The Engineering Meetings Board has approved this paper for publication. It has successfully completed SAE’s peer review process under the supervision of the session organizer. This process requires a minimum of three (3) reviews by industry experts. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form o r by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of SAE. ISSN 0148-7191 Positions and opinions advanced in this paper are those of the author(s) and not necessarily those of SAE. The author is solely responsible for the content of the paper. SAE Customer Service: Tel: 877-606-7323 (inside USA and Canada) Tel: 724-776-4970 (outside USA) Fax: 724-776-0790 Email: [email protected] SAE Web Address: http://www.sae.org Printed in USA ABSTRACT Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture. This vehicle features a 1.9L diesel engine coupled with a 10 kW belted starter-alternator to drive the front axle, and a 32 kW AC induction machine to drive the rear axle. This dual electric machine configur ation provides the vehicle with extensive control capabilities such as engine load optimization, engine start-stop, regenerative braking, effective driveline control and electric all-wheel drive. The Ohio State Challenge X team has successfully integrated the model-based design concept into the overall vehicle development process through the use of MathWorks tools. The team concentrated its efforts on the experimental validation of hybrid-electric vehicle models as well as on the verification and optimization of vehicle control systems. The use of MathWorks products such as MATLAB TM, SimulinkTM, StateflowTM, SimDrivelineTM and Real-Time WorkshopTM greatly simplified these processes. INTRODUCTION Industry is moving toward model-based design to reduce development time and costs. Various companies reported reduced development times by 50% or more through the use of this development approach [1]. Model-based design allows engineers to develop, simulate and test their design, and then modify the model quickly, enabling improved design performance through rapid iterations. This process enables engineers to evaluate a new design without investing in hardware prototyping, which can be costly and time-intensive. In addition, model-based design allows for design verification to ensure the system meets desired technical specifications, improves reliability by predicting how the system will function under real-life operating conditions, and reduces calibration by allowing initial adjustments to be made directly to the system model. In automotive engineering, vehicle models can be used to achieve various objectives. In order of increasing dynamic complexity, vehicle models can be classified as static, quasi-static, low-frequency dynamic, and high-frequency dynamic models. Both