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Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
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. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
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. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
Su, Laisuo (author) / Zhang, Shuyan (author) / McGaughey, Alan J. H. (author) / Reeja‐Jayan, B. (author) / Manthiram, Arumugam (author)
Advanced Science ; 10
2023-09-01
10 pages
Article (Journal)
Electronic Resource
English
Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
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