In the design of a vehicle powertrain, fuel consumption is a key factor that requires to be minimized within various constraints. Simple energy models can be of great help in the design process – by clarifying the role of the main dimensioning parameters and reducing the computational time of complex routines aimed at optimizing these parameters. In this work, a Fully Analytical fuel Consumption Estimation (FACE) model has been derived to predict the fuel consumption of light- and heavy-duty series hybrid-electric powertrains over given drive cycles. FACE is based on parametric models of the main components that are combined to represent the whole powertrain. Besides, energy management strategy in a hybrid-electric powertrain is indispensable to the control of energy flows between different sources. When the drive cycle and the main design parameters (e.g. vehicle road load, as well as nominal power, torque, volume of engine, motor/generators, and battery) are considered as inputs, FACE predicts the fuel consumption in closed form. The correlations leading to the fuel consumption are derived from analytical approximations of the efficiency maps. Moreover, the coefficients of fitting curves are expressed as a function of the main design parameters through analyzing the characteristics of several engines, motors, and batteries belonging to similar technologies. Similarly, the usual design constraints (such as acceleration and gradeability metrics) are scaled with the design parameters as well. In this way, constrained minimization of fuel consumption is achieved analytically. The FACE model for light-duty vehicle is a virtual series hybrid electric vehicle that has equivalent powertrain parameters to a counterpart in the market. Optimization of design parameters of the powertrains have been performed on both light- and medium-duty series hybrid powertrains, thereby demonstrating the effectiveness of the FACE model to optimize the design parameters. The investigated medium-duty series hybrid-electric powertrain is an urban delivery truck, whose results are compared with MELODYS project demonstrator designed using a heuristic model-based process.