Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning.

ABSTRACT: Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO₂ -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO₂ -based) using open-access datasets. T

SUBMITTER: Su L 

PROVIDER: S-EPMC10502833 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature