DRIVING-AWARE CABIN AND BATTERY THERMAL CONTROL SYSTEM DESIGN IN ELECTRIC VEHICLES: INTEGRATING DAILY DRIVING PATTERNS

Abstract

Battery Electric Vehicles (BEVs) entirely depend upon the battery to power the Heating, Ventilation, and Air Conditioning (HVAC) unit due to their negligible waste heat generation. Consequently, the optimization of HVAC power usage in a BEV has been a growing research area over the past several years. Researchers have studied different climate control technologies for implementation in BEVs. These approaches include different online and offline energy management strategies aiming to enhance battery lifespan, health, driving range, and passenger comfort and integrate driving behaviour into control design strategies. Model predictive control has emerged as a reliable approach for real-time cabin climate control, leveraging sensor capabilities to automatically customize control systems for individual drivers, enhancing both performance and comfort. Furthermore, advancements in computational capabilities have enabled the implementation of more sophisticated model-based and data-driven control systems. For instance, temperature-dependent and aging-dependent battery models have been developed, enhancing the accuracy and reliability of battery health and state of charge estimations. This thesis contributes to optimized cabin climate control through the implementation of a novel Linear Quadratic Regulator (LQR) with Kalman filter temperature estimation aimed at minimizing HVAC power usage while maintaining a comfortable cabin climate range. This approach is further enriched by integrating the daily driving patterns of commuter drivers. Furthermore, to enhance the performance of Battery Thermal Management Systems (BTMS), a novel battery power loss model is designed which incorporates temperature and aging effects on internal resistance. This model enables precise estimation of battery power loss. Additionally, a BTMS design capable of both heating and cooling is constructed to maintain optimal battery temperature, thereby improving battery efficiency, longevity, and overall performance. Consequently, this research significantly advances EV cabin and battery thermal management by addressing critical challenges such as reducing battery power loss estimation errors, optimizing temperature regulation, improving power efficiency, enhancing battery aging characteristics, and ensuring adaptability to diverse driving patterns while maintaining cabin comfort levels.