Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm

Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm

The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting li...

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Bibliographic Details
Main Authors: Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Batteries
Subjects:
lithium-ion battery
state of health
deep hybrid kernel extreme learning machine
improved black-winged kite algorithm
Online Access:https://www.mdpi.com/2313-0105/10/11/398
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