Due to complex driving conditions in cities, it is hard to obtain the optimal economic performance with the rule-based strategy alone. An adaptive real time control strategy was proposed to adapt the various driving conditions and to improve fuel economy of a new series-parallel hybrid electric bus (SPHEB). This method consists of the Equivalent fuel Consumption Minimization Strategy and algorithm of driving pattern recognition in essence. The key role of ECMS is the equivalence factor, which is used to convert electrical power used into an equivalent fuel quantity. Four types of roadways were selected to present the characteristics of city driving cycle， and a driving pattern recognition approach was employed to obtain better estimation of the equivalence factor under different roadway types. The main idea of the adaptive real time control strategy is periodically updating the equivalence factor dependent on the corresponding driving condition. To validate the proposed strategy, a forward model was built on the basis of simulink. The simulation results demonstrate that the roadway types can be successfully recognized and the improvement of fuel economy is up to 8.55%, while the battery SOC is limited in the desired range.