Innovative fault diagnosis and early warning method based on multifeature fusion model for electric vehicles

Author:

Yifan Zheng,Zhengjie Zhang,Sida Zhou,Xinan Zhou,Qiangwei Li,Ning Cao,Shichun Yang

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference37 articles.

1. Lithium-ion cell fault detection by single-point impedance diagnostic and degradation mechanism validation for series-wired batteries cycled at 0 degrees C;Love;Energies,2018

2. Identification of typical sub-health state of traction battery based on a data-driven approach;Wang;Batteries-Basel,2022

3. Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O-2 cathode for electric vehicles: thermal runaway features and safety management method;Zhu;Energy,2019

4. State-partial accurate voltage fault prognosis for Lithium-ion batteries based on self-attention networks;Zhang;Energies,2022

5. Battery life estimation based on cloud data for electric vehicles;Li;J. Power Sources,2020

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