Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212

The development and future of lithium ion batteries

Blomgren, G. E. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.0251701jes · Cited by 1,940

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326

The environmental impact of Li-Ion batteries and the role of key parameters – A review

Peters, J. F., Baumann, M., Zimmermann, B., Braun, J., & Weil, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.08.039 · Cited by 856

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

A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 631

Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

Miao, Q., Xie, L., Cui, H., Liang, W. Z., & PECHT, M. G. (2012) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 479

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Khelif, R., Chebel‐Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Noureddine Zerhouni, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2623260 · Cited by 440

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794997 · Cited by 407

A review on the long short-term memory model

Houdt, G. V., Mosquera, C., & Nápoles, G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09838-1 · Cited by 2,198

Deep Learning for Time Series Forecasting: A Survey

Torres, J. F., Hadjout, D., Sebaa, A., Alvarez, F. M., & Troncoso, A. (2020) · Big Data · doi:10.1089/big.2020.0159 · Cited by 904

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Li, X., Yuan, C., Li, X., & Wang, Z. (2019) · Energy · doi:10.1016/j.energy.2019.116467 · Cited by 485

Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

Lipu, M. S. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., Miah, M. S., & Mahlia, T. M. I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126044 · Cited by 478

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

Ge, M., Liu, Y., Jiang, X., & Liu, J. (2021) · Measurement · doi:10.1016/j.measurement.2021.109057 · Cited by 432

A Novel Deep Learning Framework for State of Health Estimation of Lithium-Ion Battery

Fan, Y., Xiao, F., Li, C., Yang, G., & Tang, X. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101741 · Cited by 415

LSTM-Based battery remaining useful life prediction with multi-channel charging profiles

Park, K., Choi, Y., Choi, W. J., Ryu, H.-Y., & Kim, H. (2020) · IEEE Access · doi:10.1109/access.2020.2968939 · Cited by 397

Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

Zhang, S., Zhai, B., Guo, X., Wang, K., Peng, N., & Zhang, X. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100951 · Cited by 336

Remaining Useful Life Assessment for Lithium-Ion Batteries Using CNN-LSTM-DNN Hybrid Method

Zraibi, B., Okar, C., Chaoui, H., & Mansouri, M. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3071622 · Cited by 339

Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression

Li, X., Wang, Z., & Yan, J. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.03.008 · Cited by 317

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

Hasib, S. A., Islam, S., Chakrabortty, R. K., Ryan, M. J., Saha, D. K., Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., Islam, M. R., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3089032 · Cited by 281

A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis

Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228740 · Cited by 263

Predictive Battery Health Management With Transfer Learning and Online Model Correction

Che, Y., Deng, Z., Lin, X., Hu, L., & Hu, X. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055811 · Cited by 261

Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network

Cheng, G., Wang, X., & He, Y. (2021) · Energy · doi:10.1016/j.energy.2021.121022 · Cited by 261

An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., & Wu, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111843 · Cited by 261

State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network

Zhou, D., Li, Z., Zhu, J., Zhang, H., & Hou, L. (2020) · IEEE Access · doi:10.1109/access.2020.2981261 · Cited by 245

The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Li, X., Yu, D., Vilsen, S. B., & Ioan, S. D. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.03.026 · Cited by 243

One-shot battery degradation trajectory prediction with deep learning

Li, W., Sengupta, N., Dechent, P., Howey, D. A., Annaswamy, A. M., & Sauer, D. U. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230024 · Cited by 230

Remaining useful life prediction for supercapacitor based on long short-term memory neural network

Zhou, Y., Huang, Y., Pang, J., & Wang, K. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227149 · Cited by 227

Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems

Rahman, T., & Alharbi, T. (2024) · Batteries · doi:10.3390/batteries10070220 · Cited by 235

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu, X., Shen, S., Shen, J., Zhang, Y., Li, G., Chen, Z., & Liu, Y. (2021) · iScience · doi:10.1016/j.isci.2021.103265 · Cited by 217

Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors

Zhou, Y., Wang, Y., Wang, K., Kang, L., Peng, F., Wang, L., & Pang, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114169 · Cited by 201

Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

Khaleghi, S., Hosen, M. S., Karimi, D., Behi, H., Beheshti, S. H., Mierlo, J. V., & Berecibar, M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118348 · Cited by 199

Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

Lipu, M. S. H., Ansari, S., Miah, M. S., Meraj, S. T., Hasan, K., Shihavuddin, A., Hannan, M. A., Muttaqi, K. M., & Hussain, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105752 · Cited by 199

Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression

Wang, Z., Yuan, C., & Li, X. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3028784 · Cited by 198

An Integrated Method of the Future Capacity and RUL Prediction for Lithium-Ion Battery Pack

Zhang, C., Zhao, S., & He, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3138959 · Cited by 197

Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

Li, L., Li, Y. G., Mao, R., Li, L., Hua, W., & Zhang, J. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3247614 · Cited by 197

Temperature prediction of lithium-ion battery based on artificial neural network model

Wang, Y., Chen, X., Li, C., Yu, Y., Zhou, G., Wang, C., & Zhao, W. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.120482 · Cited by 183

An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement

Dang, W., Liao, S., Yang, B., Yin, Z., Liu, M., Yin, L., & Zheng, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106469 · Cited by 175

Thermal runaway behavior during overcharge for large-format Lithium-ion batteries with different packaging patterns

Huang, L., Zhang, Z., Wang, Z., Zhang, L., Zhu, X., & Dorrell, D. G. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100811 · Cited by 173

Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression

Li, X., Yuan, C., & Wang, Z. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228358 · Cited by 170

State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

Xia, F., Wang, K., & Chen, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107161 · Cited by 168

Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery

Chen, J. C., Chen, T., Liu, W., Cheng, C., & Li, M.-G. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101405 · Cited by 168

A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm

Liu, Y., Sun, J., Shang, Y., Zhang, X., Ren, S., & Wang, D. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106645 · Cited by 168

Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered

Hong, J., Wang, Z., Chen, W., Wang, L., Lin, P., & Qu, C. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125814 · Cited by 166

A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties

Alahyari, A., Ehsan, M., & Mousavizadeh, M. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100812 · Cited by 161

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

Wu, Y., Xue, Q., Shen, J., Lei, Z., Chen, Z., & Liu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2972344 · Cited by 160

Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network

Guo, F., Wu, X., Liu, L., Ye, J., Wang, T., Fu, L., & Wu, Y. (2023) · Energy · doi:10.1016/j.energy.2023.126880 · Cited by 159

Li-Ion Battery State of Health Estimation and Remaining Useful Life Prediction Through a Model-Data-Fusion Method

Lyu, Z., Gao, R., & Chen, L. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3033297 · Cited by 156

Online Remaining Useful Life Prediction of Lithium-Ion Batteries Using Bidirectional Long Short-Term Memory with Attention Mechanism

Wang, F., Amogne, Z. E., Chou, J., & Tseng, C. (2022) · Energy · doi:10.1016/j.energy.2022.124344 · Cited by 159

State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study

Toughzaoui, Y., Bamati, S., Chaoui, H., Louahlia, H., Petrone, R., Masson, S. L., & Gualous, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104520 · Cited by 140

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Ungurean, L., Micea, M. V., & Carstoiu, G. N. (2020) · International Journal of Energy Research · doi:10.1002/er.5413 · Cited by 140

Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning

Kim, S., Choi, Y. Y., Kim, K. J., & Choi, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102893 · Cited by 138

Battery heating for lithium-ion batteries based on multi-stage alternative currents

Zhang, L., Fan, W., Wang, Z., Li, W., & Sauer, D. U. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101885 · Cited by 133

SOH prediction for Lithium-Ion batteries by using historical state and future load information with an AM-seq2seq model

Qian, C., Xu, B., Xia, Q., Ren, Y., Sun, B., & Wang, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120793 · Cited by 130

A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery

Tang, T. H., & Yuan, H. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108082 · Cited by 127

A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity

Hou, J. Y., & Song, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113900 · Cited by 121

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Xu, Q., Wu, M., Khoo, E., Chen, Z., & Li, X. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123024 · Cited by 119

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

Shu, X., Li, G., Shen, J., Yan, W., Chen, Z., & Liu, Y. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228132 · Cited by 118

State of health prognostics for series battery packs: A universal deep learning method

Che, Y., Deng, Z., Li, P., Tang, X., Khosravinia, K., Lin, X., & Hu, X. (2022) · Energy · doi:10.1016/j.energy.2021.121857 · Cited by 116

Data-driven analysis on thermal effects and temperature changes of lithium-ion battery

Zhu, S., He, C., Zhao, N., & Sha, J. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228983 · Cited by 117

A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Pang, X., Liu, X., Jia, J., Wen, J., Shi, Y., Zeng, J., & Zhao, Z. (2021) · Microelectronics Reliability · doi:10.1016/j.microrel.2021.114405 · Cited by 116

Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review

Khaleghi, S., Hosen, M. S., Mierlo, J. V., & Berecibar, M. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114224 · Cited by 117

A convolutional neural network model for battery capacity fade curve prediction using early life data

Saxena, S., Ward, L. T., Kubal, J., Lu, W., Babinec, S., & Paulson, N. H. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231736 · Cited by 112

Remaining useful life prediction for lithium-ion battery storage system: A comprehensive review of methods, key factors, issues and future outlook

Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.043 · Cited by 116

Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network

Che, Y., Zheng, Y., Wu, Y., Sui, X., Bharadwaj, P., Stroe, D., Yang, Y., Hu, X., & Teodorescu, R. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119663 · Cited by 110

Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

Wang, H., Song, W., Zio, E., Kudreyko, A. A., & Zhang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107904 · Cited by 108

Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm

Zhou, Y., Wang, S., Xie, Y., Shen, X., & Fernández, C. (2023) · Energy · doi:10.1016/j.energy.2023.128761 · Cited by 107

Remaining Useful Life Prediction Based on Deep Learning: A Survey

Wu, F., Wu, Q., Tan, Y., & Xu, X. (2024) · Sensors · doi:10.3390/s24113454 · Cited by 122

Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors

Yang, H., Wang, P., An, Y., Shi, C., Sun, X., Wang, K., Zhang, X., Wei, T., & Ma, Y. (2020) · eTransportation · doi:10.1016/j.etran.2020.100078 · Cited by 107

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

Wang, S., Jin, S., Deng, D., & Fernández, C. (2021) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2021.719718 · Cited by 108

An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

Li, W., Jiao, Z., Du, L., Fan, W., & Zhu, Y. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.03.101 · Cited by 102

State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review

Shah, A., Shah, K., Shah, C., & Shah, M. (2022) · Renewable energy focus · doi:10.1016/j.ref.2022.06.001 · Cited by 99

Data-driven state of health modelling—A review of state of the art and reflections on applications for maritime battery systems

Vanem, E., Salucci, C. B., Bakdi, A., & Alnes, Ø. Å. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103158 · Cited by 100

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu, M. S. H., Miah, M. S., Jamal, T., Rahman, T., Ansari, S., Rahman, M. S., Ashique, R. H., Shihavuddin, A., & Shakib, M. N. (2023) · Vehicles · doi:10.3390/vehicles6010002 · Cited by 103

Research progress and application of deep learning in remaining useful life, state of health and battery thermal management of lithium batteries

He, W., Li, Z., Liu, T., Liu, Z., Guo, X., Du, J., Li, X., Sun, P., & Ming, W. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107868 · Cited by 97

Multivariate Stacked Bidirectional Long Short Term Memory for Lithium-Ion Battery Health Management

Ardeshiri, R. R., Liu, M., & Ma, C. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108481 · Cited by 94

Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves

Xiong, W., Xu, G., Li, Y., Zhang, F., Ye, P., & Li, B. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106790 · Cited by 93

A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network

Li, K., Wang, Y., & Chen, Z. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105333 · Cited by 94

Estimation of SoH and internal resistances of Lithium ion battery based on LSTM network

Văn, C. N., & Quang, D. T. (2023) · International Journal of Electrochemical Science · doi:10.1016/j.ijoes.2023.100166 · Cited by 93

Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM

Jafari, S., & Byun, Y.-C. (2023) · IEEE Access · doi:10.1109/access.2023.3304699 · Cited by 93

Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R., & Stroe, D. (2021) · Electronics · doi:10.3390/electronics10243126 · Cited by 95

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Empirical Mode Decomposition and Deep Neural Networks

Qiao, J., Liu, X., & Chen, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2977429 · Cited by 89

Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data

Chinomona, B., Chung, C., Chang, L.-K., Su, W.-C., & Tsai, M. (2020) · IEEE Access · doi:10.1109/access.2020.3022505 · Cited by 89

State-of-health estimation based on real data of electric vehicles concerning user behavior

He, Z., Shen, X., Sun, Y., Zhao, S., Fan, B., & Pan, C. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102867 · Cited by 82

Physics-based fractional-order model with simplified solid phase diffusion of lithium-ion battery

Guo, D., Yang, G., Feng, X., Han, X., Lu, L., & Ouyang, M. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101404 · Cited by 81

Li-ion battery degradation modes diagnosis via Convolutional Neural Networks

Costa, N., Sánchez, L., Anseán, D., & Dubarry, M. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105558 · Cited by 81

Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction

Wang, Z., Liu, N., & Guo, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.025 · Cited by 79

State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Kong, D., Wang, S., & Ping, P. (2021) · International Journal of Energy Research · doi:10.1002/er.7548 · Cited by 79

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

A state of health estimation framework based on real-world electric vehicles operating data

Zhao, X., Hu, J., Hu, G., & Qiu, H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107031 · Cited by 76

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

Chen, Y., Duan, W., He, Y., Wang, S., & Fernández, C. (2024) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2024.100160 · Cited by 75

Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory

Wang, H., Peng, M.-J., Miao, Z., Liu, Y.-K., Ayodeji, A., & Hao, C. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.08.031 · Cited by 74

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Xu, X., Yu, C., Tang, S., Sun, X., Si, X., & Wu, L. (2019) · Energies · doi:10.3390/en12091685 · Cited by 74

Lithium-Ion Battery Health State and Remaining Useful Life Prediction Based on Hybrid Model MFE-GRU-TCA

Wang, X., Dai, K., Hu, M., & Ni, N. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112442 · Cited by 72

Time Series Feature extraction for Lithium-Ion batteries State-Of-Health prediction

Jorge, I., Mesbahi, T., Samet, A., & Boné, R. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106436 · Cited by 72

Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression

Wei, M., Ye, M., Wang, Q., Xinxin-Xu, & Twajamahoro, J. P. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103558 · Cited by 71

A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles

Chen, D., Zhang, W., Zhang, C., Sun, B., Cong, X., Wei, S., & Jiang, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120114 · Cited by 71

Predictive pretrained transformer (PPT) for real-time battery health diagnostics

Zhao, J., Wang, Z., Wu, Y., & Burke, A. F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124746 · Cited by 79

A Novel Method for SoH Prediction of Batteries Based on Stacked LSTM with Quick Charge Data

Yayan, U., Arslan, A. T., & Yucel, H. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.1901033 · Cited by 70

Optimization of hybrid pulse power characterization profile for equivalent circuit model parameter identification of Li-ion battery based on Taguchi method

Sun, J., & Kainz, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108034 · Cited by 69

Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction

Wang, Y., Zhu, J., Cao, L., Gopaluni, R. B., & Cao, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121660 · Cited by 69

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 novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries

Shao, J., Li, J., Yuan, W.-Z., Dai, C., Wang, Z., Zhao, M. S., & PECHT, M. G. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106788 · Cited by 68

A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs

Esfandyari, M., Yazdi, M. R. H., Esfahanian, V., Masih‐Tehrani, M., Nehzati, H., & Shekoofa, O. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100758 · Cited by 67

Gradient boosted regression model for the degradation analysis of prismatic cells

Wang, F., & Mamo, T. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106494 · Cited by 68

An estimation model for state of health of lithium-ion batteries using energy-based features

Cai, L., Lin, J., & Liao, X. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103846 · Cited by 67

Comparative Study of Incremental Capacity Curve Determination Methods for Lithium-Ion Batteries Considering the Real-World Situation

Liu, P., Wu, Y., She, C., Wang, Z., & Zhang, Z. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3173464 · Cited by 65

State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review

Sylvestrin, G. R., Maciel, J. N., Amorim, M. L. M., Carmo, J. P., Afonso, J. A., Lopes, S. F., & Ando, O. H. (2025) · Energies · doi:10.3390/en18030746 · Cited by 68

Cost-Effective Lebesgue Sampling Long Short-Term Memory Networks for Lithium-Ion Batteries Diagnosis and Prognosis

Zhang, H., Niu, G., Zhang, B., & Miao, Q. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3060675 · Cited by 64

Validation and Verification of Remaining Useful Life Prediction for Lithium-Ion Batteries based on AM-CNN Approach

Prasanna Kumara, S. G., Kumar, A., Babavali, S. F., Patil, C. S., Murali, M. J., & Kumar, T. C. A. (2023) · International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · doi:10.1109/icssas57918.2023.10331670 · Cited by 2

Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network

Ma, G., Zhang, Y. A., Cheng, C., Zhou, B., Hu, P., & Yuan, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113626 · Cited by 343

Research on the Method of Remaining Useful Life Prediction of Lithium-ion Battery Based on LSTM

Guan, Y., & Liu, J. (2021) · International Conference on Advances in Computer Technology, Information Science and Communications (CTISC) · doi:10.1109/ctisc52352.2021.00049 · 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

Remaining useful life prediction of lithium-ion batteries using a CEEMDAN–Bi-LSTM hybrid model

Li, H., Cao, Y., Ren, X., Zhang, D., Xu, T., Su, Z., Jiang, Y., Wang, Y., Chen, B., Zhang, H., et al. (2026) · International Journal of Electrochemical Science · doi:10.1016/j.ijoes.2026.101427 · Cited by 2