SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost

Author:

Sun JingORCID,Fan Chaoqun,Yan Huiyi

Publisher

Elsevier BV

Reference37 articles.

1. Deep learning-based battery state of charge estimation: enhancing estimation performance with unlabelled training samples;Ma;J Energy Chem,2023

2. State of health estimation for second-life lithium-ion batteries in energy storage system with partial charging-discharging Workloads;Jiang;IEEE Trans Ind Electron,2024

3. Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation;Cheng;Renew Sustain Energy Rev,2023

4. A battery Digital Twin from laboratory data using Wavelet analysis and neural networks;Di Fonso;IEEE Trans Ind Inf,2024

5. SOH prediction for Lithium-Ion batteries by using historical state and future load information with an AM-seq2seq model;Qian;Appl Energy,2023

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