In the initial stage of EV architecture development, goals for quantitative performance development are required to develop a driving performance concept that reflects customer needs. Currently, there is a lack of quantitative and detailed performance standards to present architectural direction related to concept-stage driving performance, and there are many major competitive vehicle performance and benchmarking data, but objective utilization is low. In addition, since there is no test vehicle in the concept stage, it is very difficult to establish a performance goal for the development vehicle. In order to satisfy these architectural development requirements, a series of processes ranging from architecture creation, performance review, and optimal architecture selection are implemented based on 1D simulation models. First of all, various architectures are reviewed for power/fuel performance, and activities are carried out to select optimal architectures with reviewing a combination of 1284 specifications. Next, based on the selected optimal architecture, a virtual evaluation environment for driving performance from a comprehensive performance perspective was established. A driving performance scenario based on a 1D concept model was simulated to review harmonic performance (Drivability/handling/ride). The possibility of virtual driving performance inspection was confirmed by combining a performance analysis/evaluation tool that performs quantitative evaluation based on test data and calculates final performance rating with a 1D concept model. Through this, we focused on applying the architecture performance index and securing development capabilities to set product line goals. As a result, virtual performance check is possible through the virtual environment for vehicle driving performance, and at the same time, it is possible to review the product line goal setting using the benchmarking data. The concept of the driving performance index based on test data was introduced into a virtual environment to set an objective performance goal by attempting to predict the driving performance of a concept vehicle. In particular, by developing a 1D-based structured concept model, we have built an automated infrastructure that can maintain consistency between analysis models and data management and respond quickly to various architectures. Through this, it was intended to promote data-based work. In order to utilize engineering data, a module-based system DB is established and an environment that enables automatic simulation model update based on the stand-alone data input process is provided. In addition, to verify the reliability of the driving performance prediction model, the analysis results for main driving performance scenarios were compared and verified by using test and design data of mass-produced vehicles. The simulation results using the drawings-based design specifications and test-based system characteristics followed the test results well, and it was confirmed that the reliability of predicting the vehicle performance was sufficiently ensured in the concept design stage. However, in order to obtain high-quality simulation results from some performance item perspectives, we need to further improve system-specific modeling levels and try to reflect nonlinear characteristics (nonlinearity of mount systems and rotational mechanical systems). In addition, we felt the need to develop our own driving performance evaluation technology linked to our test evaluation technology and DB, and we were able to clarify the need for more systematic driving performance-related DB management and high-quality data collection/storage. In conclusion, the methodology proposed in this paper has great significance from two perspectives. First, we proposed and applied a new concept development methodology to solve the problems of concept vehicle development process and difficulty in setting goals at the vehicle architecture development stage. A series of systematic processes for creating electric vehicle architectures and selecting an optimal architecture from a fuel efficiency/acceleration performance perspective were constructed, and a driving performance scenario based on a 1D concept model was simulated, and a target setting methodology was proposed using benchmarking data. Second, This study became the foundation for implementing a system and development environment that share both the concept model and data. Our organization is making efforts to build a MBSE platform that shares data with members who want it and aims for change management through data. The added value of this study will be the base technology for implementing an MBSE platform system and development environment that share models and data to all members of the organization, which is used as a large asset for implementing the MBSE system in the company.

Mr. ILSOO JEONG, Senior Research Engineer, Hyundai Motor Company