Hydrocarbon emissions represent one of the causes for the formation of ozone and other photochemical pollutants in the atmosphere. Hydrocarbons (HC's), their oxidation products and oxides of nitrogen (NO and NO2) react a few hundred meters of air above major cities, in the presence of sunlight, to produce strongly oxidizing components of which ozone is the most prevalent. Motor vehicles contribute to atmospheric hydrocarbons through exhaust and fuel system evaporative emissions. The main factors affecting the amount of fuel vapor generation from the fuel system are tank pressure, fuel properties, and fuel temperature.The degree of control that can be exercised over these factors is limited. Fuel temperature, the only factor that depends primarily on vehicle design and the thermal environment around the fuel system, has a major effect on vapor generation. Therefore, this work focuses on thermal energy input to the fuel as an area in which a method could be developed for controlling fuel vapor generation in the tank. A mathematical model that describes the relationships between fuel temperature and the fuel system design characteristics has been developed. The developed model determines fuel temperature and heat input at different locations along the fuel lines, along the fuel rails, and inside the tank at each time step. The model provides fully transient thermal and emissions analysis, return-less fuel system analysis, and allows analysis of the effect of different materials (steel, aluminum, plastic, and nylon). The model uses an iterative finite difference algorithm to solve the differential equations that describe the transient behavior of the fuel system. A material library is added to allow users to study the effect of different materials on the fuel temperature and fuel emissions.The analysis results show the tank temperature, and the amount of vapor vented during specific driving schedules. The parameters investigated include tank material, fuel rail and fuel line materials, and fuel RVP(Reid Vapor Pressure). The model shows excellent agreement with field tests and provides precise calculation of quantities and control of parameters that are extremely difficult to control in a test environment.