Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost
To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve...
Main Authors: | Mei Zhang, Wanli Chen, Jun Yin, Tao Feng |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/15/16/5981 |
Similar Items
-
Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction
by: Shuxiang Song, et al.
Published: (2020-02-01) -
Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm
by: Zheng Zhou, et al.
Published: (2023-01-01) -
A Vector Line Simplification Algorithm Based on the Douglas–Peucker Algorithm, Monotonic Chains and Dichotomy
by: Bo Liu, et al.
Published: (2020-04-01) -
Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost
by: Mei Zhang, et al.
Published: (2022-07-01) -
A Chain-Based Wireless Sensor Network Model Using the Douglas-Peucker Algorithm in the Iot Environment
by: Se-Jung Lim
Published: (2021-01-01)