SAE04-454 Feasibility of Reusable Vehicle Modeling: Application to Hybrid Vehicles A. Rousseau, P.Sharer, F. Besnier Argonne National Laboratory Copyright © 2004 SAE International ABSTRACT Many of today’s vehicle modeling tools are good for simulation, but they provide rather limited support for model building and management. Setting up a simulation model requires more than writing down state equations and running them on a computer. The role of a model library is to manage the physics of the system and allow users to share and reuse component models. In this paper, we describe how modern software techniques can be used to support modeling and design activities; the objective is to provide better system models in less time by assembling these system models in a “plug and play” architecture. With the introduction of hybrid electric vehicles, the number of components that can populate a model has increased considerably, and more components translates into more drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations. The proposed approach could be used to establish standards within the automotive modeling community. INTRODUCTION In a world of growing competitiveness, the role of simulation in vehicle development is constantly increasing. Because of the number of possible advanced powertrain architectures — such as hybrid or fuel cell — that can be employed, the development of the next generation of vehicles will require accurate, flexible simulation tools. Such tools are necessary to quickly narrow the technology focus to those configurations and components that are best able to reduce fuel consumption and emissions. The simulation tools must be flexible enough to encompass a wide variety of components and drivetrain configurations. With improvements in computer performance, many researchers started developing their own vehicle models. But often, computers in simulation are used only to “crunch numbers.” Moreover, model complexity is not the same as model quality. Using wrong assumptions can lead to erroneous conclusions; errors can come from modeling assumptions or from data. To answer the right questions, users need to have the right modeling tools modeling. For instance, one common mistake is to study engine emissions by using a steady-state model or to study component transient behavior by using a backward model. Indeed, specific component models and modeling philosophies should be used for specific applications. In this article, we describe how a graphical user interface (GUI), combined with an innovative software architecture, can be used to support powertrain modeling. It is important to separate modeling from simulation: We will focus on component model management and powertrain building management. that the paper will address ways in which component model management involves much more than assigning specific folders for each component and discuss how powertrain building management is more complicated than just manually connecting components together. The Powertrain System Analysis Toolkit (PSAT) developed at Argonne National Laboratory will be used to explain the methodology. PSAT INTRODUCTION PSAT [1, 2] is a powerful modeling tool that allows users to realistically evaluate not only fuel consumption but also vehicle performance. One of the most important characteristics of PSAT is that it is a forward-looking model — meaning that PSAT allows users to model real- world conditions by using real commands. For this reason, PSAT is called a command-based model. A driver model estimates the wheel torque necessary to achieve the desired vehicle speed. The powertrain controller then sends real commands to the different components: throttle for engine, displacement for clutch, gear number for transmission, or mechanical braking for wheels to achieve the desired wheel torque. Because the components react to the commands as they would under real-world conditions, researchers can implement advanced component models (based on physics rather than lookup tables), take into account transient effects (e.g., engine starting, clutch engagement/disengagement or shifting), or develop realistic control strategies (which can be used later to control hardware).