Challenge: Improving Efficiency & Production Process of Electric Vehicles using Data Science Techniques
It is often a challenging and complex task to measure rotor and stator temperatures in commercial electric vehicles. Even if these specific tasks can be completed successfully, these testing processes cannot be classified as economical for manufacturers. Keeping in mind that the temperature data have significant importance on dynamical responses of vehicles and motors’ performances, there is an emerging need for new proposals and scientific contributions in this domain.
Building a predictive machine learning or deep learning model that can propose an estimator for the stator and rotor temperatures could be used to utilize new control strategies of the motors and maximize their operational performances. If an accurate ML/DL model is built, the needs of the company for implementing additional temperature sensors in vehicles will be reduced. The potential contribution will directly result in lowering car construction and maintenance costs.
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