Predictive analytics of battery use conditions and degradation: A data-driven approach

Abstract

Lithium-ion batteries are the critical components of the latest energy storage devices, ranging from consumer electronics to electrically powered automobiles. Despite their significant applications in the storage of electrical energy devices, the functionality of these batteries largely relies on the nature of the charge/discharge cycles. Batteries subjected to complete charge/discharge cycles deteriorate faster than batteries that undergo partial cycles, which limits their possibility for reuse and recycling. Understanding and predicting battery consumption conditions can substantially help with lifecycle management and sustainable recycling initiatives. This study investigates a data-driven approach for predicting the use condition of lithium-ion batteries based on degradation criteria. Logistic regression, Naïve Bayes, and decision tree models are used to predict the previously used condition of a battery based on several variables such as days of degradation, energy throughput, C/10 capacity, and state of health (SoH). The dataset is collected from the existing literature and preprocessed to extract the required variables. After preprocessing and variable selection, the models are made and tested with cross-validation and ROC analysis. The analysis of the results suggests that the decision tree classifier performs better compared to other classification models in terms of accuracy, F1 score, and AUC values. The findings prove the applicability of the predictive analysis technique in assisting battery lifecycle management by categorizing the batteries that have been used in different scenarios for recharging.