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.

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems.

Mobile charging robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using Long Short-Term Memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems.