The approach towards building hybrid vehicles has evolved with time and requirements. What used to be direct prototype building activity has moved towards building mathematical models before the actual prototypes are built. These models are utilized in optimizing component sizes, design and calibrate controllers and to estimate fuel economy improvements. If model results show promise, the actual prototype building activity is started.But modeling of vehicles still has a long way to go before aligning with businesses and aiding them as decision making tools on R&D investments. The reason being - the model building activity itself is prolonged and expensive. In addition to this, a lot of proprietary information such as component efficiency maps is required in order to build the model. In absence of these component level data, extensive testing becomes necessary where again vast amounts of resources have to be allocated. Thus, although mathematical model building is intended as an optimization over prototyping, the need for quicker decisions at reduced costs has necessitated the optimization of the model building activity itself. Optimizing the modeling activity can go a long way in helping businesses to make quicker decisions and achieve faster time-to-market.This paper proposes an optimized model building activity even before the detailed vehicle model development can begin. This activity called as Power Absorption Modeling (PAM) deals mainly with the regenerated power and energy and the availability of this energy for traction of the parallel hybrid vehicle. The fuel economy improvement obtained from the PAM model along with knowledge of component sizes and their cost offers first cut results in evaluating the feasibility of hybridization. This enables the business to decide whether or not to make further investments into more detailed analysis of the given hybrid architecture for the scenario on hand.If the PAM model promises an acceptable pay-back for the customer, the second stage of detailed modeling can be taken up. This detailed system model involves more detailed component models, their interaction, high fidelity control algorithm development and tuning towards improved system efficiency and performance. This stage gives a more accurate picture of the individual component performances and their efficiencies. The results seen in this stage can form the criteria to decide on the viability of progressing to prototype development stage and thereon towards production.In this paper, we discuss the methodology of PAM modeling. Also elaborated are the advantages and disadvantages of the PAM over other detailed modeling techniques. An example of a parallel hybrid vehicle model developed using PAM is discussed. The PAM model and the detailed model of the same hybrid vehicle are simulated and the fuel economy improvement obtained from the models are tabulated and discussed. The time and effort involved in developing the two models is compared.