Building a vehicle model with an accuracy sufficient for fuel economy analysis is a time consuming job even with the modern day simulation tools. Getting the right kind of data for modelling a vehicle can itself be challenging. While OEMs advertise the power and torque capability of their engines, the efficiency data for the components or the control algorithms are not usually made available for independent verification. US DOE funds the testing of vehicles at Argonne national lab and this test data is publicly available. Argonne is also the premier DOE laboratory for modelling and simulation of vehicles. By combining these two unique capabilities, a process is developed to automatically develop a model for any conventional vehicle that is tested at Argonne. This paper explain the process of analyzing the publicly available test data and computing the various component parameters from that. This includes the development of engine fuel maps, gear shift maps, estimation of gear ratios and other vehicle specifications necessary to build a model. The paper includes a case study where this process is applied on a conventional vehicle test data, and compares the results from the actual test to the outputs from the automatically generated vehicle model. This brings out the advantages and limitations of this process.