Conditional Predictive Maintenance of Electric Vehicles from Electrical and Mechanical Faults

Abstract

Maintenance of the core components of an electric vehicle is very crucial to ensure productivity, longevity, drive quality, and a safe environment. Predictive Maintenance is an approach that uses the operating & faulty condition data to predict future machine conditions and make decisions upon this prediction. The methodology used for predictive maintenance and condition monitoring can be based on machine learning and data analytics. The process of learning starts with the observation of data and using it in future instances for building the model. The primary aim is to allow the computer to learn without the involvement of the intervention of human assistance. A few machine learning methods are supervised learning, semi-supervised learning, and reinforcement learning. The main aim of the presented research is to use the available sensor data of the electric vehicle from various electronic control units and design a predictive model which classifies the various electrical and mechanical faults that occur in an electric vehicle and predicts the types for increasing the reliability of the whole electrical vehicular system. The workflow of the project is defined as fault modelling, generating healthy and fault data, processing the data using time synchronous averaging, identification of the system condition indicators and finally using these condition indicators, an SVM classification prediction model is designed from which the desired results and conclusion are inferred from the simulation studies.