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

Cited by (285)

Items 101–200 of 285

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2. A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge
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3. On-board state of health estimation for lithium-ion batteries based on random forest
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4. An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration
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5. Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis
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6. State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review
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7. RUL prediction for lithium-ion batteries based on variational mode decomposition and hybrid network model
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8. Degradation path prediction of lithium-ion batteries under dynamic operating sequences
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9. Integrated approaches for lithium-ion battery state estimation and life prediction: A critical review of model-driven, data-driven, and hybrid techniques
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10. State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach
    Yun, Zhonghua, Qin, Wenhu, Shi, Weipeng, Ping, Peng · 2020 · Energies
11. Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm
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12. Probabilistic Analysis for Remaining Useful Life Prediction and Reliability Assessment
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13. Prediction of remaining useful life for lithium-ion battery with multiple health indicators
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14. Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health
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15. Uniqueness of multilayer perceptron-based capacity prediction for contributing state-of-charge estimation in a lithium primary battery
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16. Lithium-ion batteries remaining useful life prediction using Wiener process and unscented particle filter
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17. Safety and reliability analysis of lithium-ion batteries with real-time health monitoring
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18. A Probabilistic Framework for Remaining Useful Life Prediction of Bearings
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19. Optimized data-driven approach for remaining useful life prediction of Lithium-ion batteries based on sliding window and systematic sampling
    Shaheer Ansari, Afida Ayob, Molla Shahadat Hossain Lipu, Aini Hussain, Maher G. M. Abdolrasol, Muhammad Ammirrul Atiqi Mohd Zainuri, Mohamad Hanif Md Saad · 2023 · Journal of Energy Storage
20. A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods
    Liyuan Shao, Yong Zhang, Xiujuan Zheng, Xin James He, Yufeng Zheng, Zhi‐Wei Liu · 2023 · Energies
21. Progress in the prognosis of battery degradation and estimation of battery states
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22. State of Health Estimation of Lithium-Ion Batteries Based on Fixed Size LS-SVM
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23. Remaining useful life prediction of high-capacity lithium-ion batteries based on incremental capacity analysis and Gaussian kernel function optimization
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24. Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model
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25. CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
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26. Counterfactual explanations for remaining useful life estimation within a Bayesian framework
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27. Review on State of charge prediction of battery management system in electric vehicles
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28. Remaining Useful Life Prediction of Lithium-Ion Batteries: A Temporal and Differential Guided Dual Attention Neural Network
    Tianyu Wang, Zhongjing Ma, Suli Zou · 2023 · IEEE Transactions on Energy Conversion
29. Deep learning to predict battery voltage behavior after uncertain cycling-induced degradation
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30. An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging
    Wenwen Zhang, Jianfang Jia, Xiaoqiong Pang, Jie Wen, Yuanhao Shi, Jianchao Zeng · 2024 · Electronics
31. Predicting the Future Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Deep Transfer Learning
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32. An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving
    Xinwei Cong, Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yan Jiang, Xinyu Jia · 2020 · IEEE Access
33. Degradation Modeling With Long-Term Memory Considering Measurement Errors
    Yunfei Shao, Wujun Si · 2021 · IEEE Transactions on Reliability
34. A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries
    Xiaoxian Pang, Shi Zhong, Yali Wang, Wei Bo Yang, Wenzhi Zheng, Gengzhi Sun · 2022 · The Chemical Record
35. A Method for Predicting the Remaining Useful Life of Lithium Batteries Considering Capacity Regeneration and Random Fluctuations
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36. Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches
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37. Explainable RUL estimation of turbofan engines based on prognostic indicators and heterogeneous ensemble machine learning predictors
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38. State of Health Estimation Based on OS-ELM for Lithium-ion Batteries
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39. A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks
    Pei Wang, Xue Dan, Yong Yang · 2019 · International Journal of Distributed Sensor Networks
40. Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
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41. Interpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights
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42. Towards digitized electrochemical power source for electric vehicles
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43. Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data
    Lin Zou, Baoyi Wen, Yiying Wei, Yong Zhang, Jie Ren Yang, Hui Zhang · 2022 · Energies
44. A novel Long-term degradation trends predicting method for Multi-Formulation Li-ion batteries based on deep reinforcement learning
    Chao Wang, Yu Ding, Ning Yan, Liang Ma, Jian Ma, Chen Lü, Chao Yang, Yuzhuan Su, Jin Chong, Haizu Jin et al. · 2022 · Advanced Engineering Informatics
45. Validation of RUL estimation method for battery prognostic under different fast-charging conditions
    Gabriele Patrizi, Benedetta Picano, Marcantonio Catelani, Romano Fantacci, Lorenzo Ciani · 2022 · IEEE International Instrumentation and Measurement Technology Conference
46. A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality
    Jie Su, Ziyu Lin, Fengwei Xu, Gholamreza Fathi, Khalid A. Alnowibet · 2024 · Scientific Reports
47. A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries
    Muhammad Umair Ali, Amad Zafar, Haris Masood, Karam Dad Kallu, Muhammad Attique Khan, Usman Tariq, Ye Jin Kim, Byoungchol Chang · 2022 · Computational Intelligence and Neuroscience
48. A Novel Remaining Useful Life Prediction Method for Capacity Diving Lithium-Ion Batteries
    Kaidi Gao, Jingyun Xu, Zuxin Li, Zhiduan Cai, Dongming Jiang, Aigang Zeng · 2022 · ACS Omega
49. Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm
    Chao Wang, Xin Zhang, Xiang Yun, Xiangfei Meng, Xingming Fan · 2023 · Energy
50. Lithium-ion batteries remaining useful life prediction via Fourier-mixed window attention enhanced Informer with decomposition and adaptive error correction strategy
    Fang Cheng, Hui Liu, Xinwei Lv · 2025 · Advanced Engineering Informatics
51. An Adaptive Noise Reduction Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries
    Wenyu Qu, Guici Chen, Tingting Zhang · 2022 · Energies
52. Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery
    Komal Mohan Garse, Kedar Narayan Bairwa, Anindita Roy · 2024 · Journal of Electrical Systems
53. Review on RUL Prediction Methods for Lithium-ion Battery
    Jiahui Zhao, Liting Tian, Lin Cheng, Yongmi Zhang, Changyu Zhu · 2022 · IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)
54. A RUL prediction method for lithium-ion batteries based on improved singular spectrum analysis and CSA-KELM
    Guorong Ding, Hongxia Chen · 2023 · Microelectronics Reliability
55. Remaining useful life Prediction for lithium-ion battery based on CEEMDAN and SVR
    Yuanhao Shi, Yanru Yang, Jie Wen, Fangshu Cui, Jingcheng Wang · 2020 · IEEE International Conference on Industrial Informatics (INDIN)
56. Dynamic condition-based maintenance policy for degrading systems described by a random-coefficient autoregressive model: A comparative study
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57. Research on hybrid data-driven method for predicting the remaining useful life of lithium-ion batteries
    Y. G. Li, Liping Li, Lei Li, Xinyu Huang, Guodong Sun, Yina Wang, Jinglin Zhang · 2025 · Computer Physics Communications
58. The Remaining Useful Life Estimation of Lithium-ion Batteries Based on the HKA -ML-ELM Algorithm
    Yanying Ma, Dongxu Shen, Lifeng Wu, Yong Guan, Hong Zhu · 2019 · International Journal of Electrochemical Science
59. A model to determining the remaining useful life of rotating equipment, based on a new approach to determining state of degradation
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60. State of Health Prediction of Lithium-Ion Batteries Using Accelerated Degradation Test Data
    Pasquale De Falco, Luigi Pio Di Noia, R. Rizzo · 2021 · IEEE Transactions on Industry Applications
61. Neural Turing Machines for the Remaining Useful Life estimation problem
    Alex Falcon, Giovanni D’Agostino, Oswald Lanz, Giorgio Brajnik, Carlo Tasso, Giuseppe Serra · 2022 · Computers in Industry
62. Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques
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63. Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter
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64. Fading analysis of the Li(NiCoMn)O2 battery under different SOC cycle intervals
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65. Interpretable AI for explaining and predicting battery state of health using PSO-enhanced deep learning models
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66. Improved lithium-ion battery health prediction with data-based approach
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67. State of health estimation for the lithium-ion batteries based on CNN-MLP network
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68. Predicting Automotive Battery Degradation Trajectories Using Transformer With Variational Auto-Encoder Based Data Augmentation
    Haoxiang Xiang, Yujie Wang, Yin‐Yi Soo, Xin Xiong, Zonghai Chen · 2025 · IEEE Transactions on Vehicular Technology
69. Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model
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70. Health index construction with feature fusion optimization for predictive maintenance of physical systems
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71. Deep learning model-based real-time state-of-health estimation of lithium-ion batteries under dynamic operating conditions
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72. New approach for multidimensional prognostic of stochastic time series based on sparse-based fractional Levy quaternion extended Kalman filter
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73. Nonlinear autoregressive models for high accuracy early prediction of Li-ion battery end-of-life
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74. State of Health Prediction of Lithium-Ion Batteries Based on Multi-Kernel Relevance Vector Machine and Error Compensation
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75. A review of hybrid methods based remaining useful life prediction framework and SWOT analysis for energy storage systems in electric vehicle application
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76. Lithium-ion batteries remaining useful life prediction using a parallel BILSTM-MHA neural network based on a CEEMDAN module
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77. A Comprehensive Review on Data‐Driven Methods of Lithium‐Ion Batteries State‐of‐Health Forecasting
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78. Explainable artificial intelligence driven estimation of remaining useful life for lithium-ion battery
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79. State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process
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80. Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm
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81. Degradation Trajectory Prognostics for Lithium-Ion Batteries Under Varied Operating Conditions With a Hybrid-Driven Method
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82. Capacity prediction of lithium-ion batteries based on ensemble empirical mode decomposition and hybrid machine learning
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83. A Novel Method Based on Hybridization of Generative Adversarial Imputation Nets and SDAE-Kriging for RUL Prediction of Lithium-Ion Battery in Scenarios of Missing and Incomplete Data
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84. Physics-informed transfer learning by embedding physics into activation functions: an application in battery health management
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85. Estimation of SoC, SoH and RUL of Li-Ion Battery: A Review
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86. Anomaly Detection and Extra Tree Regression for Assessment of the Remaining Useful Life of Lithium-Ion Battery
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87. A Systematic Review of Prognostics and Health Management in Mobility Batteries
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88. Remaining useful life prediction of lithium-ion batteries based on KS agglomeration function integrating multi-expert knowledge
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89. A Novel Health Index for Battery RUL Degradation Modeling and Prognostics
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90. A Novel Indirect Health Indicator Extraction Based on Charging Data for Lithium-Ion Batteries Remaining Useful Life Prognostics
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91. Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)
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92. Extracting health indicators from a single cycle of data based on the aging mechanism to accurately predict the knee point and remaining useful life of spent lithium-ion battery
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93. HybridoNet-Adapt: A domain-adapted framework for accurate lithium-ion battery RUL prediction
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94. An Improved Approach Based on Transformer Network for Remaining Useful Life of Lithium-ion Battery
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95. An Evolutionary Deep Learning Framework for Accurate Remaining Capacity Prediction in Lithium-Ion Batteries
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96. A two-layer full data-driven model for state of health estimation of lithium-ion batteries based on MKRVM-ELM hybrid algorithm with ant-lion optimization
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97. Cluster-Based Prediction for Batteries in Data Centers
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98. Artificial intelligence inferred microstructural properties from voltage–capacity curves
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99. Improving Remaining Useful Life Estimation of Lithium-Ion Batteries when Nearing End of Life
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100. On-board incremental capacity/differential voltage curves acquisition for state of health monitoring of lithium-ion batteries
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