A Novel Hybrid Neural Network-Based SOH and RUL Estimation Method for Lithium-Ion Batteries

Data-driven prediction of battery cycle life before capacity degradation

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A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

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A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters

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

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An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries

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Robust State of Health Estimation of Lithium-Ion Batteries Using Convolutional Neural Network and Random Forest

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Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles

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Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis

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A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries

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Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

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Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries

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Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction

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

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Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

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

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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

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model

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Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries

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A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

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Effective and practical parameters of electrochemical Li-ion battery models for degradation diagnosis

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Wearable Sensors based Exertion Recognition using Statistical Features and Random Forest for Physical Healthcare Monitoring

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State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter

He, Z., Li, Y., Sun, Y., Zhao, S., Lin, C., Pan, C., & Wang, L. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102593 · Cited by 81 · see citing works

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 · see citing works

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 · see citing works

Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model

Yang, P., Yang, H. D., Meng, X., Song, C. R., He, T., Cai, J., Xie, Y. Y., & Xu, K. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109741 · Cited by 69 · see citing works

An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries

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State of health prediction for li-ion batteries with end-to-end deep learning

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State of charge estimation for Li-ion batteries based on iterative Kalman filter with adaptive maximum correntropy criterion

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State of health estimation and prediction of electric vehicle power battery based on operational vehicle data

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

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Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology

Rincón-Maya, C., Carazas, F. J. G., Barajas, F. H., Rodríguez, C. E. P., & Manco, O. C. Ú. (2023) · Energies · doi:10.3390/en16207081 · Cited by 27 · see citing works

A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer

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A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data

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Remaining Useful Life Prediction of Lithium Battery via Neural Network Ensemble

Zhang, H., Wu, D., Wang, Z., Chen, Y., & Xu, J. (2021) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske54062.2021.9755395 · Cited by 8 · see citing works

SOH evaluation and RUL estimation of lithium-ion batteries based on MC-CNN-TimesNet model

Li, Y., Qin, X., Chai, M., Wu, H., Zhang, F., Jiang, F., & Wen, C. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111125 · Cited by 82 · see citing works

Battery state of health estimation under fast charging via deep transfer learning

Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works

A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors

Lixin, E., Wang, J., Yang, R., Wang, C., Li, H., & Xiong, R. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100291 · Cited by 53 · see citing works

SOH-KLSTM: A hybrid Kolmogorov-Arnold Network and LSTM model for enhanced Lithium-ion battery Health Monitoring

Jarraya, I., Atitallah, S. B., Alahmed, F., Abdelkader, M., Driss, M., Abdelhadi, F., & Koubâa, A. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116541 · Cited by 50 · see citing works

Interpretable deep learning with uncertainty quantification for lithium-ion battery SOH estimation

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Artificial intelligence for estimating State of Health and Remaining Useful Life of EV batteries: A systematic review

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State of health estimation for lithium-ion batteries based on fragmented charging data and improved gated recurrent unit neural network

Chen, Z., Yue, P., Shen, J., Zhang, Q., Liu, Y., Zhang, Y., Xia, X., & Liu, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115952 · Cited by 30 · see citing works

An enhanced deep learning framework for state of health and remaining useful life prediction of lithium-ion battery based on discharge fragments

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State of health prediction for Lithium-Ion batteries using extended long Short-Term memory network and frequency enhanced channel attention mechanism with Variational mode Decomposition

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A Critical Review of AI-Based Battery Remaining Useful Life Prediction for Energy Storage Systems

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Extending battery lifecycles: A holistic review of second-life lithium-ion technology in sustainable energy systems from assessment to emerging trends

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Prediction of SOH and RUL for Li-Ion Batteries in EV Based on AttentiveLSTM Multi-Task Model

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

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Performance degradation prediction of proton exchange membrane fuel cells based on CNN-LSTM network with squeeze-and-excitation attention mechanism

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Parallel-branch enhanced ShuffleNet with dual-physics constraints for lithium-ion battery RUL prediction

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Fusing coupled degradation mechanisms with machine learning: A multi-fidelity framework for lithium-ion battery lifespan prediction

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A Comprehensive Review of Equivalent Circuit Models and Neural Network Models for Battery Management Systems

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Real-time discharge curve and state of charge estimation of lithium-ion batteries via a physics-informed full homogenized macroscale model

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Integrating transformers into physics-informed neural networks: An approach to lithium-ion battery state-of-health prognostics

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A quantum-enhanced ensemble model to forecast battery state of health under varying discharging load

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A Multitask Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life

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Remaining useful life prediction of lithium-ion battery based on VMD-CNN-BiLSTM model and attention mechanism

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State of Health Estimation for Lithium-Ion Batteries Using Separable LogSparse Self-Attention Transformer

Chen, T., Zhang, C., Jing, W., Foo, E. Y. S., Lai, X., & Zeng, N. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3552877 · Cited by 13 · see citing works

Boosting battery health prediction in electric vehicles via extended quantum optimized model with gaussian process regression

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Incremental capacity feature selection for lithium-ion battery state of health estimation considering estimation capability and efficiency

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A multi-strategy attention regression network for joint prediction of state of health and remaining useful life of lithium-ion batteries using only charging data

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Multimodal EEG–EMG and FEM-Based Adaptive Control of Passive Upper-Limb Exoskeletons

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Recent Advances in Artificial Intelligence-Driven Prognostics and Health Management of Mobility Batteries

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Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: Validation with a multi-temperature aging dataset

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Ist-SBiLSTM: Multi-level stacking ensemble approach using gravitational search algorithm and BiLSTM for accurate state of health prediction in lithium-ion batteries

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State estimation of Lithium-ion Batteries with state space model

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High-Fidelity SOH Prediction in Lithium-Ion Batteries Using Hybrid ML Networks

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Joint Estimation of Lithium‐Ion Battery Health Status and Remaining Service Life by Transfer Learning Based on PatchTST and Dynamic Weighted MSE Loss Function

Zhang, K., & Wang, X. (2025) · Energy Science & Engineering · doi:10.1002/ese3.70177 · Cited by 6 · see citing works

A deep learning framework integrated with a composite health indicator for lithium-ion battery life prediction

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SG-DiTs: A physics-informed diffusion-transformer hybrid framework for reliable battery RUL prediction

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Hybrid experts-decoupled and physics-informed neural network for lithium-ion battery degradation modeling and prognosis

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State-of-Health Estimation for Li-Ion Batteries of Real-World Electric Vehicles: Progress, Challenges, and Prospects

Zhu, R., Shaukat, H., Zahira, F., Huzefa, H. M., Kaleem, M. B., & Li, H. (2026) · Batteries · doi:10.3390/batteries12050174 · Cited by 6 · see citing works

A multi-objective optimization model for predictive opportunistic maintenance of lithium-ion batteries

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A dual-exponential EKF model for lithium-ion battery RUL prediction with Bayesian optimization

Yuan, N., Mao, R., Han, P., Xu, W., Li, Y. G., Guo, Y., & Zhang, H. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae00e6 · Cited by 5 · see citing works

Lithium-ion battery RUL prediction based on sequence decomposition and the PatchTST-MKAN model

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Battery SOH estimation via an optimized CNN–BiLSTM–Attention network using ICA-Based ageing features

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Machine learning toward electrochemical energy storage materials and devices: A review

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Transfer learning for state-of-health prognostics of lithium-ion batteries: A survey

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Convolutional Neural Network - Gated Recurrent Unit combined with Error Correction for Lithium Battery State of Health Estimation

Luo, X., Bu, W., Han, L., & Zheng, M. (2025) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein/202184 · Cited by 5 · see citing works

State of health estimation of lithium-ion batteries based on BKA-FSVR algorithm with feature reconstruction from partial constant current charging interval

Wang, Y., Zhang, X., Chen, D., & Yang, J. (2025) · Energy · doi:10.1016/j.energy.2025.137888 · Cited by 5 · see citing works

Physics-Informed Hybrid Neural Architecture for Coupled Degradation Modeling and Remaining Useful Life Prediction of LiFePO4 Batteries

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Hybrid Neural Networks for Lithium-Ion Batteries State of Health Estimation and Remaining Useful Life Prediction

Zhu, S., Zhang, X., & Wang, Y. (2025) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae0feb · Cited by 4 · see citing works

Transfer learning with composite kernel sparse Gaussian process-aided model for probabilistic state of health estimation of lithium-ion batteries against multi-source coupled harsh scenarios

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Estimation of Lithium Battery Health Status and Prediction of Remaining Useful Life Based on Improved PINN

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State of Health Estimation Method for Lithium-Ion Batteries Based on BiLSTM-Transformer and Fusion Features

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A bayesian Mamba model with time windowing for lithium-ion battery state of health Estimation

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