Predictive Thermal Management for an Electric Vehicle Powertrain

Abstract

The design of thermal management strategies for electric vehicle powertrains is an important task, since these strategies influence performance, energy consumption, safety and durability of the powertrain operation. In the present paper, a predictive thermal management strategy for the powertrain cooling system is developed. The aims are to minimize the energy consumption of the cooling system, to increase the efficiency of the electric motors and to operate them in a temperature area of safety and durability. The thermal management strategy is realized by a real-time capable nonlinear model predictive controller, incorporating a dynamical model for the powertrain of the battery electric vehicle under consideration. The usage of this model-based approach ensures a fast adaption of the control strategy to different vehicle architectures. Since predictions of the disturbances acting on the system are uncertain in general, the control strategy is extended by a stochastic model predictive control approach to handle uncertainties in the disturbance prediction. The effectiveness of the resulting strategies is demonstrated using an accurate simulation model. Special attention is given to the possible energy savings, the robustness with respect to uncertainties, as well as the computational effort of the algorithms.