Efficient Process for Multidisciplinary Optimization of Electric Drive Units: A Case Study on Enhancing Design Efficiency

The design of drive units in electric vehicles (EVs) presents challenges due to the need to pass multiple linear and non-linear load cases. This can result in inefficient design. Therefore, optimization plays a critical role in improving the design efficiency. However, setting up the optimization process itself can be challenging, especially when dealing with complex design variables and different load cases that require the use of various computer-aided engineering (CAE) solvers. The drive unit, being a casting component, presents additional challenges in setting up Multidisciplinary Design Optimization (MDO) process. This paper introduces an efficient process for addressing these challenges by presenting a sample Multidisciplinary Design Optimization (MDO) problem. The problem involves the manipulation of discrete design variables, such as the number of ribs, and incorporates five different load cases that require the utilization of different CAE solvers. The proposed process demonstrates a streamlined approach that can be completed in less than one week. It showcases the steps involved in setting up the optimization process, including defining the design variables, selecting appropriate load cases, and integrating different CAE solvers. By following this process, engineers can effectively optimize the design of drive units in EVs, leading to improved mass efficiency. Overall, this paper highlights the importance of optimization in addressing the challenges associated with the design of drive units in EVs. It presents a practical and efficient process that can be implemented within a short timeframe, allowing engineers to achieve optimal design solutions. By leveraging this approach, manufacturers can enhance the performance and efficiency of EV drive units, contributing to the advancement of electric mobility.