To further explore the potential of fuel economy for hybrid electric vehicle (HEV) , an adaptive energy management strategy (EMS) considering driver’s power demand reasonability is proposed, which is necessary to reduce fuel consumption, emission and traffic congestion. To get accurate and reliable control strategy two aspects are the most important: 1) a rigorous and organized modeling approach to describe complicated powertrain system of HEV, 2) a trade off between optimization and real time. The Energetic Macroscopic Representation (EMR) is a graphical synthetic description of electromechanical conversion system based on energy flow. Based on Energetic Macroscopic Representation (EMR) a powertrain architecture of HEV is constructed. Generally EMS includes rule based that can be used online with suboptimal solution and optimization based that ensures the minimum fuel consumption with heavy computation duty and requirement of prior knowledge. Combination of two kings of EMS to trade off optimization and real time is an intelligent selection. In this paper global optimization algorithm is utilized to compute optimal power demand and power split proportion from several typical driving cycles. Finally a fuzzy logic(FL) algorithm is designed based on DP benchmarks and can be used on free route with cycle identification. Due to driver’s stochastic operation, a torque offset strategy is developed to guarantee the satisfaction of high power demand in special case.