Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

A Mathematical Theory of Evidence

Shafer, G. R. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,760

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531

On sequential Monte Carlo sampling methods for Bayesian filtering

Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,466

Ageing mechanisms in lithium-ion batteries

Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987

A mathematical theory of evidence

Rota, G. (1977) · Advances in Mathematics · doi:10.1016/0001-8708(77)90069-x · Cited by 1,785

Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs

Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768

Prognostics and Health Management of Electronics

PECHT, M. G. (2008) · Wiley eBooks · doi:10.1002/9780470385845 · Cited by 848

Prognostics methods for battery health monitoring using a bayesian framework

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831

A review on prognostics and health monitoring of Li-ion battery

Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792

Identify capacity fading mechanism in a commercial LiFePO4 cell

Dubarry, M., & Liaw, B. Y. (2009) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.05.036 · Cited by 675

A Lithium-Ion Battery SOH Prediction Method: Temporal Dual-Channel Networks Transfer and Error Compensation Under the EMD Framework

Wan, X., Mao, F., Zhao, X., Zhang, C., Hu, W., & Wu, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3538072 · Cited by 12

Enhanced multi-scale signal decomposition transformer neural network for state of health estimation of lithium-ion batteries

Li, Y., Shi, H., Huang, Q., Li, K., Liu, C., Nie, S., Jia, X., & Fernández, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118191 · Cited by 13

A Transfer Gated Recurrent Unit Model for Battery Health Monitoring Under Cross-Domain Conditions

Tian, J., Xie, J., Zhang, J., Huang, C.-S., Jiang, Y., Luo, H., Chow, M., & Yin, S. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3548227 · Cited by 12

A Multiscale Entropy-Based Long Short Term Memory Model for Lithium-Ion Battery Prognostics

Namdari, A., & Li, Z. S. (2021) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm51084.2021.9486674 · Cited by 12

Short-Term Capacity Estimation and Long-Term Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Data-Driven Method

Xia, F., Chen, X., & Chen, J. (2022) · Journal of Energy Engineering · doi:10.1061/(asce)ey.1943-7897.0000865 · Cited by 12

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Wang, H., Ye, X., Li, Y., & Zhu, G. (2023) · Sustainability · doi:10.3390/su15129176 · Cited by 12

Fiber-Bragg-Grating-Based Sensor System to Measure Battery State of Charge Based on a Machine Learning Model

Bandyopadhyay, S., Fabian, M., Li, K., Sun, T. S., & Grattan, K. T. V. (2023) · Batteries · doi:10.3390/batteries9100508 · Cited by 12

Battery state of health estimation using variable projection algorithm based on truncated variable order fractional gradient descent

Cheng, L., Pu, Y., Chen, J., & Liu, Q. (2023) · Ionics · doi:10.1007/s11581-023-05332-0 · Cited by 12

Prediction of Lithium-ion Battery's Remaining Useful Life Based on Relevance Vector Machine

Zhang, Z., Huang, M., Chen, Y., & Zhu, S. (2016) · SAE International journal of alternative powertrains · doi:10.4271/2015-01-9147 · Cited by 12

Accurate Estimation of Battery SOH and RUL Based on a Progressive LSTM with a Time Compensated Entropy Index

Bak, T., & Lee, S. (2019) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2019.v11i1.833 · Cited by 12

An Online State of Health Estimation Method for Lithium-ion Batteries Based on Integrated Voltage

Zhou, Y., Huang, M., & PECHT, M. G. (2018) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2018.8448947 · Cited by 12

High-power lithium-ion battery characterization dataset for stochastic battery modeling

Khan, M. A., Thatipamula, S., Tresca, L., Xu, L., Trewartha, A., & Onori, S. (2025) · Scientific Data · doi:10.1038/s41597-025-05725-y · Cited by 12

Cross‐domain state‐of‐health estimation of Li‐ion batteries based on transfer neural network with soft‐dynamic time warping

Zhang, W., Hu, J., Lin, B., Liu, D., Wang, M., Mu, D., & Lu, Y. D. (2023) · Energy Science & Engineering · doi:10.1002/ese3.1509 · Cited by 12

An On-line SOH estimation method for power battery under low sampling rate

Zhao, L., Wang, Z., Ma, Z., & Li, Y. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110695 · Cited by 11

State of health estimation of lithium-ion batteries based on interval voltage features

Li, Z., Zhang, F., Cai, Z., Xu, L., Shen, S., & Yu, P. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114112 · Cited by 11

Deep learning model-based real-time state-of-health estimation of lithium-ion batteries under dynamic operating conditions

Tang, T., Yang, X., Li, M., Li, X., Huang, H., Guan, C., Huang, J., Wang, Y., & Zhou, C. (2025) · Energy · doi:10.1016/j.energy.2025.134697 · Cited by 11

Particle swarm optimization-gated recurrent unit neural network lithium battery state of health estimation based on feature optimization selection strategy

Zhang, L., Yang, K., & Zhang, X. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.237798 · Cited by 11

A novel method for state of health estimation of lithium-ion batteries based on improved TimesNet and health indicators extraction

Wang, S., Zhang, H., Cai, S., Yin, Q., & Zeng, L. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118059 · Cited by 11

Health Monitoring of Lithium-Ion Batteries

Sood, B., Hendricks, C., Osterman, M. D., & PECHT, M. G. (2014) · EDFA Technical Articles · doi:10.31399/asm.edfa.2014-2.p004 · Cited by 11

Enhanced Whale Optimization Algorithm with Wavelet Decomposition for Lithium Battery Health Estimation in Deep Extreme Learning Machines

Wang, H., Luo, J., Zhu, G., & Li, Y. (2023) · Applied Sciences · doi:10.3390/app131810079 · Cited by 11

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

Song, W., Chen, J., Wang, Z., Kudreyko, A. A., Qi, D., & Zio, E. (2023) · Fractal and Fractional · doi:10.3390/fractalfract7110827 · Cited by 11

Aging trajectory and end-of-life prediction for lithium-ion battery via similar fragment extraction of capacity degradation curves

Zhang, S., Wu, S., Cao, G., Chen, S., Wang, Z., & Wang, N. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.140686 · Cited by 11

Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits

Li, Q., Zhong, J., Du, J., Yi, Y., Tian, J., Li, Y., Lai, C., Lu, T., & Xie, J. (2024) · Energy · doi:10.1016/j.energy.2024.130881 · Cited by 11

Remaining Useful Life Prediction of Li-Ion Battery Based on Charge Voltage Characteristics

Sim, S. H., Gang, J. H., An, D., Kim, S. I., Kim, J. Y., & Choi, J. H. (2013) · Transactions of the Korean Society of Mechanical Engineers B · doi:10.3795/ksme-b.2013.37.4.313 · Cited by 11

Evaluation of Lithium Batteries Based on Continuous Hidden Markov Model

Lin, Y., Hu, M., Yin, X., Guo, J. H., & Li, Z. S. (2017) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c.2017.43 · Cited by 11

Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

Gonzalez, E. L., Desforges, X., & Archimède, B. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.08.005 · Cited by 11

Prognostics of lithium-ion batteries using a deterministic Bayesian approach

Fangdan, Z., Jiang, J., Zaidan, M. A., He, W., & PECHT, M. G. (2015) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2015.7245037 · Cited by 11

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

García, E., Quiles, E., Correcher, A., & Morant, F. (2019) · Sensors · doi:10.3390/s19204480 · Cited by 11

Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter

Wang, S., Han, W., Chen, L., Zhang, X., & PECHT, M. G. (2020) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331220961576 · Cited by 11

Remaining Useful Life Prognostics for Lithium-ion Battery Based on Gaussian Processing Regression Combined with the Empirical Model

Yin, S., Pang, J., Liu, D., & Peng, Y. (2013) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2013.v5i1.2179 · Cited by 11

Nonlinear autoregressive models for high accuracy early prediction of Li-ion battery end-of-life

Shah, A. A., Shah, N., Luo, L., Xing, W. W., Leung, P., Zhu, X., & Liao, Q. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109014 · Cited by 11

Multivariate prognosis of battery advanced state of health via transformers

Paulson, N. H., Kubal, J. J., & Babinec, S. J. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.101928 · Cited by 11

A novel model-data fusion method for capacity and battery remaining useful life prediction

Zhou, D., Zhu, Z., Li, C., Jiang, W., Ma, Y., Lu, J., Li, S., & Wang, W. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112929 · Cited by 10

FPCA-SETCN: A Novel Deep Learning Framework for Remaining Useful Life Prediction

Chen, J., Wen, Y., Sun, X., Zeb, A., Meiabadi, M. S., & Karganroudi, S. S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3447717 · Cited by 10

Fuzzy information granulation for capacity efficient prediction in lithium-ion battery

Ouyang, T., Wang, C., Jin, S., & Su, Y. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115241 · Cited by 10

Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory

Li, T., Liu, J., Thelen, A., Mishra, A. K., Yang, X., Wang, Z., & Hu, C. K. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125703 · Cited by 12

Application of a Transformer network based on multi-scale branches and fast Fourier gating mechanism in the state of charge prediction for sodium-ion batteries

Liu, Z., Ruan, G., Tian, Y., Hu, X., An, Z., & Yang, K. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127931 · Cited by 10

Deep Transfer Learning Technology-Based Condition Monitoring and Fault Diagnosis of Electric Vehicle Electric Powertrain Systems: A Review

Xu, H., Liu, Z., Jiang, D., Qu, R., & Tian, J. (2025) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2025.3595437 · Cited by 10

Meta Learning Based State of Health Estimation of Lithium-Ion Batteries With Small Sampling Retraining

Shu, X., Yang, H., Chen, Z., Liu, Y., Tang, A., & Shen, J. (2025) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2025.3613630 · Cited by 10

Generalized foundation model for lithium-ion battery state-of-health prediction with distribution metric learning

Gong, Z., Li, X., Zhang, W., & Yang, S. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120566 · Cited by 10

Exploiting physics-knowledge from unlabeled data to enhance battery lifetime prediction

Tang, A., Li, Y., Tian, J., Yu, Q., Yu, N., & Xu, Y. (2026) · eTransportation · doi:10.1016/j.etran.2026.100560 · Cited by 10

Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach

Lü, C., Zhang, L., Ma, J., Chen, Z., Tao, L., Su, Y., Chong, J., Jin, H., & Lin, Y. (2017) · Energy · doi:10.1016/j.energy.2017.06.167 · Cited by 10

Data-Driven State of Health Estimation for Lithium-Ion Batteries Based on Universal Feature Selection

Li, Y., Huang, P., Gao, L. T., Zhao, C., & Guo, Z. (2023) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/acc696 · Cited by 10

Degradation Trajectory Prognostics for Lithium-Ion Batteries Under Varied Operating Conditions With a Hybrid-Driven Method

Zhang, Y., Wang, Y., Xia, Y., & Chen, W. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3317473 · Cited by 10

Reliability analysis of inverse Gaussian processes with two-stage degenerate paths

Liang, Y., Yan, Z., & Sun, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34625 · Cited by 10

Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

Ossai, C. I. (2018) · Batteries · doi:10.3390/batteries4020015 · Cited by 10

Predicting Battery Aging Trajectory via a Migrated Aging Model and Bayesian Monte Carlo Method

Tang, X., Yao, K., Zou, C., Liu, B., & Gao, F. (2019) · Energy Procedia · doi:10.1016/j.egypro.2019.01.320 · Cited by 10

Battery remaining useful life prediction using improved mutated particle filter

Li, J., Zhang, M., Zheng, H. P., & Jie, J. (2020) · Energy Storage · doi:10.1002/est2.218 · Cited by 10

Remaining Useful Life Prediction of Lithium-ion Battery Based on Unscented Kalman Filter and Back propagation Neural Network

Xiao, Z., Fang, H., Li, Z., & Chang, Y. L. (2019) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls.2019.8908952 · Cited by 10

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

El‐Dalahmeh, M., Al‐Greer, M., El-Dalahmeh, M., & Short, M. (2021) · IET Smart Grid · doi:10.1049/stg2.12013 · Cited by 10

Flexible Deep Learning-Based State of Health Estimation of Lithium-Ion Batteries with Features Extracted from Partial Charging Curves

Lai, R., Li, X., & Wang, J. (2024) · Batteries · doi:10.3390/batteries10050164 · Cited by 9

State of charge prediction for lithium-ion batteries based on multi-process scale encoding and adaptive graph convolution

Wang, H., Wei, W., Cui, L., Zhou, G., Xiang, G., & Zhang, Q. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115482 · Cited by 9

A lithium-ion battery remaining useful life prediction model based on multilayer perceptron expert networks and temporal feature composition

Yi, X., Xiao, J., Lei, G., Hu, X., & Feng, Z. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238371 · Cited by 9

Frequency Spectral Prior Guided Graph Learning for Degradation Decoupling in Reliable Battery SOH Estimation

Guo, F., Tao, S., Zhang, J., Zhou, H., Ji, J., Dong, D., & Mo, H. (2026) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2026.100446 · Cited by 10

Degradation-Aware Ensemble of Diverse Predictors for Remaining Useful Life Prediction

Nemani, V. P., Thelen, A., Hu, C. K., & Daining, S. (2022) · Journal of Mechanical Design · doi:10.1115/1.4056149 · Cited by 9

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

Remaining Useful Life Prediction of Lithium‐Ion Batteries Based on a Combination of Ensemble Empirical Mode Decomposition and Deep Belief Network–Long Short‐Term Memory

Wu, T., Cheng, K., Kang, J., & Liu, R. (2024) · Energy Technology · doi:10.1002/ente.202301033 · Cited by 9

Fuzzy logic-controlled online state-of-health (SOH) prediction in large format LiMn2O4 cell for energy storage system (ESS) applications

Kim, J., & Nikitenkov, D. (2014) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2014.6894986 · Cited by 9

On-board capacity estimation of lithium-ion batteries based on charge phase

Zhou, Y., & Huang, M. (2018) · Journal of Electrical Engineering and Technology · doi:10.5370/jeet.2018.13.2.733 · Cited by 9

Reliability Analysis for Multi-Phase Wiener Processes Considering Phase- Varying Nonlinearity

Wang, Z., Zhang, A., Liu, C., & Wu, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3044681 · Cited by 9

Prediction method for propagating crack length of carbon-fiber-based composite double cantilever beam using its electromechanical behavior and particle filter

Lee, I. Y., Roh, H. D., & Park, Y.-B. (2021) · Composite Structures · doi:10.1016/j.compstruct.2021.114650 · Cited by 9

A migration-based method for non-invasive revelation of microscopic degradation mechanisms and health prognosis of lithium-ion batteries

Xu, R., Wang, Y., & Chen, Z. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105769 · Cited by 9

Uncertainty processing in prognostics and health management: An overview

Liu, D., Luo, Y., & Peng, Y. (2012) · Prognostics and System Health Management Conference · doi:10.1109/phm.2012.6228860 · Cited by 9

Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests

Yang, H., Qiu, Y., & Guo, X. (2015) · Energies · doi:10.3390/en81112322 · Cited by 9

Anomaly Detection and Extra Tree Regression for Assessment of the Remaining Useful Life of Lithium-Ion Battery

Ossai, C. I., & Egwutuoha, I. P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44041-1_124 · Cited by 9

Data-driven framework for lithium-ion battery remaining useful life estimation based on improved nonlinear degradation factor

Guo, L., Pang, J., Datong, L., & Xiyuan, P. (2013) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi.2013.6743205 · Cited by 9

Comparing experimental designs for parameterizing semi-empirical and deep learning-based lithium-ion battery aging models

Kröger, T., Maisel, S., Jank, G., Gamra, K. A., Brehler, T., & Lienkamp, M. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114702 · Cited by 8

A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

Cai, L., Yan, J., Jin, H., Meng, J., Peng, J., Wang, B., Liang, W., & Teodorescu, R. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.12.005 · Cited by 8

Early-stage remaining useful life prediction for lithium-ion batteries based on geometric output construction

Lai, X., Qian, L., Tang, X., Zheng, Y., Zhu, J., Sun, T., Shen, K., & Lu, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115792 · Cited by 8

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach

Zhao, S., Sun, D., Liu, Y., & Liang, Y. (2025) · Energies · doi:10.3390/en18051114 · Cited by 8

A Stable Lithium-Ion Battery SOH Estimation Framework for Suppressing Measurement Noise With Unknown Distribution

Ma, W., Xue, J., Li, Y., Guo, P., Liu, X., Wei, Z., Wang, Y., & Chen, B. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3554735 · Cited by 8

End-to-End State-of-Health Prediction of Lithium-Ion Battery via Vision Transformer Enhanced by a Hybrid Scale Information Fusion Network

Yang, Y., Han, B., Liu, Z., & Zhou, H. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/adce38 · Cited by 9

Early life prediction of fast-charging battery based on feature engineering and state space models

Guan, S., He, Z., Ma, S., Dong, Z., & Gao, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116969 · Cited by 8

State of health estimation for lithium-ion batteries based on short-term and global dependency information

Shu, X., Chen, F., Hu, Y., Chen, Z., Liu, Y., Tang, A., Shen, J., & Liu, X. (2025) · Energy · doi:10.1016/j.energy.2025.137319 · Cited by 8

Remaining useful life prediction of lithium batteries based on multiscale feature extraction and multiscale feature aggregation

Geng, J., Tian, L., & Guo, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118296 · Cited by 8

A parallel LTCN-PHA network for remaining useful life prediction of lithium-ion batteries

Yu, J., Cai, Y., Yang, X., & Li, L. (2025) · Energy · doi:10.1016/j.energy.2025.138436 · Cited by 8

Defining an accuracy limit in battery state estimation

Jiang, S., Tao, S., Lee, J., & Moura, S. J. (2026) · Joule · doi:10.1016/j.joule.2026.102342 · Cited by 8

Remaining Useful Life Prediction of Lithium-Ion Batteries using Semi-Empirical Model and Bat-Based Particle Filter

Lian, Y., Wang, J. V., Deng, X., Kang, J., Zhu, G., & Xiang, K. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180516 · Cited by 8

The particle swarm optimization multi-kernel relevance vector machine for remaining useful life prediction of lithium-ion batteries

Tian, Q., Liu, X., Ding, S., Chen, H., Huang, J., & Yang, Z. (2022) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac54830.2022.9929806 · Cited by 8

Data-Driven Prediction of Li-Ion Battery Degradation Using Predicted Features

Xing, W. W., Shah, A. A., Shah, N., Wu, Y., Xu, Q., Rodchanarowan, A., Leung, P., Zhu, X., & Liao, Q. (2023) · Processes · doi:10.3390/pr11030678 · Cited by 8

Lifespan prediction of Li-ion batteries in electrical vehicles by applying coulombic efficiency: from anode material to battery cell to vehicle application

Liu, X., Li, Y., Jiang, X., & Xu, K. (2024) · Sustainable Energy & Fuels · doi:10.1039/d3se01455j · Cited by 8

Enhancing Lithium-Ion Battery Health Predictions by Hybrid-Grained Graph Modeling

Xing, C., Liu, H., Zhang, Z., Wang, J., & Wang, J. (2024) · Sensors · doi:10.3390/s24134185 · Cited by 8

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

Xiao, Y., Shi, X., Li, X., Duan, Y., Li, X., Zhang, J., Luo, T., Wang, J., Tan, Y., Gao, Z., et al. (2024) · Energy Storage · doi:10.1002/est2.70080 · Cited by 9

A maintenance-focused approach to complex system design

Yu, B. Y., Honda, T., Zubair, S. M., Sharqawy, M. H., & Yang, M. C. (2016) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s089006041600024x · Cited by 8

Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach

Mishra, M., Martinsson, J., Goebel, K., & Rantatalo, M. (2021) · IEEE Access · doi:10.1109/access.2021.3130157 · Cited by 8

A Novel Indirect Health Indicator Extraction Based on Charging Data for Lithium-Ion Batteries Remaining Useful Life Prognostics

Gao, D., Huang, M., & Xie, J. (2017) · SAE International journal of alternative powertrains · doi:10.4271/2017-01-9078 · Cited by 8

A Comparative Analysis of Lithium Ion Battery Input Profiles for Remaining Useful Life Prediction by Cascade Forward Neural Network

Ansari, S., Ayob, A., Lipu, M. S. H., Saad, M. H. M., & Hussain, A. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454234 · Cited by 8

PHM of Li‐ion Batteries

Saxena, S., Xing, Y., & PECHT, M. G. (2018) · Wiley eBooks · doi:10.1002/9781119515326.ch13 · Cited by 8

Ensemble of model-based and data-driven prognostic approaches for reliability prediction

Fan, T., & Zhao, W. (2017) · 2017 Prognostics and System Health Management Conference (PHM-Harbin) · doi:10.1109/phm.2017.8079114 · Cited by 7

Robust State of Health Estimation for Heterogeneous Batteries With Privacy Preserving

Wang, T., Zhang, Z., Tao, Y., & Dong, Z. Y. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3535537 · Cited by 7

State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval

Sun, J., Zhang, X., Cao, W., Bo, L., Liu, C., & Wang, B. (2025) · AIMS energy · doi:10.3934/energy.2025012 · Cited by 7

State of Health Estimation for Lithium-Ion Battery With Label-Free Data Based on Deep Domain Feature Independent Alignment Network

Song, C., Yuan, H., Wang, G., & Cui, N. (2025) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2025.3580291 · Cited by 7

A flexible multi-feature transformer model for enhanced prediction of lithium-ion battery’s remaining useful life

Xue, K. W., Fung, K. C., Lai, S. J., Lam, K., & Lai, C. M. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238302 · Cited by 7

A SOH estimation method utilizing crossformer-transfer learning architecture for new energy storage stations considering erratic power injection

Yuan, T., Han, B., Song, X., Shuang, Y., & Wang, R. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118542 · Cited by 7

State of health estimation of lithium-ion batteries using variational-diffusion generative model and attention-enhanced hybrid temporal network

Miao, J., Xie, J., Deng, C., Piao, C., Huang, M. H., & He, M. (2026) · Energy · doi:10.1016/j.energy.2026.139913 · Cited by 7

Review on data-driven state of health estimation methods for lithium-ion batteries

Wang, S., Zhou, J., Li, L., Zhang, L., Fernández, C., & Blaabjerg, F. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.240783 · 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

Capacity estimation of Li-ion battery based on transformer-adversarial discriminative domain adaptation

Liu, X., Yang, C., Meng, Y., Zhu, J., & Duan, Y. (2023) · AIP Advances · doi:10.1063/5.0152038 · Cited by 7

A Bayesian method for capacity degradation prediction of lithium-ion battery considering both within and cross group heterogeneity

Zhang, J., Wang, C., Li, J., Xie, Y., Mao, L., & Hu, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121855 · Cited by 7

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun, Y., Tian, H., Hu, F., & Du, J. (2024) · Batteries · doi:10.3390/batteries10060187 · Cited by 8

Remaining useful performance analysis of batteries

He, W., Williard, N., Osterman, M. D., & PECHT, M. G. (2011) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2011.6024341 · Cited by 27

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Dempster-Shafer Theory and the Support Vector Regression-Particle Filter

Dong, H. (2021) · IEEE Access · doi:10.1109/access.2021.3136131 · Cited by 14

Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

Zhao, L., Li, Q., & Suo, B. (2020) · IEEE Access · doi:10.1109/access.2020.2987426 · Cited by 13

Estimating the remaining useful life of Li-ion batteries with a Bayesian updating model

Hai, Y., Tang, J., & Tsui, K. (2012) · IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) · doi:10.1109/ieem.2012.6838119 · Cited by 2

A Method for Predicting the Remaining Life of Lithium-Ion Batteries Based on an Improved Dempster–Shafer Evidence Theory Framework

Zhang, T., & Sun, H. (2025) · Energies · doi:10.3390/en18133370 · Cited by 5