Lucia Giulia Bosio
Model Predictive Control Strategies Applied to Battery Power Limit Estimation in Electric Vehicles.
Rel. Massimo Violante, Nicholas Canella. Politecnico di Torino, Master of science program in Mechatronic Engineering, 2024
Abstract
As the automotive industry transitions towards cleaner and more sustainable technologies, the shift from internal combustion engine (ICE) vehicles to electric vehicles (EVs) has gained momentum. This shift, driven by increasing environmental concerns and stringent regulations, highlights the need for advanced energy management systems, particularly in battery technology. A crucial component in ensuring optimal battery performance and longevity is the Battery Management System (BMS), which plays a pivotal role in monitoring and controlling critical parameters like temperature, State of Charge (SoC), and State of Power (SoP). Among these, SoP estimation is vital for high-performance applications such as electric vehicles, as it dictates how much power the battery can safely provide under current operating conditions.
Unlike instantaneous estimates, predictive estimates of SoP offer a significant advantage by allowing for load scheduling over a future time horizon
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