As the core control of hybrid electric vehicles (HEVs), energy management strategy directly affects the fuel economy of vehicle under road conditions. The relative cubic velocity and positive kinetic energy were first selected as characteristic parameters to represent road conditions. A dynamic model of HEV was then established, including the longitudinal dynamics model, engine model, drive motor model, and power battery model. The representative cyclic conditions were further selected by condition similarity analysis, and the characteristic parameters of conditions were extracted to evaluate the energy consumption of HEV. Meanwhile, the low and medium speed portions of CLTC were selected as representative cyclic conditions for road conditions based on the degree of parameter approximation. Finally, an optimization strategy model based on dynamic programming guided the optimization of HEV's rule-based energy management strategy. The simulation results show that the optimized energy management strategy reduces HEV fuel consumption by 1.6% to 3% under real road conditions.