Powertrain modeling is an invaluable tool for exploring energy saving potential in commercial vehicles. A robust, automated method to estimate parameters for these models can accelerate the pace of model development and calibration, while enriching existing data sets. A Bayesian technique to estimate mass, drag coefficient, rolling resistance and other vehicle characteristics was developed. The estimates were validated using dynamometer data, then applied to recorded vehicle CAN data. Individual vehicle results and database trends are presented.