Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

Abstract

A nonlinear model predictive control (NMPC) for the thermal management (TM) of plug-in hybrid electric vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure high components’ performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low-voltage actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components’ thermal stress and, at the same time, electrical consumption. In this context, NMPC proves to be a powerful method for achieving multiple objectives in multiple input multiple output systems. This paper proposes an NMPC for the TM of the high-voltage battery and the power electronics cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant, which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multidomain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures of 2 $^\circ$ C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared with the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and reduces the cooling electrical consumption by more than 5%. In terms of the objective function, which is an accumulated and weighted sum of the two goals, this improvement amounts to 30%. Finally, the online SIL presented in this paper suggests that the used optimizer is fast enough for a future implementation in the vehicle.