A scalable life cycle inventory of an electrical automotive traction machine—part I: design and composition

Journal article, 2018

Purpose: A scalable life cycle inventory (LCI) model of a permanent magnet electrical machine, containing both design and production data, has been established. The purpose is to contribute with new and easy-to-use data for LCA of electric vehicles by providing a scalable mass estimation and manufacturing inventory for a typical electrical automotive traction machine. The aim of this article (part I of two publications) is to present the machine design, the model structure, and an evaluation of the models’ mass estimations. Methods: Data for design and production of electrical machines has been compiled from books, scientific papers, benchmarking literature, expert interviews, various specifications, factory records, and a factory site visit. For the design part, one small and one large reference machine were constructed in a software tool, which linked the machines’ maximum ability to deliver torque to the mass of its electromagnetically active parts. Additional data for remaining parts was then gathered separately to make the design complete. The two datasets were combined into one model, which calculates the mass of all motor subparts from an input of maximum power and torque. The range of the model is 20–200 kW and 48–477 Nm. The validity of the model was evaluated through comparison with seven permanent magnet electrical traction machines from established brands. Results and discussion: The LCI model was successfully implemented to calculate the mass content of 20 different materials in the motor. The models’ mass estimations deviate up to 21% from the examples of real motors, which still falls within expectations for a good result, considering a noticeable variability in design, even for the same machine type and similar requirements. The model results form a rough and reasonable median in comparison to the pattern created by all data points. Also, the reference motors were assessed for performance, showing that the electromagnetic efficiency reaches 96–97%. Conclusions: The LCI model relies on thorough design data collection and fundamental electromagnetic theory. The selected design has a high efficiency, and the motor is suitable for electric propulsion of vehicles. Furthermore, the LCI model generates representative mass estimations when compared with recently published data for electrical traction machines. Hence, for permanent magnet-type machines, the LCI model may be used as a generic component estimation for LCA of electric vehicles, when specific data is lacking.