A review on Machine Learning Enhanced Predictive Maintenance for Electric Vehicle Power Electronics: A Pathway to Improved Reliability and Longevity

Abstract

The emergence of electric vehicles (EVs) as a mainstream mode of transportation presents new challenges in the realm of power electronics, particularly concerning reliability and longevity. Power electronics are the cornerstone of EV performance, dictating efficiency, durability, and overall vehicle health. Traditional maintenance strategies fall short in addressing the dynamic operational demands and complex failure mechanisms inherent in EV power systems. This paper introduces a machine learning (ML)-enhanced predictive maintenance framework designed to revolutionize the upkeep of EV power electronics. By harnessing advanced ML algorithms, the framework predicts potential system failures and degradation patterns, enabling preemptive maintenance actions. A robust data-driven approach is employed, utilizing operational data and failure modes to train the predictive models. The efficacy of the proposed method is demonstrated through extensive simulation and real-world EV power system analyses, showcasing significant improvements in fault identification accuracy and maintenance scheduling optimization. The result is a substantial extension of component lifespan and a reduction in unplanned downtimes, propelling EV power electronics towards higher reliability standards. This work not only contributes a novel predictive maintenance methodology but also paves the way for adaptive maintenance regimes, tailored to the unique demands of EV power electronics systems in the pursuit of sustainable and resilient transportation solutions.