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

Lag-Llama-Based Remaining Useful Life Prediction for Lithium-Ion Batteries of Electric Vehicles With Autocorrelation Analysis

Kaleem, M. B., Li, H., Zhu, Z., Chen, X., Fan, Y., Yan, L., & Liu, W. (2026) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2026.3673041 · Cited by 1

A Deep Learning Hybrid Model for Battery State of Health Estimation Based on Reconstructed Current and Voltage Vurves

Song, S., Zhang, C., Song, C., Duan, W., Xiao, F., Jin, L., & Qi, C. (2026) · Automotive Innovation · doi:10.1007/s42154-025-00416-1 · Cited by 1

Lithium-ion battery capacity estimation from partial charging curves using adaptive high-information sampling and deep learning

Su, X., Shen, G., Qiao, X., Ge, Y., & Xu, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121976 · Cited by 1

A method for estimating the SOH of lithium-ion batteries based on exponential smoothing transformer with temporal-channel coordinate attention and reversible instance normalization

Wang, S., Chen, K., Jin, Z., Qin, A., & Zeng, L. (2026) · Ionics · doi:10.1007/s11581-026-07092-z · Cited by 1

A Multi-Step RUL Prediction Method for Lithium-Ion Batteries Based on Multi-Scale Temporal Features and Frequency-Domain Spectral Interaction

Tu, Y., Xu, S., Wang, J., & Jin, M. (2026) · Batteries · doi:10.3390/batteries12040137 · Cited by 1

State-of-health estimation of lithium-ion battery under dynamic conditions using a dual-domain feature fusion machine learning approach

Xiong, J., 夏敏磊, Xu, S., Ding, P., Pan, H., Gao, Q., Guo, W., & Min, Y. (2026) · Ionics · doi:10.1007/s11581-025-06832-x · Cited by 1

Physics-informed signal decomposition and data-driven RUL estimation for lithium-ion batteries

Fan, Y., Xu, K., Zhu, K., & Yu, X. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6297 · Cited by 1

Toward early battery anomaly detection: A multimodal dataset and deep learning framework for lithium-ion batteries

Zitouni, M., Arbaoui, S., Giustozzi, F., Samet, A., & Mesbahi, T. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100816 · Cited by 1

Taylor polynomial-constrained physics-informed neural network for battery degradation modeling and SOH estimation

Xu, W. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae83d4 · Cited by 1

Joint estimation of battery state of health and remaining useful life via degradation encoding and implicit physics-informed neural networks

Liu, Z., Yao, L., Huang, W., Jiang, Y., Qiu, S., & Tang, X. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.241124 · Cited by 1

Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation

Chen, J., Gui, X., Fang, S., Tao, S., Zheng, S., Liu, W., & Bian, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818856 · Cited by 1

A framework for joint state-of-charge and state-of-health estimation of lithium-ion batteries

Zhao, R., Jiang, X., Jiao, D., Du, G., & Zhu, Z. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124027 · Cited by 1

RSL Prediction Approach for Systems with Operation State Switches

Si, X., Zhang, Z., & Hu, C. (2017) · Springer series in reliability engineering · doi:10.1007/978-3-662-54030-5_12 · Cited by 1

Improved Battery Degradation Modelling with Coupled Physical and Machine Learning Modelling

Daniel, N., Stoyanov, S., Bailey, C., & Flynn, D. (2021) · International Spring Seminar on Electronics Technology · doi:10.1109/isse51996.2021.9467565 · Cited by 1

An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode

Zhou, H.-X., Zhou, C.-G., & Feng, H. (2022) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x221110969 · Cited by 1

Capacity Prediction of Lithium-Ion Battery Based on HGWO-SVR

Liao, Q., Chen, K., Liu, K., Yang, Y. K., Gao, G., & Wu, G. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1027-4_8 · Cited by 1

Prediction Method of Ohmic Resistance and Charge Transfer Resistance for Lithium-Ion Batteries Based on CSA-SVR

Zhu, J., Chen, K., Liu, K., Gao, G., & Wu, G. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1027-4_51 · Cited by 1

DELiB: Deep Extreme Learning-Based Health Estimation for Lithium-ion Battery

Vasanth, M. S. V. S. V., Gunturu, P. A., Sinha, A., & Das, D. (2023) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc53148.2023.10176060 · Cited by 1

Remaining useful life prediction of lithium-ion battery based on new health factor in long short-term memory network

Zhang, Z., Liu, P., Ge, W., & Lai, Y. (2023) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3613330.3613333 · Cited by 1

Electric Vehicle Battery Pack Prediction of Capacity Degradation Based on Deep Learning Architecture and Internet of Things

Singh, M., & Reddy, K. J. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-5613-5_14 · Cited by 1

Comparative Study on Incremental Capacity Analysis with Machine Learning Algorithms for State of Health Estimation of Lithium-Ion Batteries

Zheng, L., & Guo, X. (2023) · IEEE International Future Energy Electronics Conference (IFEEC) · doi:10.1109/ifeec58486.2023.10458624 · Cited by 1

Retrieval-based Battery Degradation Prediction for Battery Energy Storage System Operations

Li, Y., Yang, Q., Wen, H., Zheng, H., Liu, W., Li, H., Li, Y., & Liu, Y. (2023) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00128 · Cited by 1

An optimal goose lithium-ion batteries accurate and rapid RUL prediction method with automatic initial hyperparameters settings

Li, G., Huang, Y., Sun, C., & Pang, Y. F. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad7a94 · Cited by 1

Stress-informed transfer learning: Accelerated battery life evaluation model across diverse operating conditions and cell mechanisms

Zeng, G., Du, Y., Song, Y., Liu, D., & Peng, Y. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100629 · Cited by 1

Life Cycle Risk Mitigations

Dai, J., Ohadi, M. M., Das, D., & PECHT, M. G. (2013) · Springer eBooks · doi:10.1007/978-1-4614-5602-5_8 · Cited by 1

Lithium-Ion Battery Capacity Prediction Using Recursive Least Squares with Forgetting Factor

Zhou, Z. W., Lu, Y. D., Huang, Y., Shi, Z., & Li, X. (2015) · Advances in engineering research/Advances in Engineering Research · doi:10.2991/eame-15.2015.159 · Cited by 1

Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier

Ng, S. Y. S., & Tsui, K. (2012) · IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) · doi:10.1109/ieem.2012.6838148 · Cited by 1

Grey prediction method for forecasting the capacity of lithium-ion batteries

Kim, D., Goh, T., Park, M., Seo, M., & Kim, S. W. (2015) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2015.7364793 · Cited by 1

Prognostics of Lithium-Ion Batteries Under Uncertainty Using Multiple Capacity Degradation Information

Li, F., & Wang, Y. (2017) · Springer eBooks · doi:10.1007/978-3-319-59280-0_109 · Cited by 1

Prognostics for lithium-ion battery operating under different depth of discharge using hybrid method

Chang, Y. L., Fang, H., Li, S., & Yan, Y. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8408225 · Cited by 1

A Data-Driven Particle Filter For Lithium-Ion Batteries State-Of-Life Prognosis

Cadini, F., Sbarufatti, C., Cancelliere, F., & Giglio, M. (2018) · PHM Society European Conference · doi:10.36001/phme.2018.v4i1.416 · Cited by 1

Nonlinear Mixed Effect Model-Based Prognostics for Lithium-ion Battery Charge Decay

Ossai, C. I., & Raghavan, N. (2018) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/ram.2018.8463128 · Cited by 1

A Fast Monte Carlo Method for Model-based Prognostics Based on Stochastic Calculus

Corbetta, M., & Kulkarni, C. S. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-2232 · Cited by 1

Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis

Ossai, C. I. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2018.v9i2.2703 · Cited by 1

Deep Residual Net-Based Prognosis Method for Lithium-ion Batteries with Information Fusion from Different Scales

Zhu, Y., Li, X., & Zhang, W. (2022) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-yantai55411.2022.9941914 · Cited by 1

Experimental Design and Maintenance, Towards a Decision-Making Approach Driven by Degradation Models, with Application to Lithium-Ion Batteries

Pievatolo, A., Magrini, A., Meccariello, G., Cristaldi, L., Patrizi, G., & Nikiforova, N. D. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405766 · Cited by 1

Model-based diagnosis and prognosis of induction motors under stator winding fault

Nguyen, V. H. (2018) · doi:10.32657/10220/46022 · Cited by 1

LITHIUM-ION BATTERY DEGRADATION EVALUATION THROUGH BAYESIAN NETWORK METHOD FOR RESIDENTIAL ENERGY STORAGE SYSTEMS

Khan, K. (2019) · doi:10.37099/mtu.dc.etdr/935 · Cited by 1

Remaining Useful Life Prediction of Lithium-Ion Battery Using Improved Ant Lion Optimization F-Distribution Particle FillterPreprint

Ma, Y., Shan, C., Zhu, J., & Hu, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4147559 · Cited by 1

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

Electrochemical Impedance Spectroscopy Based-Deep Ensemble for Lithium-Ion Batteries Prognostics Considering Data Imbalance and UncertaintyPreprint

Ruan, S.-J., & Lin, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4897125 · Cited by 1

Developing Explainable AI Methods for Predictive Analytics and Prognostics

Alomari, Y. (2025) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2024.388 · Cited by 1

Reliability study of lithium ion cell and battery pack

Leng, F. (2016) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/67063

Probabilistic Based Classification Techniques for Improved Prognostics Using Time Series Data

Rezvanizaniani, S. M. (2015) · OhioLink ETD Center (Ohio Library and Information Network)

Rejoinder: Concert Unlikely, “Jugalbandi” Perhaps

Singpurwalla, N. D. (2016) · Statistical Science · doi:10.1214/16-sts593

Differentiation of ECM and noise model/data rejection for high-capacity and high-power cell according to the electrical characteristics

Lee, H.-J., Park, J., & Kim, J. (2017) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec.2017.7930859

Lifetime estimation for battery with the regeneration phenomena in both degradation state and rate

Zhang, J., Hu, C., He, X. H., Si, X., & Zhou, D. (2017) · Prognostics and System Health Management Conference · doi:10.1109/phm.2017.8079132

Lithium battery state-of-charge estimation based on theory of evidence with interval analysis

Duangpummet, S., & Karnjana, J. (2017) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.23919/sice.2017.8105727

Contributions to industrial process condition forecasting applied to copper rod manufacturing process

Zurita, D. (2017) · doi:10.5821/dissertation-2117-113674

A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning

Zhao, S. (2018) · International Conference on Manufacturing Science and Engineering (ICMSE) · doi:10.2991/icmse-18.2018.30

A BAYESIAN NETWORK APPROACH TO BATTERY AGING IN ELECTRIC VEHICLE TRANSPORTATION AND GRID INTEGRATION

Jafari, M. (2018) · doi:10.37099/mtu.dc.etdr/588

Sustainable Manufacturing of High-Precision, Heat-Resistant Aspherical Lenses Using Ultraviolet Illumination With Prognosis of Remaining Useful Life

Kim, J., Lim, J., Park, C., Myung, H., Lee, J., & Kang, S. (2018) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4042125

Comparing methods for the estimation of a stationary battery’s capacity

Santos, P. C. D. E. B., Seabra, A. C., & Del-Moral-Hernandez, E. (2018) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2018.8576203

Evidence Fusion Algorithm Based on Correction Conflict

Meng, Y., Xu, L., Jing, Y., & Wang, Y. (2018) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme.2018.00180

On-Board Prediction of Remaining Useful Life of Lithium-Ion Battery

Hu, C. K., Shen, S., & Li, Y. (2019)

An Improved Particle Filter Method for Accurate Remaining Useful Life Prediction

Huang, D., Wang, M., Zhao, S., Wen, P., Chen, S., & Dou, Z. (2019) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2019.8819414

Research on Battery State Tracking and Remaining Life Prediction Method Based on Improved Particle Filtering and Reformed Self-Adaptive Fusion Algorithms

Jiao, Z., Fan, X., & Zhang, X. (2019) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2019.10105

Analysis and Modeling of Capacity Fading in Lithium Ion Batteries

Appiah, W. A. (2020) · doi:10.22677/thesis.200000321089

Métodos para a estimativa da capacidade de baterias estacionárias com ênfase em aplicações de segurança.

Santos, P. C. D. E. B. (2020) · doi:10.11606/d.3.2020.tde-05032021-091755

Indirect measurement of the battery capacity of smart devices.

Pihlap, H., Annuk, A., Allik, A., & Hovi, M. (2017) · Agronomy Research

Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system

Mustaza, M. S. A. (2021) · UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)

Second Life Battery Life Cycle EstimationPreprint

Yang, J., Beatty, M., Strickland, D., Varnosfaderani, M. A., & Warren, J. (2022) · doi:10.36227/techrxiv.21610080.v1

Second Life Battery Life Cycle EstimationPreprint

Yang, J., Beatty, M., Strickland, D., Varnosfaderani, M. A., & Warren, J. (2022) · doi:10.36227/techrxiv.21610080

Two Statistical Degradation Models of Batteries Under Different Operating Conditions

Kong, J., & Wang, D. (2022) · Springer eBooks · doi:10.1007/978-3-031-07155-3_11

Cross-Entropy based Feature Selection for Lithium-ion Battery State of Health Estimation

Wu, J., Cheng, Z., & Wang, L. (2022) · International Conference on Systems and Control (ICSC) · doi:10.1109/icsc57768.2022.9993852

A Robust Prognostic Indicator for Renewable Energy Fuel Cells: A Hybrid Data-Driven Prediction Approach

Zhou, D., Tian, Z., & Liang, J. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-16620-4_10

ANÁLISIS DE LA DEGRADACIÓN DE BATERÍAS EN CONTEXTOS DE USO APLICANDO MODELOS ESTADÍSTICOS

Quispe, J. K. H., Colque, J. P. B., Torres‐Cruz, F., Figueroa, A. T., Mollocondo, C. I. M., Coa, Y. L. J., Quispe, V. I., Bendita, C. E. Y., & Velásquez, R. A. M. (2023) · Editora Científica Digital eBooks · doi:10.37885/230312560

การวเคราะหผลประโยชนและตนทนของการตดตงระบบผลตไฟฟาจากพลงงานแสงอาทตยแบบตดตงบนหลงคารวมกบแบตเตอร

ต่างวิวัฒน์, บ. (2017) · doi:10.58837/chula.the.2017.1354

Remaining Useful Lifetime Prediction Method of Aviation Equipment Based on Improved Particle Filter

Yangjun, G., & Zezhou, W. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-5203-8_10

Research on IC curve feature extraction and lithium battery SOH estimation method based on QPSO-BP Algorithm

Dinghong, L., Lifeng, W., Li, Z., & Liu, L. (2023) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae60493.2023.00117

Conditional Wasserstein barycenters to predict battery health degradation at unobserved experimental conditions

Larvaron, B., Clausel, M., Bertoncello, A., Benjamin, S., Oppenheim, G., & Bertin, C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.110015 · Cited by 1

An Improved Data-Driven Life Prediction Model of Lithium-Ion Battery

Ge, B., & Wang, L. (2023) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce53617.2023.10362270

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation

Goh, H. H., An, Z., Zhang, D., Dai, W., Kurniawan, T. A., & Goh, K. C. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1367444

Machine learning-based aging models for estimating battery state of health and predicting future degradation

Thelen, A. (2023) · doi:10.31274/td-20240329-481

Computational and data-enabled approaches for modeling and prognostics of engineered systems

Lui, Y. H. (2021) · doi:10.31274/td-20240329-161

Relative 6-DOF GNC Accuracy Analysis in the Final Phase of HTV-X Automated Docking Mission

Sasaki, T., MURAKAMI, N., Yamamoto, T., & KONDO, Y. (2024) · Transactions of the Society of Instrument and Control Engineers · doi:10.9746/sicetr.60.182

A Mixed-State Particle Filter for Remaining Useful Life Prediction of Lithium-Ion BatteriesPreprint

Huang, M., Du, Z., Ruibo, L. U., Wang, X., & Peng, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4784078

Bearing Remaining Useful Life Estimation Using Arimoto Entropy Based Features and Relevance Vector Machine ModelPreprint

He, D., & Wang, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4835430

Electrochemical Impedance Spectroscopy-Based Deep Ensemble for Lithium-Ion Batteries Prognostics Considering Data Imbalance and UncertaintyPreprint

Ruan, S.-J., & Lin, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891451

Monitoring battery status and facility conditions

Tabassum, T., & Khalghani, M. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99545-0.00003-8

Maximizing the performance of data-driven capacity estimation for lithium-ion battery

Moon, H., Kim, J., & Han, S. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.455

RUL Prediction of Split Torque Transmission System Using Particle Filtering and Degenerate Model

Yang, W., Wang, Z., Tang, X., Hu, L., & Xu, Y. (2024) · Mechanisms and machine science · doi:10.1007/978-3-031-69483-7_40

E2F2: End-to-End Feature-Free Network for Lithium-Ion Battery State of Health Prediction

Huang, Z., Dang, L., & Xing, L. (2024) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei63073.2024.10696695

A Lithium-ion Battery SOH Estimation Method Based on WT-EMD-Informer Model

Huang, J., Mao, Y., Liao, Y., Zhu, W., & He, T. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00033

Quantifying Cost of BESS for Providing Power System Regulation Service Considering Capacity Regeneration

Ma, D., Ye, C., & Liu, Y. (2024) · IEEE Student Conference on Electric Machines and Systems (SCEMS) · doi:10.1109/scems63294.2024.10756495

A Lithium-Ion Battery Health State Assessment Based on Bi-LSTM-Transformer Algorithm

Li, C., Dai, H., Ye, J., Liu, D., Lin, W., Du, G., & Yang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0232-2_22

Life Prediction of Lithium-Ion Batteries Based on Fusion Techniques

Wu, P., Liu, P., Hu, X., Zhang, Z., Wang, X., & Ma, N. (2024) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac63745.2024.10899715

Early Prediction of Battery Lifetime Using Centered Isotonic Regression with Quantile-Transformed Features

Khan, M. A., Wang, Y., & Jiang, B. (2025) · Batteries · doi:10.3390/batteries11040145

A Lithium-Ion Battery RUL Prediction Method Based on ConvTrans and tAPE Under Capacity Regeneration Noise and Low-Dimensional Time Series Data

Chen, J., Lu, Q., Wang, X., Ge, H., & Xie, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3564016

Lithium-Ion Battery State of Health Estimation Based on Time-Frequency Image Fusion and KAN

Fang, J., Wu, G., Chu, H., Ding, P., Xia, X., & Dong, K. (2024) · International Conference on Smart Power & Internet Energy Systems (SPIES) · doi:10.1109/spies63782.2024.10982961

A distribution-free interval estimation method for lithium-ion battery state of health

Pang, X., Guo, Z., Jia, J., Wen, J., Li, X., Zeng, J., & Zhang, J. (2025) · Ionics · doi:10.1007/s11581-025-06470-3

DPCNN-Transformer Network for the Prediction of Remaining Useful Life of Lithium-Ion Batteries

Liu, Y., Su, Y., Guan, S., Wang, Y., Zhang, W., & Zhang, X. (2025) · Green energy and technology · doi:10.1007/978-981-96-7226-4_7

Physics-Informed Few-Shot Learning for Cross-Domain Battery Lifespan Prediction Under Heterogeneous Degradation ConditionsPreprint

Yin, Y., Huang, D., Qin, N., Li, K., Zhang, C., Wang, T., & Cai, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5492120

Physics-Informed Few-Shot Learning for Cross-Domain Battery Lifespan Prediction Under Heterogeneous Degradation ConditionsPreprint

Yin, Y., Huang, D., Qin, N., Li, K., Zhang, C., Wang, T., & Cai, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5520808

Physics-Informed Few-Shot Learning for Cross-Domain Battery Lifespan Prediction Under Heterogeneous Degradation ConditionsPreprint

Yin, Y., Huang, D., Qin, N., Li, K., Zhang, C., Wang, T., & Cai, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5531643

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

Gaussian Mixture Regression-Based Lithium Battery State-of-Health (SOH) Prediction Method

Wang, H., Lee, N., Deng, X., Jiang, Y., & Yang, Y. P. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-6340-8_5

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