Prognostics in Battery Health Management

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Battery state of health monitoring, combining conductance technology with other measurement parameters for real-time battery performance analysis

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Advanced Technology Development Program for Lithium-Ion Batteries: Gen 2 Performance Evaluation Final Report

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Determining the state-of-health of maintenance-free aircraft batteries

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An Enhanced Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction

Ahwiadi, M., & Wang, W. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2853900 · Cited by 107

AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries

Chen, D., & Zhou, X. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110780 · Cited by 109

A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges

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A Robust Prognostic Indicator for Renewable Energy Technologies: A Novel Error Correction Grey Prediction Model

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Batteries State of Health Estimation via Efficient Neural Networks with Multiple Channel Charging Profiles

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State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model

Yu, J. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2444237 · Cited by 98

Energy efficiency of lithium-ion batteries: Influential factors and long-term degradation

Lin, Z., Li, D., & Zou, Y. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109386 · Cited by 97

Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models

Nagulapati, V. M., Lee, H., Jung, D., ́, B. B., Choi, Y., & Lim, H. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108048 · Cited by 94

Health Prognosis With Optimized Feature Selection for Lithium-Ion Battery in Electric Vehicle Applications

Wu, J., Cui, X., Zhang, H., & Lin, M. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2021.3075558 · Cited by 92

A Degradation Empirical-Model-Free Battery End-of-Life Prediction Framework Based on Gaussian Process Regression and Kalman Filter

Meng, J., Yue, M., & Diallo, D. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3209629 · Cited by 90

Feature-based lithium-ion battery state of health estimation with artificial neural networks

Driscoll, L., de la Torre, S., & Gómez‐Ruiz, J. A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104584 · Cited by 86

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

Caceres, J., Gonzalez, D., Zhou, T., & Droguett, E. L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2811 · Cited by 91

Remaining useful life prediction for multi-phase deteriorating process based on Wiener process

Liao, G., Yin, H., Mĭn, C., & Lin, Z. H. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107361 · Cited by 84

Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network

Chen, L., Ding, Y., Liu, B., Wu, S., Wang, Y., & Pan, H. (2021) · Energy · doi:10.1016/j.energy.2021.122581 · Cited by 84

Advancements in Artificial Neural Networks for health management of energy storage lithium-ion batteries: A comprehensive review

Zou, Y., Lin, Z., Li, D., & Liu, Z. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109069 · Cited by 85

Battery remaining useful life prediction at different discharge rates

Wang, D., Yang, F., Zhao, Y., & Tsui, K. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.09.009 · Cited by 83

Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

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One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

Tian, N., Wang, Y., Chen, J., & Fang, H. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101282 · Cited by 81

AI-Driven Digital Twin Model for Reliable Lithium-Ion Battery Discharge Capacity Predictions

Nair, P., Vakharia, V., Shah, M., Kumar, Y., Woźniak, M., Shafi, J., & Ijaz, M. F. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/8185044 · Cited by 87

Prognostics of lithium-ion batteries using model-based and data-driven methods

Chen, C., & PECHT, M. G. (2012) · Prognostics and System Health Management Conference · doi:10.1109/phm.2012.6228850 · Cited by 80

A data-driven approach based on deep neural networks for lithium-ion battery prognostics

Kara, A. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05976-x · Cited by 78

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Mixture of Ensemble Empirical Mode Decomposition and GWO-SVR Model

Yang, Z., Wang, Y., & Kong, C. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3125108 · Cited by 77

State-of-health and remaining-useful-life estimations of lithium-ion battery based on temporal convolutional network-long short-term memory

Li, C., Han, X., Zhang, Q., Li, M., Rao, Z., Liao, W., Liu, X., Liu, X., & Li, G. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109498 · Cited by 78

SOH and RUL prediction of Li-ion batteries based on improved Gaussian process regression

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Fault diagnosis of Li-Ion batteries using multiple-model adaptive estimation

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Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

Bai, G., Su, Y., Rahman, M. M., & Wang, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108944 · Cited by 76

Predictive Diagnosis of High-Power Transformer Faults by Networking Vibration Measuring Nodes With Integrated Signal Processing

Saponara, S., Fanucci, L., Bernardo, F., & Falciani, A. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2552658 · Cited by 76

Machine Learning Approaches in Battery Management Systems: State of the Art: Remaining useful life and fault detection

Ardeshiri, R. R., Balagopal, B., Alsabbagh, A., Ma, C., & Chow, M. (2020) · IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES) · doi:10.1109/ieses45645.2020.9210642 · Cited by 76

Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Jo, S., Jung, S., & Roh, T. (2021) · Energies · doi:10.3390/en14217206 · Cited by 76

Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with Particle Swarm Optimization

Gao, D., & Huang, M. (2017) · Journal of Power Electronics · doi:10.6113/jpe.2017.17.5.1288 · Cited by 75

Lithium-ion battery health assessment method based on belief rule base with interpretability

Han, P., He, W., Cao, Y., Li, Y., Mu, Q., & Wang, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110160 · Cited by 74

An integrated imputation-prediction scheme for prognostics of battery data with missing observations

Razavi‐Far, R., Chakrabarti, S., Saif, M., & Zio, E. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.033 · Cited by 73

Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis

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Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions

Reza, M. S., Hannan, M. A., Ker, P. J., Mansor, M., Lipu, M. S. H., Hossain, M. J., & Mahlia, T. M. I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107698 · Cited by 77

A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM)

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Piecewise model based intelligent prognostics for state of health prediction of rechargeable batteries with capacity regeneration phenomena

Wang, D., Kong, J.-Z., Zhao, Y., & Tsui, K.-L. (2019) · Measurement · doi:10.1016/j.measurement.2019.07.064 · Cited by 71

Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression

Liu, D., Pang, J., Zhou, J., & Peng, Y. (2012) · Prognostics and System Health Management Conference · doi:10.1109/phm.2012.6228848 · Cited by 71

State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network

Wu, M., Zhong, Y., Wu, J., Wang, Y., & Wang, L. (2023) · Energy · doi:10.1016/j.energy.2023.129061 · Cited by 70

Remaining Useful Life Prediction of Battery Using a Novel Indicator and Framework With Fractional Grey Model and Unscented Particle Filter

Chen, L., Chen, J. W., Wang, H., Wang, Y., An, J., Yang, R., & Pan, H. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2952620 · Cited by 69

A Prognostic Model for Stochastic Degrading Systems With State Recovery: Application to Li-Ion Batteries

Zhang, Z., Si, X., Hu, C., & PECHT, M. G. (2017) · IEEE Transactions on Reliability · doi:10.1109/tr.2017.2742298 · Cited by 67

Distributed prognostic health management with gaussian process regression

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Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes

Corbetta, M., Sbarufatti, C., Giglio, M., & Todd, M. D. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.012 · Cited by 68

Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature

Liu, D., Luo, Y., Peng, Y., Peng, X., & PECHT, M. G. (2012) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2012.v4i1.2165 · Cited by 65

Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

Qin, W., Lv, H., Liu, C., Datta, N., & Jahanshahi, P. (2019) · Industrial Management & Data Systems · doi:10.1108/imds-03-2019-0195 · Cited by 67

A Balancing Current Ratio Based State-of-Health Estimation Solution for Lithium-Ion Battery Pack

Tang, X., Gao, F., Liu, K., Liu, Q., & Foley, A. M. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3108715 · Cited by 69

A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction

Li, D., Wang, W., & Ismail, F. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2303534 · Cited by 68

Vehicle energy system active defense: A health assessment of lithium‐ion batteries

Hong, S., Yue, T., & Liu, H. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22309 · Cited by 66

Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

Kim, S. W., Oh, K., & Lee, S. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119011 · Cited by 67

Multisource Domain Adaption for Health Degradation Monitoring of Lithium-Ion Batteries

Ye, Z., Yu, J., & Mao, L. (2021) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3085430 · Cited by 65

State-of-Health Estimation for Lithium-ion Batteries Based on Wiener Process with Modeling the Relaxation Effect

Xu, X., Yu, C., Tang, S., Sun, X., Si, X., & Wu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2923095 · Cited by 64

Joint nonlinear-drift-driven Wiener process-Markov chain degradation switching model for adaptive online predicting lithium-ion battery remaining useful life

Zhang, Y., Feng, F., Wang, S., Meng, J., Xie, J., Ling, R., Yin, H., Zhang, K., & Chai, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121043 · Cited by 64

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Data Preprocessing and Improved ELM

Wu, W., & Lu, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267362 · Cited by 63

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture

Li, L., Wang, P., Chao, K., Zhou, Y., & Xie, Y. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163004 · Cited by 63

A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

Shin, I., Lee, J., Lee, J. Y., Jung, K., Kwon, D., Youn, B. D., Jang, H. S., & Choi, J.-H. (2018) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-018-0055-0 · Cited by 63

Health-Conscious Energy Management for Fuel Cell Hybrid Electric Vehicles Based on Prognostics-Enabled Decision-Making

Yue, M., Jemeï, S., & Noureddine Zerhouni, A. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2937130 · Cited by 63

Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter

Li, J., Lyu, C., Wang, L., Zhang, L., & Li, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.06.133 · Cited by 62

Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity

Zhang, Z., Si, X., Hu, C., & Kong, X. (2015) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x15579322 · Cited by 61

A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

Liu, Z., Zhao, J., Wang, H., & Yang, C. (2020) · Energies · doi:10.3390/en13040830 · Cited by 61

Rechargeable batteries and their management

Kularatna, N. (2011) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2011.5735252 · Cited by 59

Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms

Sierra, G., Orchard, M. E., Goebel, K., & Kulkarni, C. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.04.030 · Cited by 59

A data-driven method for state of health prediction of lithium-ion batteries in a unified framework

Cai, L., Lin, J., & Liao, X. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104371 · Cited by 58

Health Degradation Detection and Monitoring of Lithium-Ion Battery Based on Adaptive Learning Method

Yu, J. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2293234 · Cited by 57

Systematic mixed adaptive observer and EKF approach to estimate SOC and SOH of lithium–ion battery

Gholizadeh, M., & Yazdizadeh, A. (2020) · IET Electrical Systems in Transportation · doi:10.1049/iet-est.2019.0033 · Cited by 55

A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry

Li, X., Yuan, C., Wang, Z., & Xie, J. (2022) · Energy · doi:10.1016/j.energy.2021.122206 · Cited by 56

State of Health Diagnosis and Remaining Useful Life Prediction for Lithium-ion Battery Based on Data Model Fusion Method

Cui, X., & Hu, T. (2020) · IEEE Access · doi:10.1109/access.2020.3038182 · Cited by 54

The state of health prediction of Li-ion batteries based on an improved extreme learning machine

Hou, X., Guo, X., Yuan, Y., Zhao, K., Liang, T., Yuan, C., & Long, T. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108044 · Cited by 54

A Review of Factors Affecting the Lifespan of Lithium-ion Battery and its Health Estimation Methods

Zhang, X., Han, Y., & Zhang, W. (2021) · Transactions on Electrical and Electronic Materials · doi:10.1007/s42341-021-00357-6 · Cited by 53

Effect of Regenerative Braking on Battery Life

Chidambaram, R. K., Chatterjee, D., Barman, B., Das, P. P., Taler, D., Taler, J., & Sobota, T. (2023) · Energies · doi:10.3390/en16145303 · Cited by 55

Optimal charging for general equivalent electrical battery model, and battery life management

Abdollahi, A., Han, X., Raghunathan, N., Pattipati, B., Balasingam, B., Pattipati, K. R., Bar‐Shalom, Y., & Card, B. (2016) · Journal of Energy Storage · doi:10.1016/j.est.2016.11.002 · Cited by 53

State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble

Ma, C., Zhai, X., Wang, Z., Tian, M., Yu, Q., Liu, L., Liu, H., Wang, H., & Yang, X. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0865-y · Cited by 52

Equivalent circuit simulated deep network architecture and transfer learning for remaining useful life prediction of lithium-ion batteries

Nguyen, C. D., & Bae, S. J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108042 · Cited by 53

Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification, and Validation

Tian, N., Fang, H., Chen, J., & Wang, Y. (2020) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2020.2976036 · Cited by 52

Estimating SoC, SoH, or RuL of Rechargeable Batteries via IoT: A Review

Bokstaller, J., Schneider, J., & Brocke, J. V. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3342367 · Cited by 52

An Analog Bayesian Classifier Implementation, for Thyroid Disease Detection, based on a Low-Power, Current-Mode Gaussian Function Circuit

Alimisis, V., Gennis, G., Dimas, C., & Sotiriadis, P. P. (2021) · doi:10.1109/icm52667.2021.9664939 · Cited by 50

Electrochemical Model Based Fault Diagnosis of Li-Ion Battery Using Fuzzy Logic

Muddappa, V. K. S., & Anwar, S. (2014) · doi:10.1115/imece2014-37134 · Cited by 48

A Time-Domain Least Squares Approach to Electrochemical Impedance Spectroscopy

Lindahl, P. A., Cornachione, M., & Shaw, S. R. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2210457 · Cited by 48

A Review: Prognostics and Health Management in Automotive and Aerospace

Nguyễn, V. Đ., Kefalas, M., Yang, K., Apostolidis, A., Olhofer, M., Limmer, S., & ̈ack, T. B. (2019) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2019.v10i2.2730 · Cited by 48

State-of-health estimation and remaining useful life prediction of lithium-ion batteries using DnCNN-CNN

Chae, S. G., Bae, S. J., & Oh, K.-Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.114826 · Cited by 47

Optimization techniques for electrochemical devices for hydrogen production and energy storage applications

Tawalbeh, M., Farooq, A., Martis, R., & Al–Othman, A. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.06.264 · Cited by 50

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Jafari, S., & Byun, Y.-C. (2023) · Computers · doi:10.3390/computers12110219 · Cited by 48

State of health prediction of lithium-ion batteries using particle swarm optimization with Levy flight and generalized opposition-based learning

Zhang, B., Liu, W., Cai, Y., Zhou, Z., Wang, L., Liao, Q., Fu, Z., & Cheng, Z. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110816 · Cited by 47

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Iftikhar, M., Shoaib, M., Altaf, A., Iqbal, F., Villar, S. G., López, L. A. D., & Ashraf, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-77427-1 · Cited by 46

Particle filter-based prognostic approach for railway track geometry

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Study on impedance model of Li-ion battery

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A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features

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A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

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A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals

Fu, Q., & Wang, H. (2020) · Applied Sciences · doi:10.3390/app10175765 · Cited by 45

Machine learning for full lifecycle management of lithium-ion batteries

Zhai, Q., Jiang, H., Long, N., Kang, Q., Meng, X., Zhou, M., Yan, L., & Ma, T. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114647 · Cited by 44

Dynamic battery remaining useful life estimation: An on-line data-driven approach

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On-Board Short-Circuit Detection of Li-ion Batteries Undergoing Fixed Charging Profile as in Smartphone Applications

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A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Li, L., Li, Y., Cui, W., Chen, Z., Wang, D., Zhou, B., & Hong, D. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231961 · Cited by 43

Model based condition monitoring in lithium-ion batteries

Singh, A., Izadian, A., & Anwar, S. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.06.052 · Cited by 43

Prognostics of Proton Exchange Membrane Fuel Cell stack in a particle filtering framework including characterization disturbances and voltage recovery

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Lithium-ion battery diagnostics and prognostics enhanced with Dempster-Shafer decision fusion

Weddington, J., Niu, G., Chen, R., Yan, W., & Zhang, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.057 · Cited by 42

A comparative review of prognostics-based reliability methods for Lithium batteries

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Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

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Calendar ageing model of Li-ion battery combining physics-based and empirical approaches

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