Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

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 · see citing works

Practical Nonparametric Statistics

Groggel, D. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271101 · Cited by 11,071 · see citing works

Time Series Analysis: Forecasting and Control

Kleiner, B., Box, G. E. P., & Jenkins, G. M. (1977) · Technometrics · doi:10.2307/1267710 · Cited by 6,447 · see citing works

Time Series Analysis: Forecasting and Control.

Anderson, O. D., Box, G. E. P., & Jenkins, G. M. (1978) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988198 · Cited by 5,368 · see citing works

Practical Nonparametric Statistics

Coakley, C. W., & Conover, W. J. (2000) · Journal of the American Statistical Association · doi:10.2307/2669565 · Cited by 1,406 · see citing works

Time Series Analysis Forecasting and Control.

Chakravarti, I. M., Box, G. E. P., & Jenkins, G. M. (1973) · Journal of the American Statistical Association · doi:10.2307/2284112 · Cited by 992 · see citing works

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 · see citing works

An ensemble model for predicting the remaining useful performance of lithium-ion batteries

Xing, Y., Eden, W. M., Tsui, K., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 675 · see citing works

Prognostics in Battery Health Management

Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594 · see citing works

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

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

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 · see citing works

A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer

Kim, I.-S. (2009) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2009.2034966 · Cited by 438 · see citing works

Comparison of prognostic algorithms for estimating remaining useful life of batteries

Saha, B., Goebel, K., & Christophersen, J. P. (2009) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331208092030 · Cited by 423 · see citing works

Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

Wang, D., Miao, Q., & Pecht, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.03.129 · Cited by 351 · see citing works

A naive Bayes model for robust remaining useful life prediction of lithium-ion battery

Ng, S. S. Y., Xing, Y., & Tsui, K. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.12.020 · Cited by 340 · see citing works

Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310 · see citing works

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter

Wang, D., Yang, F., Tsui, K., Zhou, Q., & Bae, S. J. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2534258 · Cited by 285 · see citing works

An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274 · see citing works

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Liu, D., Zhou, J., Pan, D., Peng, Y., & Peng, X. (2015) · Measurement · doi:10.1016/j.measurement.2014.11.031 · Cited by 266 · cited 10× · see citing works

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Liao, L., & Köttig, F. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.03.013 · Cited by 259 · see citing works

Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

Liu, D., Luo, Y., Liu, J., Peng, Y., Guo, L., & PECHT, M. G. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1520-x · Cited by 226 · see citing works

Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

Olivares, B. E., Cerda, M. A., Orchard, M. E., & Silva, J. F. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2215142 · Cited by 217 · see citing works

Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

Liu, D., Wang, H., Peng, Y., Xie, W. H., & Liao, H. (2013) · Energies · doi:10.3390/en6083654 · Cited by 186 · see citing works

An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation

Liu, D., Xie, W. H., Liao, H., & Peng, Y. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2348613 · Cited by 171 · see citing works

An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system

Liu, H., Tian, H.-Q., & Li, Y.-F. (2015) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2015.02.004 · Cited by 170 · see citing works

Method for estimating capacity and predicting remaining useful life of lithium-ion battery

Hu, C. K., Jain, G., Tamirisa, P. A., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.086 · Cited by 162 · see citing works

Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions

Liu, H., Tian, H.-Q., & Li, Y.-F. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.09.060 · Cited by 157 · see citing works

A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter

Li, F., & Xu, J. (2015) · Microelectronics Reliability · doi:10.1016/j.microrel.2015.02.025 · Cited by 150 · see citing works

Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh

Nury, A. H., Hasan, K., & Alam, M. J. B. (2015) · Journal of King Saud University - Science · doi:10.1016/j.jksus.2015.12.002 · Cited by 149 · see citing works

Comparison of a particle filter and other state estimation methods for prognostics of lithium-ion batteries

Walker, E. A., Rayman, S. C., & White, R. E. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.04.020 · Cited by 141 · see citing works

Application of support vector regression machines to the processing of end effects of Hilbert–Huang transform

Cheng, J., Yu, D., & Yang, Y. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.005 · Cited by 141 · see citing works

ARMA model order estimation based on the eigenvalues of the covariance matrix

Liang, G., Wilkes, D. M., & CADZOW, J. A. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.277805 · Cited by 140 · see citing works

Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques

Kozlowski, J. D. (2004) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2003.1234169 · Cited by 136 · see citing works

State of health estimation of lithium‐ion batteries: A multiscale G aussian process regression modeling approach

He, Y., Shen, J.-N., Shen, J., & Ma, Z. (2015) · AIChE Journal · doi:10.1002/aic.14760 · Cited by 117 · see citing works

A particle filtering and kernel smoothing-based approach for new design component prognostics

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2014) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2014.10.003 · Cited by 116 · see citing works

Information-Theoretic Measures and Sequential Monte Carlo Methods for Detection of Regeneration Phenomena in the Degradation of Lithium-Ion Battery Cells

Orchard, M. E., Lacalle, M., Olivares, B. E., Silva, J. F., Palma-Behnke, R. E., Estévez, P. A., Severino, B., Calderón-Muñoz, W. R., & Cortés-Carmona, M. (2015) · IEEE Transactions on Reliability · doi:10.1109/tr.2015.2394356 · Cited by 40 · see citing works

An optimized Relevance Vector Machine with incremental learning strategy for lithium-ion battery remaining useful life estimation

Zhou, J., Liu, D., Peng, Y., & Peng, X. (2013) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2013.6555479 · Cited by 31 · see citing works

Mitigating end effects of EMD using non-equidistance grey model

He, Z., Shen, Y., Wang, Q., Wang, Y., Feng, N., & Ma, L. (2012) · Journal of Systems Engineering and Electronics · doi:10.1109/jsee.2012.00075 · Cited by 15 · see citing works

Remaining useful life estimation of lithium-ion battery using exemplar-based conditional particle filter

Liu, Z., Sun, G., Bu, S., & Zhang, C. (2015) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2015.7245046 · Cited by 4 · see citing works

Adaptive Boundary Effect Processing For Empirical Mode Decomposition Using Template Matching

Ye, Y. (2013) · Applied Mathematics & Information Sciences · doi:10.12785/amis/071l10 · Cited by 3 · see citing works

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320 · see citing works

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193 · see citing works

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Liu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2973876 · Cited by 838 · see citing works

A review of the state of health for lithium-ion batteries: Research status and suggestions

Tian, H., Qin, P., Li, K., & Zhao, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120813 · Cited by 654 · see citing works

Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network

Liu, H., Mi, X., & Li, Y.-F. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.053 · Cited by 476 · see citing works

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 425 · see citing works

A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery

Qu, J., Liu, F., Ma, Y., & Fan, J. (2019) · IEEE Access · doi:10.1109/access.2019.2925468 · Cited by 400 · see citing works

A review on prognostics and health management (PHM) methods of lithium-ion batteries

Meng, H., & Li, Y. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109405 · Cited by 393 · see citing works

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Rauf, H., Khalid, M., & Arshad, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111903 · Cited by 331 · see citing works

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 · see citing works

Turning waste into wealth: A systematic review on echelon utilization and material recycling of retired lithium-ion batteries

Lai, X., Huang, Y., Gu, H., Deng, C., Han, X., Feng, X., & Zheng, Y. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.05.010 · Cited by 253 · see citing works

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter

Zhang, L., Mu, Z., & Sun, C. (2018) · IEEE Access · doi:10.1109/access.2018.2816684 · Cited by 252 · see citing works

Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning

Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C., Tsou, N., Sun, F., & Li, J. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.05.007 · Cited by 246 · see citing works

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 236 · see citing works

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Health Indicator and Gaussian Process Regression Model

Liu, J., & Chen, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2905740 · Cited by 215 · see citing works

Remaining Useful Life Estimation for Prognostics of Lithium-Ion Batteries Based on Recurrent Neural Network

Catelani, M., Ciani, L., Fantacci, R., Patrizi, G., & Picano, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3111009 · Cited by 217 · see citing works

Critical summary and perspectives on state-of-health of lithium-ion battery

Yang, B. F., Qian, Y., Li, Q., Chen, Q., Wu, J., Luo, E., Xie, R., Zheng, R., Yan, Y., Shi, S., et al. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114077 · Cited by 209 · see citing works

Lithium-ion batteries remaining useful life prediction based on BLS-RVM

Chen, Z., Shi, N., Ji, Y., Niu, M., & Wang, Y. (2021) · Energy · doi:10.1016/j.energy.2021.121269 · Cited by 197 · see citing works

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Song, Y., Liu, D., Chen, Y., & Peng, Y. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.06.045 · Cited by 195 · see citing works

Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method

Tong, Z., Miao, J., Tong, S., & Lü, Y. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128265 · Cited by 169 · see citing works

Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection

Zhang, J., Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3202642 · Cited by 170 · see citing works

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 · see citing works

A critical review on self-adaptive Li-ion battery ageing models

Lucu, M., Martinez-Laserna, E., Gandiaga, I., & Camblong, H. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.08.064 · Cited by 166 · see citing works

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter

Wu, Y., Li, W., Wang, Y., & Zhang, K. (2019) · IEEE Access · doi:10.1109/access.2019.2913163 · Cited by 168 · see citing works

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 · see citing works

Remaining life prediction of lithium-ion batteries based on health management: A review

Song, K., Hu, D., Tong, Y., & Yue, X. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106193 · Cited by 153 · see citing works

Short-term wind speed forecasting based on long short-term memory and improved BP neural network

Chen, G., Tang, B., Zeng, X., Zhou, P., Kang, P., & Long, H. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107365 · Cited by 149 · see citing works

Combining multiple deep learning algorithms for prognostic and health management of aircraft

Che, C., Wang, H., Fu, Q., & Ni, X. (2019) · Aerospace Science and Technology · doi:10.1016/j.ast.2019.105423 · Cited by 146 · see citing works

Aging mechanisms, prognostics and management for lithium-ion batteries: Recent advances

Wang, Y., Xiang, H., Soo, Y.-Y., & Fan, X. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114915 · Cited by 142 · see citing works

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Ji, C., & Sun, W. H. (2022) · Processes · doi:10.3390/pr10020335 · Cited by 143 · see citing works

State of Health estimation for lithium-ion batteries using Random Forest and Gated Recurrent Unit

Wang, X., Hu, B., Su, X., Xu, L., & Zhu, D. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109796 · Cited by 135 · see citing works

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

Chen, Z., Sun, M., Shu, X., Xiao, R., & Shen, J. (2018) · Applied Sciences · doi:10.3390/app8060925 · Cited by 134 · see citing works

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 · see citing works

A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries

Wang, F., & Mamo, T. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.08.073 · Cited by 127 · see citing works

Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy

Basia, A., Simeu-Abazi, Z., Gascard, E., & Zwolinski, P. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.02.004 · Cited by 125 · see citing works

State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network

Chen, Z., Xue, Q., Xiao, R., Liu, Y., & Shen, J. (2019) · IEEE Access · doi:10.1109/access.2019.2930680 · Cited by 120 · see citing works

Prognostics and health management for predictive maintenance: A review

Huang, C., Bu, S., Lee, H. H., Chan, C.-H., Kong, S. W., & Yung, W. K. C. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.05.021 · Cited by 135 · see citing works

Remaining useful life prediction of lithium-ion batteries based on Monte Carlo Dropout and gated recurrent unit

Wei, M., Gu, H., Ye, M., Wang, Q., Xu, X., & Wu, C. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.05.019 · Cited by 116 · see citing works

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 114 · see citing works

A long short-term memory neural network based Wiener process model for remaining useful life prediction

Chen, X., & Liu, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108651 · Cited by 113 · see citing works

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 111 · see citing works

Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries

Zhao, J., Zhu, Y., Zhang, B., Liu, M., Wang, J., Liu, C., & Hao, X. (2023) · Sustainability · doi:10.3390/su15065014 · Cited by 106 · see citing works

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 105 · see citing works

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

Zhou, D., Al‐Durra, A., Zhang, K., Ravey, A., & Gao, F. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2893867 · Cited by 99 · see citing works

Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction

Song, S., Fei, C., & Xia, H. (2020) · Energies · doi:10.3390/en13040812 · Cited by 97 · see citing works

A Data-Driven Method With Mode Decomposition Mechanism for Remaining Useful Life Prediction of Lithium-Ion Batteries

Wang, J., Zhang, S., Li, C., Wu, L., & Wang, Y. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3183886 · Cited by 97 · see citing works

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 98 · see citing works

Long-range dependence and heavy tail characteristics for remaining useful life prediction in rolling bearing degradation

Song, W., He, L., & Zio, E. (2021) · Applied Mathematical Modelling · doi:10.1016/j.apm.2021.09.041 · Cited by 92 · see citing works

Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework

Lyu, G., Zhang, H., & Miao, Q. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109315 · Cited by 90 · see citing works

State of Health Estimation for Lithium-Ion Battery Using Empirical Degradation and Error Compensation Models

Jiang, Y., Zhang, J., Xia, L., & Liu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3005229 · Cited by 89 · see citing works

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 88 · see citing works

Residual life prediction of lithium-ion batteries based on data preprocessing and a priori knowledge-assisted CNN-LSTM

Xie, Q., Liu, R., Huang, J., & Su, J. (2023) · Energy · doi:10.1016/j.energy.2023.128232 · Cited by 88 · see citing works

State-of-health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm

Hong, S., Qin, C., Lai, X., Meng, Z., & Dai, H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107179 · Cited by 86 · see citing works

Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Wang, C., & Chen, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123280 · Cited by 86 · see citing works

A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization

Xiao, J., Jiang, C., & Wang, B. (2023) · Batteries · doi:10.3390/batteries9010057 · Cited by 81 · see citing works

A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries

Ma, G., Wang, Z., Liu, W., Fang, J., Zhang, Y. A., Ding, H., & Yuan, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110012 · Cited by 81 · see citing works

A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation

Cong, X., Zhang, C., Jiang, J., Zhang, W., & Jiang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3024019 · Cited by 80 · see citing works

A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

Zhao, L. H., Wang, Y., & Cheng, J. (2019) · Applied Sciences · doi:10.3390/app9091890 · Cited by 80 · see citing works

A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

Cui, S., & Joe, I. (2021) · IEEE Access · doi:10.1109/access.2021.3058018 · Cited by 83 · see citing works

Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process

Zhang, S.-J., Kang, R., & Lin, Y. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107440 · Cited by 78 · see citing works

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 · see citing works

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

Feng, H., & Shi, G. (2021) · Journal of Power Electronics · doi:10.1007/s43236-021-00318-5 · Cited by 76 · see citing works

An Improved Generic Hybrid Prognostic Method for RUL Prediction Based on PF-LSTM Learning

Xue, K., Yang, J., Yang, M., & Wang, D. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3251391 · Cited by 75 · see citing works

Deep Learning Prognostics for Lithium-Ion Battery Based on Ensembled Long Short-Term Memory Networks

Liu, Y., Zhao, G., & Peng, X. (2019) · IEEE Access · doi:10.1109/access.2019.2937798 · Cited by 75 · see citing works

A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion

Han, X., Wang, Z., & Wei, Z. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117511 · Cited by 75 · see citing works

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2021) · Energies · doi:10.3390/en14227521 · Cited by 75 · see citing works

A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

Fan, J., Fan, J., Liu, F., Qu, J., & Li, R. (2019) · IEEE Access · doi:10.1109/access.2019.2947843 · Cited by 77 · see citing works

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 74 · see citing works

Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining

Wang, Y., Hei, C., Liu, H., Zhang, S., & Wang, J. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3182135 · Cited by 70 · see citing works

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 · see citing works

Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

Guo, L., He, H., Ren, Y., Li, R., Jiang, B., & Gong, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107317 · Cited by 69 · see citing works

Method of Predicting SOH and RUL of Lithium-Ion Battery Based on the Combination of LSTM and GPR

Zhao, J., Zhu, Y., Zhang, B., Liu, M., Wang, J., Liu, C., & Zhang, Y. (2022) · Sustainability · doi:10.3390/su141911865 · Cited by 68 · see citing works

Unraveling the Degradation Mechanisms of Lithium-Ion Batteries

Júnior, C. A. R., Sanseverino, E. R., Gallo, P., Amaral, M. M., Koch, D., Kotak, Y., Diel, S., Walter, G., Schweiger, H., & Zanin, H. (2024) · Energies · doi:10.3390/en17143372 · Cited by 69 · see citing works

A hybrid approach based on deep neural network and double exponential model for remaining useful life prediction

Liang, J., Liu, H., & Xiao, N. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123563 · Cited by 68 · see citing works

Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy

Luping, C., Xu, L., & Zhou, Y. (2018) · Energies · doi:10.3390/en11040820 · Cited by 66 · see citing works

A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments

Liu, Y., Hou, B., Ahmed, M., Mao, Z., Feng, J., & Chen, Z. (2024) · Applied Energy · doi:10.1016/j.apenergy.2023.122555 · Cited by 65 · see citing works

Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation

Zhou, Y. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.12.026 · Cited by 65 · see citing works

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 · see citing works

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 · see citing works

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Capacity Estimation and Box-Cox Transformation

Xue, Q., Shen, S., Li, G., Zhang, Y., Chen, Z., & Liu, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3039553 · Cited by 57 · see citing works

A data-driven framework for lithium-ion battery RUL using LSTM and XGBoost with feature selection via Binary Firefly Algorithm

Jin, Z., Li, X., Qiu, Z., Fei, L., Kong, E., & Li, B. (2024) · Energy · doi:10.1016/j.energy.2024.134229 · Cited by 61 · see citing works

State‐of‐Health Prediction for Lithium‐Ion Batteries Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise‐Gate Recurrent Unit Fusion Model

Xia, F., Wang, K., & Chen, J. (2022) · Energy Technology · doi:10.1002/ente.202100767 · Cited by 54 · see citing works

A new weighted CEEMDAN-based prediction model: An experimental investigation of decomposition and non-decomposition approaches

Jun, W., Luo, Y., Lingyu, T., & Ge, P. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.033 · Cited by 52 · see citing works

Li-ion battery prognostic and health management through an indirect hybrid model

Lyu, Z., Wang, G., & Gao, R. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102990 · Cited by 52 · see citing works

Capacity and remaining useful life prediction for lithium-ion batteries based on sequence decomposition and a deep-learning network

Wang, Z., Liu, Y., Wang, F., Wang, H., & Su, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108085 · Cited by 52 · see citing works

Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2021) · Sustainability · doi:10.3390/su132313333 · Cited by 50 · see citing works

Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

Kim, S., Lee, P., Lee, M.-Y., Kim, J., & Na, W. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103888 · Cited by 49 · see citing works

Prognostics of Lithium-Ion Batteries Based on Capacity Regeneration Analysis and Long Short-Term Memory Network

Cui, Y., & Chen, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3154003 · Cited by 46 · see citing works

Cycle life estimation of lithium-ion polymer batteries using artificial neural network and support vector machine with time-resolved thermography

Zhou, X., Hsieh, S., Peng, B., & Hsieh, D. H. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.10.013 · Cited by 46 · see citing works

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Ma, B., Zhang, L., Yu, H., Zou, B., Wang, W., Zhang, C., Yang, S., & Liu, X. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.02.052 · Cited by 45 · see citing works

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

Wang, Y., Ni, Y., Li, N., Lu, S., Zhang, S., Feng, Z., & Wang, J. (2019) · Energy Science & Engineering · doi:10.1002/ese3.460 · Cited by 45 · see citing works

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 · see citing works

Remaining useful life prediction of lithium-ion battery based on fusion model considering capacity regeneration phenomenon

He, N., Yang, Z., Qian, C., Li, R., Gao, F., & Cheng, F. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111068 · Cited by 43 · see citing works

A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction

Hu, X., Yang, X. P. O., Feng, F., Liu, K., & Lin, X. (2020) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4049234 · Cited by 43 · see citing works

A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

Wang, X., Li, J., Shia, B., Kao, Y., Ho, C.-W., & Chen, M. (2021) · Processes · doi:10.3390/pr9122174 · Cited by 43 · see citing works

Research on state of health prediction model for lithium batteries based on actual diverse data

Zhou, D., Zheng, W., Chen, S., Fu, P., Zhu, H., Song, B., Qu, X., & Wang, T. (2021) · Energy · doi:10.1016/j.energy.2021.120851 · Cited by 42 · see citing works

Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM

Ouyang, M., & Shen, P. (2022) · Energies · doi:10.3390/en15238918 · Cited by 42 · see citing works

Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

Yu, J., Mo, B., Tang, D., Liu, H., & Wan, J. (2017) · Quality Engineering · doi:10.1080/08982112.2017.1322210 · Cited by 40 · see citing works

Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data

Zhang, H., Su, Y., Altaf, F., Wik, T., & Gros, S. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3226683 · Cited by 40 · see citing works

Multi‐Scale Prediction of RUL and SOH for Lithium‐Ion Batteries Based on WNN‐UPF Combined Model

Jia, J., Keke, W., Xiaoqiong, P., Shi, Y., Jie, W., & Jianchao, Z. (2021) · Chinese Journal of Electronics · doi:10.1049/cje.2020.10.012 · Cited by 39 · see citing works

Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm

Li, Z., Cai, Z., Zheng, J., Shen, S., Dong, W., & Liu, D. (2023) · Energy Technology · doi:10.1002/ente.202300232 · Cited by 9 · see citing works

Research on the prediction method of lithium-ion battery remaining life based on EMD-Arima

Liu, Z., Zhang, J., & Feng, F. (2022) · International Conference on Manufacturing, Industrial Automation and Electronics (ICMIAE) · doi:10.1109/icmiae57032.2022.00077 · Cited by 1 · see citing works

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 88 · see citing works

Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining

Wang, Y., Hei, C., Liu, H., Zhang, S., & Wang, J. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3182135 · Cited by 70 · see citing works

Capacity prediction of lithium-ion batteries based on ensemble empirical mode decomposition and hybrid machine learning

Gao, K., Sun, J., Huang, Z., & Liu, C. (2024) · Ionics · doi:10.1007/s11581-024-05768-y · Cited by 10 · see citing works