process

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Advancing Lithium-Ion Battery Management with Deep Learning: A Comprehensive Review

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Trajectory-Based State-of-Charge Prediction Using LSTM Recurrent Neural Networks

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Tackling Limited Labeled Field Data Challenges for State of Health Estimation of Lithium-Ion Batteries by Advanced Semi-Supervised Regression

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Refining State-of-Charge Estimation for Battery Energy Storage System using Historical Operating Data

Xiao, L., Lin, D., Zhang, X., Li, Z., & Jiang, Q. (2021) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee51499.2021.9436838 · Cited by 3

Optimized Embedded Architectures for Model Predictive Control Algorithms for Battery Cell Management Systems in Electric Vehicles

Madsen, A. K. (2020) · Digital Collections of Colorado (Colorado State University) · Cited by 3

Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks.Preprint

Salucci, C. B., Bakdi, A., Glad, I. K., Vanem, E., & Bin, R. D. (2021) · arXiv (Cornell University) · Cited by 3

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

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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 · Cited by 3

Interpretable hybrid machine learning demystifies the degradation of practical lithium-sulfur batteries

Seh, Z. W. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.12.003 · Cited by 3

TFDSUNet: Time-Frequency Dual-Stream Uncertainty Network for Battery SOH/SOC PredictionPreprint

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Semi-Supervised Deep Learning for Lithium-Ion Battery State of Health Estimation Using Dynamic Discharge ProfilesPreprint

Xiang, Y., Fan, W., Zhu, J., Wei, X., & Dai, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4529651 · Cited by 3

Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data

Bae, Y. S., Lee, S., & Moon, J. (2024) · Batteries · doi:10.3390/batteries10110389 · Cited by 2

Enhancing Li-Ion Battery State-of-Charge Prediction for Electric Vehicles Using Deep Learning Models

Sahbani, A., Cherif, K., & Saad, K. B. (2024) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa63423.2024.10912587 · Cited by 2

Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries

Yang, D., He, W., & He, X. (2025) · Energies · doi:10.3390/en18082105 · Cited by 2

Unsupervised battery pack fault detection for electric vehicles based on partial charging segments and multi-algorithm fusion

Chao, P., Xu, Y., Li, J., Zhou, J., Xiang, F., Zheng, M., Dai, H., & Hu, X. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118916 · Cited by 2

A Generalized Degradation Model Based on Semi-Physics-Informed Neural Stochastic Differential Equation

Wang, Z., Chen, Z., Xia, T., & Pan, E. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3620010 · Cited by 2

Predicting dendrite growth in lithium metal batteries through iterative neural networks and voltage embedding

Kim, S. Y., Kwon, S. W., Bashir, M. N., & Lee, J. S. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01824-x · Cited by 2

A robust and novel approach to joint SoC and SoH estimation for Li-ion batteries with experimental validation

Kumar, A., Samanta, S., Radhakrishnan, R., & Patel, H. K. G. (2025) · International Journal of Dynamics and Control · doi:10.1007/s40435-025-01978-y · Cited by 2

Design of a Li-ion battery cooling system incorporating PCM, heat pipes, and liquid circuits using marine predator algorithm-enhanced ANN and multi-verse optimization

Ali, N. B., Louhichi, B., Hassan, W. H., Alizadeh, A., Hussein, A. A., Aich, W., Hajlaoui, K., & Aminian, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-41155-5 · Cited by 2

Artificial intelligence-driven paradigms for layered cathode materials: A systematic framework for multi-scale engineering

Gao, J., Huang, T., Zhang, Y., Chen, B., Ren, X., Hu, J., Zhang, Q., & Xie, H. (2026) · Nano Energy · doi:10.1016/j.nanoen.2026.111978 · Cited by 2

Sustainable future strategies towards the reuse and recycling of lithium-ion batteries: a concise account of materials and methods

Altaf, I., Hussain, S., Akhter, R., & Maktedar, S. S. (2026) · Sustainable Energy & Fuels · doi:10.1039/d5se01218j · Cited by 2

Exploratory study of battery aging analysis with machine learning models to complete multi-physical ones for more adaptable systems

Challier, L., Ndour, G., Garrigos, M., Chouchane, K., Conand, C., Lavaste, V., & Alliod, C. (2022) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3529399.3529402 · Cited by 2

State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method

Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. D. (2022) · Mechanisms and machine science · doi:10.1007/978-3-030-99075-6_57 · Cited by 2

A Survey on Battery State of Charge and State of Health Estimation Using Machine Learning and Deep Learning Techniques

Reddy, M. S., & Monisha, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6088-8_31 · Cited by 2

SOC Estimation of Lithium Battery Multi-cells using Neural Networks

Lee, J.-H., & Lee, I.-S. (2023) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2023.21.5.49 · Cited by 2

SOC Online Estimation Error Correction Algorithm Based on Particle Swarm Optimization Particle Filter Algorithm

Lin, Q., Liu, D., Wang, S., & Xiao, W. (2023) · New Energy and Energy Storage System Control Summit Forum (NEESSC) · doi:10.1109/neessc59976.2023.10349317 · Cited by 2

Development of Short-Term Load Forecasting Model for DSM by Regulating Charging and Discharging of EVs and ESS with RES Based on the Prediction of this Model

Paul, W. U. H., Siddiqui, A. S., & Sheeraz, K. (2023) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.166.08 · Cited by 2

Overview of Li-ion battery degradation

Dai, H., & Zhu, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15543-7.00002-9 · Cited by 2

Class Probability-guided Ensemble Learning-based Semantic Segmentation to Predict Cancerous Regions on Hematoxylin and Eosin-stained Images

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Computational design of nanostructured materials for battery applications

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