Demonstration of a Nonlinear Model Predictive Control of a Thermal Management System for Electric Vehicles in Real-Time
The range of electric vehicles can dramatically decrease in winter due to high thermal demands of the passenger cabin. Energy-efficient thermal management systems help to decrease energy consumption and range drop in winter by integrating waste heat from powertrain components. The associated rising system complexity can be handled by optimization-based control approaches. In (Fischer et al., 12th Int. Modelica Conf, 2017), we have presented the development of a virtual Nonlinear Model Predictive Control (NMPC) approach of a thermal management system and showed the advantages over a conventional PID-controlled system. In this article, we build upon this work and show such an NMPC setting in real-time with a real-world thermal management system integrated in a vehicle demonstrator. The resulting feedback time between acquisition of measurement data and new NMPC controller outputs is in the order of milliseconds and proves the real-time feasibility of the approach.