Real-time Optimal Battery Thermal Management System Controller for Electric and Plug-in Hybrid Electric Vehicles

Abstract

The objective of this thesis is to propose a real-time model predictive control (MPC) scheme for the battery thermal management system (BTMS) of given plug-in hybrid electric and electric vehicles (PHEV/EVs). Although BTMS control in its basic form can be well represented by a reference tracking problem, there exists only little research in the literature taking such an approach. Due to the importance of a prediction component in thermal systems, here the BTMS controller has been designed based on MPC theory to address this gap in the literature. Application of the controller to the baseline vehicles is then examined by several simulations with di erent optimization algorithms. By comparing the results of the predictive controller with those of the standard rulebased (RB) controller over a variety of driving scenarios, it is observed that the predictive controller signi cantly reduces the power consumption and provides a better tracking behaviour. Integrating trip prediction into the control algorithm is particularly important in cases such as aggressive driving cycles and highly variable road-grades, where the standard BTMS scheme does not perform as e ectively due to the load current pro le. Moreover, based on the simulation results, the designed controller is observed to have a turnaround time between 10 s to 1 ms, and is thus applicable to the real-time automotive systems. Prosperity of the proposed BTMS control methodology paves the way for the use of model-based (MB) thermal management techniques, not only in future