A data-driven method for extracting aging features to accurately predict the battery health

Xiong, Rui, Sun, Yue, Wang, Chenxu, Tian, Jinpeng, Chen, Xiang, Li, Hailong, Zhang, Qiang·2023·Energy storage materials

Cited by (190)

Items 101–190 of 190

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  5. State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval
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  7. Estimating battery state of health using impedance spectrum geometric health indicators and recurrent deep sigma point process
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  9. Battery state of health estimation for partial charge segments using incremental capacity analysis
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  10. Advantageous characteristics of constant voltage charging: A good option to estimate battery states for lithium-ion batteries
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  11. A new bin size index method for statistical analysis of multimodal datasets from materials characterization
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  12. Early prediction of lithium-ion battery health in electric vehicles using small-sample learning and hybrid models
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  14. Hybrid experts-decoupled and physics-informed neural network for lithium-ion battery degradation modeling and prognosis
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  15. On physics-informed fuzzy neural network for the state of health prediction of lithium-ion batteries
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  16. An improved feature selection framework based on the distribution of relaxation times from mid-to-high frequency impedance for lithium-ion battery diagnostics
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  17. State of Health Estimation for Lithium-Ion Batteries Using an Ensemble Learning Framework Based on Optimized Interval of Charging Curves
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  18. DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation
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  22. Improved war optimization algorithm assists-consolidated memory short-term memory modeling method under multi-task for the state of health estimation in lithium-ion batteries
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  42. Prediction of remaining useful life of lithium-ion battery using convolutional neural network-bidirectional long short-term memory with attention mechanism
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    Fu‐Kwun Wang, Getnet Awoke Kebede · 2026 · ISA Transactions
  44. AI-Driven Predictive Maintenance and Battery Management in Electric Vehicles
    D. Sheema, Shaik Tawfiq Farhan, Shaik Hussan Sahith · 2026 · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)
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    Liangliang Wei, Zang Chen, Hongzhang Xu, Tianpei Liu, Yurui Xiang, Xiaojun Tan, Mengtang Li, Yuqian Fan · 2026 · IEEE Transactions on Industrial Electronics
  46. Changepoint detection as a light data-driven approach to battery state-of-health prediction
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  47. State of health estimation of lithium-ion battery packs based on global-divergent features and multi-layer stacking ensemble model
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  48. State monitoring and intelligent management of lithium-ion batteries based on advanced sensing technology: From principles to application
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  51. Remaining useful life prediction of lithium-ion battery packs using prior cell degradation and limited pack-level data
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  53. Machine learning-assisted health management of retired batteries
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  54. A novel feature combination screening method for Li-ion battery state of health prediction
    Yuntian Pei, Liqun Chen, Zhang Chen, Jinhui Zhou, Wenjing Shen · 2023 · International Conference on Artificial Intelligence and Computer Science (AICS)
  55. PNP-Lightweight Model for Predicting Remaining Useful Life of Lithium-Ion Battery to Applying Embedded Systems
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  56. State of health estimation and remaining useful lifetime prediction of battery based on the real dynamic forklift profile
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  60. State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
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