A Model Predictive Control Strategy for Advanced Cabin Air Conditioning and Air Quality

Electromobility is on a growth path in Europe. This is supported by the European Commission’s “green deal” and the funding projects it contains. However, it is not enough to promote battery-electric vehicles only politically. It is also necessary to increase customer acceptance of this technology. In extreme weather conditions air conditioning can have an unfavorable impact on the driving range. An intelligent thermal strategy can help to reduce energy consumption and maintain comfort in the cabin.

With this in mind, this paper presents a predictive control strategy for air conditioning of battery electric vehicles that optimizes the use of the energy sources available in the vehicle. For this purpose, maximum utilization of the air recirculation rate is desirable. In this context, however, it must be prevented that the air quality suffers from an excessive increase of the CO₂ concentration in the cabin. This criterion is also taken into account by the control algorithm. Another feature of the developed approach is the possible use of heating radiating surfaces to increase comfort and reduce energy consumption. The functionalities described were implemented in a Model Predictive Control (MPC) approach and tested in simulation.

The modeling and the functional architecture of the MPC cabin air conditioning system will be explained in this paper. Using selected scenarios, it will be shown that the novel strategy can significantly save energy while ensuring the occupant comfort.