Lithium Battery State of Health Prognostication Employing Multi-Model Fusion Approach Based on Image Coding of Charging Voltage and Temperature Data-Reference-Cited by-同舟云学术

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Lithium Battery State of Health Prognostication Employing Multi-Model Fusion Approach Based on Image Coding of Charging Voltage and Temperature Data

Published:2024 Issue: Volume: Page:
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Author:

Zhang Wencan,He Hancheng,Li Taotao,Yuan Jiangfeng,Xie Yi,Long Zhuoru

Publisher

Elsevier BV

Reference48 articles.

1. Data-driven state of health estimation in retired battery based on low and medium-frequency electrochemical impedance spectroscopy;W Zhang;Measurement,2023

2. Impedance-Based Battery Management System for Safety Monitoring of Lithium-Ion Batteries;B Carkhuff;IEEE Transactions on Industrial Electronics,2018

3. Deep neural network battery life and voltage prediction by using data of one cycle only;C.-W Hsu;Applied Energy,2022

4. A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries;M Lin;Journal of Power Sources,2022

5. Unlocking extra value from grid batteries using advanced models;J M Reniers;Journal of Power Sources,2021

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