Fully Automated Global Powertrain Optimization for Electric Truck Using Active Learning Algorithm and Backwards Simulation Approach

To reduce greenhouse gas emissions, electric vehicles (EVs) are increasingly becoming a significant part of the automotive market. This trend is not limited to passenger cars, but also extending to the truck segment. Electric trucks, due to their substantial self-weight and payload, require a different approach compared to electric passenger cars. While electric passenger cars generally use only one motor or a single-speed transmission, electric trucks necessitate the use of multiple motors or multi-speed transmissions. This addresses a complex issue: how to determine the size of the motor, the number of motors, the number of gears, and the corresponding gear ratios in the drive system of an electric truck. The nonlinearity of the vehicle system further complicates this issue. To tackle this, the paper proposes the application of an active learning algorithm, namely Bayesian optimization, to determine the configuration of the motors and transmission. The goal is to provide an optimal powertrain solution that minimizes cost and maximizes efficiency. The energy consumption and performance of the electric truck are evaluated based on the FEV simulation tool, which uses a backwards simulation approach to calculate the energy consumption of the vehicle for a quick simulation of different powertrain configurations. The baseline motor model is rebuilt in Ansys Motor-CAD to obtain the loss distribution of the motor by FEM simulation, such as copper, iron and magnet losses, which enables to use the FEV simulation tool to calculate the energy consumption of the scaled motor. Furthermore, the Bayesian optimization is particularly suitable for black-box systems. The optimization process will be presented through learning curves. This allows for a clear visualization of the overall optimization performance over iterations, facilitating the identification of the optimal solution.