A Simulation-Integrated Decision Support System for Advanced Vehicle Design Demonstrated on Colorado State's EcoCAR

Abstract

Modeling and simulation are becoming standard tools in the academic and industrial worlds of advanced automotive design. When coupled with optimization, these tools have the potential to fundamentally change the design process and facilitate rapid progress in the state of the field by opening the door to inverse design. In inverse design, the designer controls decision variables directly, and design variables are obtained as outputs. Through inverse design, the tradeoffs imbedded in vehicle technologies can be communicated efficiently to decision makers, and the mystery in the design space can be illuminated. The full capabilities of inverse design, however, have not yet been realized due to lengthy simulation run times for optimization and not having models integrated with the decision metrics. As first-time participants in the EcoCAR2 competition, the Colorado State University design team embarked on a research mission to investigate feasible technologies and identify tradeoffs within the context of the competition, which required confronting these roadblocks. To perform architecture selection and component sizing the CSU team developed a decision support system (DSS) from which inverse design was performed. Surrogate models of the vehicle design space were developed to bypass the roadblock of computation time and competition-relevant decision metrics were directly integrated to enable inverse design. This unique approach enabled the evaluation of optimized technology options within the context of the competition and provided assessment of Pareto-optimal tradeoffs within each technology option. With this tool the CSU design team illuminated the design space of the EcoCAR2 competition and selected a vehicle that not only excels in the competition but also pushes the boundaries of conventional design philosophy. The CSU design team has chosen to build a blended charge-depleting mode plug-in fuel cell hybrid electric vehicle that optimizes energy consumption and emissions while meeting consumer acceptability requirements. The methods and results of this research are presented in the following report.