Cvok I.

Model predictive control of a passenger cabin heating and air-conditioning system of an electric vehicle

Doctoral thesis (in Croatian), Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia, 2022
Consumer acceptance of electric vehicles is increasing strongly, with the trend bound to continue in the future due to beneficial regulations, government incentives, and consumer's awareness and willingness to shift towards sustainable mobility. Although the innovation in automotive industry is accelerating and the declared range of current battery electric vehicles (BEVs) is increasing, their mass market share is still hindered due to long and widely unavailable charging and end-users’ perception of lacking BEVs range. The already restricted driving range of BEVs is significantly reduced in extremely hot and cold ambient conditions due to high energy consumption of the heating, ventilation and air-conditioning (HVAC) system. To overcome the BEV range reduction in extreme weather conditions, new energyefficient HVAC systems have been developed recently for improved cabin heating and cooling efficiency. These are typically vapor-compression cycle-based heat pump systems with integrated cabin, battery, and powertrain thermal management, and they support operation in both heating and cooling mode. The advanced BEV HVAC systems are characterized by an increased number of actuators, which makes the energy management and control system design more challenging. To minimize the power consumption at a favourable level of thermal comfort, it is necessary to develop new control systems that can optimally coordinate multiple and often redundant actuators of the HVAC system, and which utilize optimisation-based control methods, such as control allocation or model predictive control. The thesis first presents modelling of an advanced heat pump-based BEV HVAC system and a cabin thermal dynamics system, which paves the road for model-based optimal control system design. Next, dynamic programming-based offline control trajectory optimization is carried out to gain insight into the optimal control actions for various operating conditions and obtain guidelines for the design of online control systems. Finally, a cascade control strategy based on the optimal control allocation and a nonlinear model predictive control strategy are designed for the considered HVAC system. Both control systems are verified in simulation environments, while the cascade control strategy is also implemented in a B-segment BEV and experimentally examined in hot and cold weather conditions. The main aim of the thesis is to design optimal control systems for a passenger cabin heating and cooling system of an electric vehicle, which coordinate multiple redundant actuators, accounts for the dynamics and constraints of the overall system and utilizes predictive information such as vehicle's driving cycle and ambient conditions, in order to improve energy efficiency and maintain high level of thermal comfort in extremely cold and hot weather conditions.