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Thermal Management Optimization in Battery Electric Vehicles Using Hierarchical Nonlinear MPC and Machine Learning Techniques.
Rel. Diego Regruto Tomalino, Francesco Ripa. Politecnico di Torino, Master of science program in Computer Engineering, 2024
Abstract
The landscape of global transportation is undergoing a significant transformation with the widespread adoption of electric vehicles (EVs). The reasons behind this major shift in vehicle technology have become widely recognised and understood at this point in history, as are the challenges. One significant problem to address on this matter is maximizing travel distance by optimizing the use of the batteries' limited capacity, while ensuring passengers' comfort. The aim of this thesis is to improve the performance of the control strategy proposed by previous works over the Thermal Management System of a Battery Electric Vehicle by applying Machine Learning techniques. This is approached with the objective of maximising battery lifespan and minimising energy consumption.
The control strategy is based on Two-level Hierarchical Nonlinear Model Predictive Control, which enables the adjustment of control actions by anticipating and responding to predicted future conditions
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