Opt4E – Multi-criteria Synthesis and Optimization of Powertrains for Electric Vehicles

Project Description

Despite the comparatively long development time for conception, design and construction, powertrains of current EVs are still far from meeting all development goals. Although powerful development methods and tools are available for the individual components of the drivetrain (such as power electronics, electric motors, transmissions), there are currently no publicly available methods or tools for tuning and optimizing the entire drive system. In particular, the influences and mutual influences of manufacturing tolerances, NVH behavior, thermal management, and drivability in interaction with efficiency/tribology and operating strategy are very complex. The mutual influences of the numerous optimization criteria are often difficult to model and are sometimes contradictory. Depending on the weighting function and application profile, this can result in many different drives for achieving a target.

In this context, the Opt4E research project aims to develop and validate methods and tools for the comprehensive synthesis and optimization of EV powertrains. Key performance indicators (KPIs) are derived from typical target criteria and evaluations of an electric powertrain (such as costs, dynamics, efficiency, consumption, and comfort). The user of the optimization program should define the extent to which the individual KPIs are included in an overall assessment of an individual and thus in the optimization loop based on their specified usage profile and other individual weightings. With the help of this development platform, promising powertrain topologies can be created, evaluated, and compared at an early stage. Possible conflicts of objectives between global and detailed design are identified and effectively resolved by an integrated process. The methods for EV drive systems available via the development platform accelerate development cycles, save development costs, and allow potential to be viewed holistically and exploited in the best possible way. The early inclusion of dependencies between the subsystems in later operating behavior, particularly in the areas of efficiency, thermal management, and tolerance influences, also leads to optimal utilization of resources in the production process, which results in indirect CO2 savings. An increase in the efficiency of the overall system, on the other hand, leads directly to considerable CO2 savings in the operation of the systems and thus contributes to the establishment of sustainable drive architectures. The overarching goal of the method carrier programmed as a development platform and the subordinate sub-goals as well as the interrelationships and dependencies are shown in Figure 1 below.