This paper uses the minimization and weighted sum of battery capacity loss and energy consumption under driving cycles as objective functions to improve the economy of Electric Vehicles (EVs) with an hybrid energy storage system composed of power batteries and ultracapacitors. Furthermore, Dynamic Programming (DP) is employed to determine the objective function values under different weight coefficients, the comprehensive cost consisting of battery aging and power consumption costs, and the relationship between the hybrid power distribution. We also evaluate the real-time fuzzy Energy Management Strategy (EMS), fuzzy control strategies, and a strategy based on DP using the World Light vehicle Test Procedure (WLTP) driving cycle and a synthesis driving cycle derived from New European Driving Cycle (NEDC), WLTP, and Urban Dynamometer Driving Schedule (UDDS) as examples. Then, the proposed strategy is compared with the fuzzy control strategy and the strategy based on DP. Compared with fuzzy energy management strategy (namely FZY-EMS), the proposed EMS reduces the battery capacity loss and system energy consumption. The results demonstrate the effectiveness of the proposed EMS in improving EV economy.