Development of a digital twin of a powertrain for efficiency estimations with machine learning and physical modeling

Accurate prediction of the efficiency of electromechanical powertrains is crucial for the development of electric vehicles. Until now, this has been done through time-consuming and costly test bench investigations in late development phases. To address that, digital modeling and development processes are implemented in the product development process. Although, existing modeling approaches such as simulations or classical data-driven models often have limitations in terms of prediction accuracy, prediction speed, or model size. To address this problem, this paper develops five different data-driven modeling approaches for digital twins, ranging from simple black-box models to hybrid grey-box approaches. In addition, a sixth modeling approach, a white-box model, was conducted as a reference to the other model approaches. Validation is based on recorded measurement data from a test bench setup of an electromechanical powertrain consisting of a permanent magnet synchronous machine and a multi-stage transmission.The evaluation includes classic error metrics such as mean absolute error and mean relative error, but also model size, training times, and prediction speeds. In addition, a quotient of input and output variables is included, which is used to evaluate the information content of the model structure. Despite a slightly increased error tolerance, the final bright-grey-model approach offers the best compromise between prediction accuracy, real-time capability, and a high output-to-input quotient. The results demonstrate that data-driven digital twins with physically informed structures enable early and real-time prediction of the efficiency of electromechanical powertrains.