Capacity estimation of lithium-ion battery based on soft dynamic time warping, stratified random sampling and pruned residual neural networks-Reference-Cited by-同舟云学术

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Capacity estimation of lithium-ion battery based on soft dynamic time warping, stratified random sampling and pruned residual neural networks

Published:2024-12 Issue: Volume:138 Page:109278
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Xu Jiangtao^ORCID,Qu Jie^ORCID

Publisher

Elsevier BV

Reference78 articles.

1. Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule;Anseán;J. Power Sources,2017

2. Mechanistic investigation of silicon-graphite/LiNi0.8Mn0.1Co0.1O2 commercial cells for non-intrusive diagnosis and prognosis;Anseán;J. Power Sources,2020

3. ‘‘Knees’’ in lithium-ion battery aging trajectories;Attia;J. Electrochem. Soc.,2022

4. Synthetic vs. Real driving cycles: a comparison of electric vehicle battery degradation;Baure;Batteries,2019

5. Learning-compression algorithms for neural net pruning;Carreira-Perpinán,2018

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