Research on Data-Driven-Based Remaining Useful Life of Lithium-ion Battery
Author:
Affiliation:
1. School of Eelectronics and Information Engineering, Harbin Institute of Technology,Harbin,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10058187/10058056/10058386.pdf?arnumber=10058386
Reference15 articles.
1. Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network
2. State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study
3. Remaining useful life prediction of lithium battery based on capacity regeneration point detection;ma;Energy,2021
4. Remaining Useful Life Assessment for Lithium-Ion Batteries Using CNN-LSTM-DNN Hybrid Method
5. Lithium-ion batteries remaining useful life prediction based on BLS-RVM
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1. A Novel Long Short-Term Memory Approach for Online State-of-Health Identification in Lithium-Ion Battery Cells;Batteries;2024-02-26
2. Lithium Battery State of Health Estimation Based on Indirect Health Factors and Optimized Radial Basis Function Neural Network;2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR);2023-09-22
3. SOH Estimation of Lithium Batteries Based on New Health Factors and Optimized BP Neural Networks;2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD);2023-07-29
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