For various holistic powertrain optimisation approaches, simultaneous component sizing is still a challenging task as electric machine optimisation typically reduces transmission to only a few parameters and vice versa. By doing so, manual evaluation on the system level is still required before the next optimisation loop. Therefore, a holistic concept design method, developed at ika, aims to reduce the necessity of human input and verification by increasing the degree of automation in powertrain development. However, the complexity of the optimisation problem increases even further when considering multi-motor concepts and incorporating different modularity approaches. As a countermeasure to avoidable iterations in the design process, several ideas on how to accelerate powertrain development while still achieving the desired trade-off between the degree of modularity and vehicle performance will be discussed. After demonstrating the potential of selected optimisation techniques (regarding computing time, accuracy, and the targeted powertrain characteristics), a prototypic e-axle which was conceptualized using the ika method will be presented as well. Using the measurement data, a demonstration of machine learning-based calibration with a focus on transmission losses (for more precise prediction of energy demand and overheating prevention) will round up the presentation.

Learn more about:

• Separate component sizing as a bottleneck in the development process and ideas on how to overcome it
• Different modelling approaches and their benefits and drawbacks in the powertrain design
• Calibration of a digital twin of a dual-motor powertrain for heavy-duty commercial vehicles