Early Prediction Method for Remaining Useful Life of Retired Batteries in Second-life Applications

Author:

Tan Yuyi1,Liu Tianpei1,Ye Xingbin1,Chen Yanhua1,Yang Qingcheng1,Peng Weiwen1

Affiliation:

1. School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University,Shenzhen,China,518107

Funder

National Key R&D Program of China

Postdoctoral Research Foundation of China

Fundamental Research Funds for the Central Universities

Sun Yat-sen University

Publisher

IEEE

Reference11 articles.

1. A novel approach investigating the RUL predication of retired power LIBs using GP method[J];dongfeng;J Electrochem En Conv Stor,2021

2. An Accurate and Interpretable Lifetime Prediction Method for Batteries using Extreme Gradient Boosting Tree and TreeExplainer

3. Remaining Useful Life Estimation for LFP Cells in Second-Life Applications;iván;IEEE Transactions on Instrumentation and Measurement,2021

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

5. Predicting the state of charge and health of batteries using data-driven machine learning

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