A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

Gaussian Processes for Regression

Williams, C. K. I., & Rasmussen, C. E. (1995) · Aston Publications Explorer (Aston University) · Cited by 1,121

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

Gaussian process regression for forecasting battery state of health

Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732

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Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664

A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation

Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561

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Weng, C., Cui, Y., Sun, J., & Peng, H. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.02.012 · Cited by 561

An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 531

Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis

Tosun, N. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2386-y · Cited by 471

Behavior and State-of-Health Monitoring of Li-ion Batteries Using Impedance Spectroscopy and Recurrent Neural Networks

Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.04.050 · Cited by 462

Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443

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Pohlmann, S., Mashayekh, A., Stroebl, F., Karnehm, D., Kuder, M., Neve, A., & Weyh, T. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111649 · Cited by 32

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

Qi, N., Yan, K., Yu, Y., Li, R., Huang, R., Chen, L., & Su, Y. (2023) · Frontiers in Energy · doi:10.1007/s11708-023-0891-7 · Cited by 32

Electrochemical-distributed thermal coupled model-based state of charge estimation for cylindrical lithium-ion batteries

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Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials

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A Flexible deep convolutional neural network coupled with progressive training framework for online capacity estimation of lithium-ion batteries

Xue, Q., Li, J., Xiao, Y., Chai, Z., Liu, Z., & Chen, J. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136575 · Cited by 32

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Gao, M., Shen, H., Bao, Z., Deng, Y., & He, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3341857 · Cited by 30

Lithium-ion battery remaining useful life prediction based on data-driven and particle filter fusion model

Wu, C., Xu, C., Wang, L., Fu, J., & Meng, J. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100267 · Cited by 30

Technoeconomic decision support for second-life batteries

Zhuang, J., Bach, A., van Vlijmen, B., Reichelstein, S. J., Chueh, W. C., Onori, S., & Benson, S. M. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125800 · Cited by 30

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Wang, B., Wang, X., Hu, B., Xu, L., & Xiao, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118577 · Cited by 32

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Peng, J., Zheng, Z., Zhang, X., Deng, K., Gao, K., Li, H., Chen, B., Yang, Y., & Huang, Z. (2020) · Energies · doi:10.3390/en13030752 · Cited by 30

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Wu, S., Pan, W., & Zhu, M. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac8ee4 · Cited by 30

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Wang, T., Dong, Z. Y., & Xiong, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3448626 · Cited by 31

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Diagnosis and prognosis of battery degradation through re-evaluation and Gaussian process regression of electrochemical model parameters

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Research on online estimation of available capacity of lithium batteries based on daily charging data

Zhou, D., Zheng, W., Fu, P., & Pan, X. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.227713 · Cited by 28

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Arthur, C. K., Bhatawdekar, R. M., Mohamad, E. T., Sabri, M. M. S., Bohra, M., Khandelwal, M., & Kwon, S. (2022) · Applied Sciences · doi:10.3390/app12189189 · Cited by 28

Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network

El-Dalahmeh, M., Al‐Greer, M., El‐Dalahmeh, M., & Bashir, I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108046 · Cited by 29

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Zhao, D., Li, H., Zhou, F., Zhong, Y., Zhang, G., Liu, Z., & Hou, J. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14060145 · Cited by 28

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Lai, X., Yao, Y., Tang, X., Zheng, Y., Zhou, Y., Sun, Y., & Gao, F. (2023) · Energy · doi:10.1016/j.energy.2023.128971 · Cited by 27

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Waseem, M., Huang, J., Wong, C.-N., & Lee, C. K. M. (2023) · Mathematics · doi:10.3390/math11204263 · Cited by 26

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Dong, G., Hua, N., Chen, H., & Lou, Y. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3445344 · Cited by 26

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Yao, W., Lai, R., Tian, Y., Li, X., & Tian, J. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3471867 · Cited by 26

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Lee, S., & Kim, Y. (2020) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc45564.2020.9147633 · Cited by 26

A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Tian, Y., Han, M., Kulkarni, C. S., & Fink, O. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108529 · Cited by 27

A fusion framework for lithium-ion batteries state of health estimation using compressed sensing and entropy weight method

He, N., Qian, C., Shen, C., & Huangfu, Y. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.10.003 · Cited by 26

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Tang, J. W., Liu, Q., Liu, S., Xie, X., Jicheng, Z., & Li, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2936213 · Cited by 26

Machine learning-assisted data-driven optimization and understanding of the multiple stage process for extraction of polysaccharides and secondary metabolites from natural products

Ma, J., Yao, J., Ren, X., Dong, Y., Song, R., Zhong, X., Zheng, Y., Shan, D., Lv, F., Li, X., et al. (2023) · Green Chemistry · doi:10.1039/d2gc04574e · Cited by 25

State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance

Sun, Z., He, W., Wang, J., & He, X. (2024) · Energies · doi:10.3390/en17112487 · Cited by 28

Sustainable approaches and advancements in the recycling and recovery of metals in batteries: A review

Jeswin Anto, L., & Kandasubramanian, B. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100271 · Cited by 25

Machine learning‐based approach for useful capacity prediction of second‐life batteries employing appropriate input selection

Bhatt, A., Ongsakul, W., Nimal Madhu, M., & Singh, J. G. (2021) · International Journal of Energy Research · doi:10.1002/er.7160 · Cited by 28

State of health forecasting of Lithium-ion batteries applicable in real-world operational conditions

von Bülow, F., Mentz, J., & Meisen, T. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103439 · Cited by 25

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Zhang, X., Sun, J., Shang, Y., Ren, S., Liu, Y., & Wang, D. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.972486 · Cited by 25

State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

Liu, B., Xu, J., & Xia, W. (2023) · International Journal of Energy Research · doi:10.1155/2023/8569161 · Cited by 25

State-of-health estimation for lithium-ion batteries using relaxation voltage under dynamic conditions

Ke, X., Hong, H., Zheng, P., Zhang, S., Zhu, L., Li, Z., Cai, J., Fan, P., Yang, J. J., Wang, J., et al. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113506 · Cited by 24

A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting

Liu, Z.-F., Huang, Y.-H., Zhang, S.-R., Luo, X.-F., Chen, X., Lin, J.-J., Tang, Y. J., Guo, L., & Li, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124741 · Cited by 24

A novel state-of-health estimation method for fast charging lithium-ion batteries based on an adversarial encoder network

Fan, Y., Wang, H., Zheng, Y., Zhao, J., Wu, H., Wang, K., Yang, S., & Tan, X. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107087 · Cited by 24

A light-weight feature extractor for lithium-ion battery health prognosis

Zhou, D., Wang, B., Zhu, C., Zhou, F., & Wu, H. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109352 · Cited by 24

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Wang, R., Zhu, M., & Zhang, X. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2023.110376 · Cited by 24

Early-stage lifetime prediction for lithium-ion batteries: Linear regression - ensemble learning hybrid model based on impedance spectroscopy geometry

Zhang, S., Yuan, W., Wang, Y., Cheng, S., & Wang, J. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235153 · Cited by 24

Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning

Li, A. G., Wang, W., West, A. C., & Preindl, M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119005 · Cited by 24

Battery capacity trajectory prediction by capturing the correlation between different vehicles

Li, J., Deng, Z., Liu, H., Xie, Y., Liu, C., & Lu, C. (2022) · Energy · doi:10.1016/j.energy.2022.125123 · Cited by 24

Lithium-ion battery state of health estimation method based on variational quantum algorithm optimized stacking strategy

Wang, L., Jiang, S., Mao, Y., Li, Z., Zhang, Y., & Li, M. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.02.034 · Cited by 23

State of Charge Estimation of Lithium-Ion Based on VFFRLS-Noise Adaptive CKF Algorithm

Wang, L., Han, J., Liu, C., & Li, G. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c03999 · Cited by 23

Physics-based fractional-order model and parameters identification of liquid metal battery

Shi, Q., Guo, Z., Wang, S., Yan, S., Zhou, X., Li, H., Wang, K., & Jiang, K. (2022) · Electrochimica Acta · doi:10.1016/j.electacta.2022.140916 · Cited by 23

Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm

Song, X., Liu, P., Chen, K., Liu, K., Gao, G., & Wu, G. (2023) · International Journal of Green Energy · doi:10.1080/15435075.2023.2264959 · Cited by 23

Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

Mo, D., Wang, S., Fan, Y., Takyi‐Aninakwa, P., Zhang, M., Wang, Y., & Fernández, C. (2024) · Energy · doi:10.1016/j.energy.2024.132723 · Cited by 23

An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Lai, X., Yi, W., Zheng, Y., & Zhou, L. (2018) · Electronics · doi:10.3390/electronics7110321 · Cited by 23

Battery health prognostics based on improved incremental capacity using a hybrid grey modelling and Gaussian process regression

Li, K., & Xie, N. (2024) · Energy · doi:10.1016/j.energy.2024.131888 · Cited by 22

Correlation based-graph neural network for health prognosis of non-fully charged and discharged lithium-ion batteries

Wei, W., Chen, Z. B., Liu, W., & Pan, E. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235984 · Cited by 24

An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

Zhang, S., Peng, N., & Zhang, X. (2021) · International Journal of Energy Research · doi:10.1002/er.6964 · Cited by 22

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

Li, W. S., Li, H., He, Z., Ji, W., Zeng, J. H., Li, X., Zhang, Y., Zhang, P., & Zhao, J. (2022) · Energies · doi:10.3390/en15239165 · Cited by 22

Online State-of-Health Estimation for Fast-Charging Lithium-Ion Batteries Based on a Transformer–Long Short-Term Memory Neural Network

Fan, Y., Li, Y., Zhao, J., Wang, L., Yan, C., Wu, X., Zhang, P., Wang, J., Gao, G., & Wei, L. (2023) · Batteries · doi:10.3390/batteries9110539 · Cited by 22

Surrogate Ensemble-Assisted Hyper-Heuristic Algorithm for Expensive Optimization Problems

Zhong, R., Yu, J., Zhang, C., & Munetomo, M. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00346-y · Cited by 22

A novel endurance prediction method of series connected lithium-ion batteries based on the voltage change rate and iterative calculation

Wang, S., Tang, W., Fernández, C., Yu, C., Zou, C., & Zhang, X.-Q. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.10.349 · Cited by 22

A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model

Wang, S., Stroe, D., Fernández, C., Li-ying, X., Fan, Y., & Cao, W. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118506 · Cited by 22

Capacity Estimation of Li-Ion Batteries Using Constant Current Charging Voltage With Multilayer Perceptron

Park, M., Seo, M., Song, Y., & Kim, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3028095 · Cited by 22

Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models

Huang, Z., Huang, Z., An, P., Liu, J., Gao, C., & Huang, J. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103445 · Cited by 21

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Sravanthi, C. L., Sekhar, J. N. C., Alluraiah, N. C., Dhanamjayulu, C., Pujari, H. K., & Khan, B. (2025) · International Transactions on Electrical Energy Systems · doi:10.1155/etep/2242749 · Cited by 21

Addressing Sensor Data Heterogeneity and Sample Imbalance: A Transformer-Based Approach for Battery Degradation Prediction in Electric Vehicles

Wu, B., Qiu, S., & Liu, W. (2025) · Sensors · doi:10.3390/s25113564 · Cited by 23

A lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features

Chen, G., Zhong, S., Yang, Y., Zhang, X., Deng, W., & Liu, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117324 · Cited by 21

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Health estimation of lithium-ion batteries with voltage reconstruction and fusion model

Liu, X., Li, S., Tian, J., Wei, Z., & Wang, P. (2023) · Energy · doi:10.1016/j.energy.2023.128216 · Cited by 21

Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications

Marghichi, M. Е., Dangoury, S., Zahrou, Y., Loulijat, A., Chojaa, H., Banakhr, F. A., & Mosaad, M. I. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293753 · Cited by 21

Deep learning method for online parameter identification of lithium-ion batteries using electrochemical synthetic data

Li, J., Li, X., Yuan, X., & Zhang, Y. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103697 · Cited by 21

Human-Knowledge-Augmented Gaussian Process Regression for State-of-Health Prediction of Lithium-Ion Batteries With Charging Curves

Zhou, Q., Wang, C., Sun, Z., Li, J., Williams, H., & Xu, H. (2021) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4050798 · Cited by 21

A hybrid method for online cycle life prediction of lithium‐ion batteries

Wang, F., Amogne, Z. E., Tseng, C., & Chou, J. (2022) · International Journal of Energy Research · doi:10.1002/er.7785 · Cited by 21

SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features

Qian, K., Li, Y., Zou, Q., Cao, K., & Li, Z. (2025) · Energies · doi:10.3390/en18133248 · Cited by 20

A hybrid quantum neural network and classical gated recurrent unit for battery state of health forecasting incorporating SHAP analysis

Soon, K. L., & Soon, L. T. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118596 · Cited by 22

Multimodal deep learning with time-frequency health features for battery SOH and RUL prediction

Wang, R., Chen, L., Xu, J., Yuan, F., Han, J., Li, Z., Li, Z., Zhang, Y., Li, P., Zhang, L., et al. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.09.018 · Cited by 20

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

Ren, J., Cai, J., & Li, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-91241-z · Cited by 20

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang, Z., Sugiarto, L., & Lu, Y.-C. (2023) · EcoMat · doi:10.1002/eom2.12345 · Cited by 20

Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging

Zou, Q., & Wen, J. (2024) · Energy · doi:10.1016/j.energy.2024.132884 · Cited by 20

Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

Rahbari, O., Mayet, C., Omar, N., & Mierlo, J. V. (2018) · Applied Sciences · doi:10.3390/app8081301 · Cited by 20

Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

Jafari, M., Brown, L. E., & Gauchía, L. (2019) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2019.2956350 · Cited by 20

A hybrid deep learning model for lithium-ion battery state-of-health estimation using electrochemical impedance spectroscopy

Wu, C., Wang, L., Meng, J., Huang, J., Yang, T., Wang, L., Chang, Y., & He, X. (2025) · Energy · doi:10.1016/j.energy.2025.138974 · Cited by 21

Regression based battery state of health estimation for multiple electric vehicle fast charging protocols

Acquarone, M., Miretti, F., Giuliacci, T. A., Duque, J., Misul, D. A., & Kollmeyer, P. J. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235601 · Cited by 19

Resolving overlapping convex objects in silhouette images by concavity analysis and Gaussian process

Zafari, S., Murashkina, M., Eerola, T., Sampo, J., Kalviainen, H. A., & Haario, H. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102962 · Cited by 19

An online state of health estimation method based on battery management system monitoring data

Liu, F., Liu, X., Su, W., Lin, H. Z., Chen, H., & He, M. (2020) · International Journal of Energy Research · doi:10.1002/er.5351 · Cited by 19

Online Estimation of the State of Health for the Lithium-Ion Battery Based on Open Circuit Model

Pan, Y., Li, J., Tian’an, W., Shengxun, Y., Yunxuan, Z., Zigang, L., & Yanwen, H. (2025) · IEEE Access · doi:10.1109/access.2025.3536166 · Cited by 18

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Tan, X., Liu, X., Wang, H., Fan, Y., & Feng, G. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.844985 · Cited by 18

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

Zou, L., Wen, B., Wei, Y., Zhang, Y., Yang, J. R., & Zhang, H. (2022) · Energies · doi:10.3390/en15062237 · Cited by 18

Voltage and temperature information ensembled probabilistic battery health evaluation via deep Gaussian mixture density network

Fei, Z., Huang, Z., & Zhang, X. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108587 · Cited by 18

A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Long, Z., Lian, Y., Yin, A., Zhou, J., & Song, L. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.110248 · Cited by 18

State of Health Estimation for Lithium-Ion Batteries Based on Improved Gaussian Process Regression

Pan, J., Tong, S., Li, Y., Tang, J., Xiong, B., & Chen, Y. (2021) · IEEE ECCE Asia Downunder · doi:10.1109/ecce-asia49820.2021.9479238 · Cited by 3

State of health estimation based on modified Gaussian process regression for lithium-ion batteries

Wang, J., Deng, Z., Yu, T., Yoshida, A., Xu, L., Guan, G., & Abudula, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104512 · Cited by 123

SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction

He, Y. S., Bai, W., Wang, L., Wu, H., & Ding, M. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110678 · Cited by 154

State of health estimation for lithium-ion batteries with Bayesian optimized Gaussian process regression

Ye, L.-H., He, Z., Ke, C.-L., Cheng, X., Zhao, X., Zhang, Z.-X., & Shi, A.-P. (2025) · Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering · doi:10.1177/09544070241287254 · Cited by 2

An Intelligent State of Health Estimation Method for Lithiumion Batteries

Li, H., Peng, R., Zhu, Z., & Zhu, R. (2023) · Advances in transdisciplinary engineering · doi:10.3233/atde230153