For this work, a methodology of modeling and predicting fuel consumption in a hybrid vehicle as a function of the engine operating temperature has been developed for cold ambient operation (-7°C, 266°K). This methodology requires two steps: 1) development of a temperature dependent engine brake specific fuel consumption (BSFC) map, and, 2) a data-fitting technique for predicting engine temperature to be used as an input to the temperature dependent BSFC maps. For the first step, response surface methodology (RSM) techniques were applied to generate brake specific fuel consumption (BSFC) maps as a function of the engine thermal state. For the second step, data fitting techniques were also used to fit a simplified lumped capacitance heat transfer model using several experimental datasets.Utilizing these techniques, an analysis of fuel consumption as a function of thermal state across a broad range of engine operating conditions is presented. These techniques allow for prediction of fuel consumption for a vehicle as a function of the engine's operation and temperature. Results will be shown over repeated Urban Dynamometer Driving Schedule (UDDS) and US06 cycles at an ambient test cell temperature of 266°K. Details into the variability of fueling rates under a broad range of engine temperatures are presented. Certain details into the technique will be presented, as well as analysis comparing the model to experimental datasets.