An Optimized Random Forest Regression Model for Li-Ion Battery Prognostics and Health Management

An Optimized Random Forest Regression Model for Li-Ion Battery Prognostics and Health Management

This study proposes an optimized random forest regression model to achieve online battery prognostics and health management. To estimate the battery state of health (SOH), two aging features (AFs) are extracted based on the incremental capacity curve (ICC) to quantify capacity degradation, further a...

Full description

Bibliographic Details
Main Authors: Geng Wang, Zhiqiang Lyu, Xiaoyu Li
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Batteries
Subjects:
lithium-ion battery
prognostics and health management
state of health
remaining useful life
random forest regression model
Bayesian optimization
Online Access:https://www.mdpi.com/2313-0105/9/6/332

Internet

https://www.mdpi.com/2313-0105/9/6/332

Similar Items