Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network
The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to...
Main Authors: | Lei Yao, Shiming Xu, Yanqiu Xiao, Junjian Hou, Xiaoyun Gong, Zhijun Fu, Aihua Tang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9698074/ |
Similar Items
-
A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network
by: Jia Wang, et al.
Published: (2021-08-01) -
A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods
by: Lei Yao, et al.
Published: (2021-08-01) -
A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
by: Bosong Zou, et al.
Published: (2023-07-01) -
A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
by: Manh-Kien Tran, et al.
Published: (2020-03-01) -
Battery management systems for large lithium-ion battery packs /
by: Andrea, Davide.
Published: (2010)