Battery capacity prediction for electric vehicles using an ensemble model in a fog computing framework

Electric Vehicles (EVs) are crucial in addressing environmental issues associated with traditional vehicles. However, accurately predicting battery capacity remains challenging for optimizing EV performance. This paper presents a Capacity Level Prediction Strategy (CLPS) consisting of three layers: Internet of Things (IoT), fog, and cloud. IoT data is initially sent to the fog layer for quick analysis and decision-making, then transferred to the cloud for long-term storage and future analysis. A Capacity Level Prediction Model (CLPM) is implemented in the fog layer, featuring two phases: preprocessing and capacity prediction. The preprocessing phase addresses missing data, outlier rejection, and feature selection. The prediction phase utilizes an Ensemble Prediction Model (EPM), combining the results of Random Forest (RF) and Deep Neural Network (DNN) models, with Logistic Regression (LR) for aggregation. The proposed CLPM outperforms existing approaches, achieving high accuracy (Mean Squared Error (MSE): 0.0003, coefficient of determination (R²): 0.9925, Mean Absolute Percentage Error (MAPE): 0.0066, and Mean Absolute Error (MAE): 0.01077639), and significantly improving battery monitoring, charging efficiency, and the overall lifespan of EVs.