Optimizing EV Battery Efficiency with Predictive Analytics

Abstract

This study explores the potential of predictive analytics to optimize the performance and lifespan of electric vehicle (EV) batteries by leveraging historical performance data, usage patterns, and environmental factors. Employing a quantitative research approach with a sample size of 85, data was collected from various sources, including battery management systems, telematics devices, and meteorological databases. The study focuses on key metrics such as charge capacity, usage frequency, and environmental conditions. Data pre-processing techniques like imputation of missing values, outlier detection, and normalization were applied to ensure consistency and reliability. The integrated dataset was divided into 70% training and 30% test data, upon which predictive models, including regression and classification models, were developed using Random Forests and deep learning techniques. The models predicted battery performance and classified battery health into "Good," "Moderate," and "Poor" categories. The regression model achieved a mean absolute error (MAE) of 2.4 Ah and an R-squared value of 0.87, while the classification model attained an accuracy of 91%, precision of 89%, recall of 93%, and an F1-score of 91%. The findings demonstrate that predictive analytics can significantly enhance battery management practices, improving the efficiency and longevity of EV batteries, which has critical implications for the sustainable adoption of electric vehicles.