In the realm of electric and hybrid vehicles (EVs, HEVs), the intelligent thermal system control unit is essential for optimizing performance, safety, and efficiency. Unlike traditional internal combustion engines, EVs rely heavily on battery performance, which is significantly influenced by temperature. An intelligent thermal management system helps battery packs to operate within their optimal temperature range, enhancing energy efficiency, extending battery life, and maximizing driving range. Furthermore, it plays a crucial role in managing the thermal dynamics of power electronics and electric motors, preventing overheating, and ensuring reliable operation. As the demand for high-performance and efficient electric vehicles grows, the integration of advanced thermal control strategies becomes increasingly vital, paving the way for innovations in EV design and functionality.
One of the key aspects of an intelligent thermal system control is their prediction capability. These predictive functions leverage real-time geospatial data and advanced algorithms to anticipate vehicle behavior and optimize vehicle/thermal system control operation. By integrating location intelligence with predictive analytics, vehicles can proactively adjust to dynamic driving conditions, improve performance and efficiency, and enhance safety and comfort features.
This paper explores the methodologies and applications of location-based prediction functions, highlighting their potential to improve thermal system of a modern Electric Vehicles, Plug-in or Strong hybrids and conventional ICE engines. The paper also presents a novel algorithm that enables the thermal system control of the vehicle to possess predictive capabilities independently, without relying on sophisticated navigation services, cloud connectivity or a high-performance computer.