Design of an Adaptive Machine Learning Framework for Predictive Maintenance in Electrified Vehicle Subsystems

Abstract

Electrified transportation is becoming a crucial component of the global energy transition. Therefore, ensuring the reliability and resilience of its subsystems is essential for its sustainable operations. The most important subsystem is the battery system whose failures can affect operational efficiency. While the machine learning predictive maintenance method has shown promise for early fault detection, most existing approaches rely on offline and statically trained models that are unable to adapt to sensor degradation and concept drift. In this paper, we present the evaluation of an adaptive machine learning method for predictive maintenance in EV battery systems. We proposed a framework using the NASA Prognostics Center of Excellence (PCoE) lithium-ion battery dataset that uses an online adaptive learning and concept drift detection method to maintain model accuracy. A random forest regression model has been developed to predict battery capacity degradation based on extracted statistical and degradation-related features. The drift detection mechanism that is based on rolling mean absolute error (MAE) was used to identify performance degradation and trigger model retraining using a sliding window approach. The model result showed high predictive performance by achieving a high level of agreement between predicted and actual battery capacity degradation. The statistical result shows a MAE of 0.061, a root mean square error (RMSE) of 0.11, and a coefficient of determination (R2) of 0.911. This shows its ability to capture nonlinear battery degradation trends accurately. The integration of a decision layer also enables maintenance to be prioritized by balancing system reliability and energy efficiency. This adaptive method addressed concept drift by capturing patterns and maintaining predictive accuracy through continuous model updates in EV battery systems.