Battery electric vehicles (BEVs) are currently enjoying rising sales figures. However, BEVs still have problems with customer acceptance, partly due to limited driving ranges. To improve the situation, this paper introduces a novel approach utilising temperature-dependent efficiencies using an economic model predictive control approach (MPC) in combination with an active grille shutter in order to accelerate the heating of the permanent magnet synchronous machine. The measurements of temperature-dependent component efficiencies on a powertrain test bench are presented and analysed in detail in the speed/torque range. Thermal models based on the lumped parameter thermal network approach were developed and validated as part of the system-level validation against a US06 wind tunnel measurement. After the build-up and implementation of the MPC, various simulations were conducted. For the investigations, three driving cycles were considered at component start temperatures of 20–80 °C. The results show that using the MPC with the grille shutter can save 0.69–2.02% energy at the HV level compared to the rule-based control with a shutter, of which up to 1.02% is due to temperature-dependent efficiencies. Comparing the MPC with the grille shutter to a vehicle without a shutter, savings of 2.8–4.2% were achieved, while up to 1.67% was achieved due to temperature effects in the powertrain.

The complexity of automobile powertrains continues to increase, leading to increased development time and effort. One method to address this challenge is to shift vehicle calibration and testing tasks to simulations with Engine-in-the-Loop (EiL) test setups. This increases the efficiency of the development process through high flexibility, low prototype costs, an early starting point, and a high degree of automation. EiL emulates the dynamic interactions between the internal combustion engine and other powertrain components by closed-loop coupling to real-time simulation models. The coupling quality strongly depends on the interface properties, the hardware layout, and the control functionality of the dynamometer test bench. This paper analyzes an existing test bench and presents the impact of altered test bench parameters on different EiL quality aspects. Thereto, the setup is analyzed and a Simulink based grey box test bench model is derived. The latter is parametrized with a full factorial approach in comparison to real test bench measurements. The test bench model is then used for simulative investigations considering different test bench parameters: Two different use cases of Model-in-the-Loop (MiL) supported EiL are investigated. First, a simulative improvement of the test bench control quality by altering the PI based dyno speed controller parameters is investigated. The virtually calibrated controller parameters are applied to the real EiL system and compared to vehicle measurements. Second, sensitivity studies are shown with respect to dynamometer inertia and system dead time in terms of control quality and energy. As a major outcome, a compromise of control accuracy and stability for different speed controller parameters, dyno inertia, and system dead time is shown. Furthermore, it is demonstrated that EiL has only a small deviation in terms of energy compared to a full vehicle, making it a valid approach when considering CO₂ emissions.