Improving the energy efficiency of electric vehicles has various significant benefits, such as increasing the driving range. The Thermal Management System (TMS) plays a large role in optimizing the vehicle energy consumption and battery lifetime. In this thesis, a nonlinear Model Predictive control (MPC) strategy is presented to regulate the battery, motor and inverter temperatures to minimize vehicle energy consumption and maximize battery lifetime, two conflicting objectives traded off by a single tunable parameter, whilst staying
within temperature limits to ensure safety. The control dynamics are nonlinear and discontinuous, due to valves in the system that can only be fully opened or fully closed, leading to a nonlinear mixed-integer optimization problem. A nonlinear model of the TMS including actuators and electric components is formulated that is validated by using simulation results over three drive cycles for moderate and hot ambient conditions. Mean temperature deviations between -0.22 and 0.25 °C are achieved for the battery, and between 0.14 and 1.48 °C for the motors, depending on the drive cycle. Using outer convexification, the optimization problem is reformulated as a continuous problem, which can be solved efficiently. The control strategy is tested in a simulation environment for both moderate and hot ambient temperatures and three different drive cycles. The strategy is compared to a benchmark strategy that uses a finite-state machine and PID-based control loops. A decrease in power consumption between 7% and 11% is achieved for 5 out of 6 use cases whilst additionally decreasing the ageing rate by 0% to 7%. For one use case, an increase in
energy consumption is achieved by 2.5%, but the relative ageing rate is decreased by 44%. At hot temperatures, improvements are mostly achieved due to finding an energy-efficient battery cooling trajectory. At moderate temperatures, improvements are mostly achieved by increased motor cooling to take advantage of the temperature-dependent motor losses. The results obtained using a continuous solver are also compared to those obtained using a
mixed-integer solver. Minimal loss in performance is seen compared to the mixed-integer solver, whilst requiring a significantly lower computation time that is within the sample time, making the MPC strategy fast enough for real-time control in the simulation environment. Finally, the effect of using noisy forecast information is used to test the robustness of the controller. Using noisy forecast information instead of perfect forecast information, the energy consumption and ageing rate increase by 0.0% to 1.2%.