This study developed a methodology to navigate optimal speed trajectory to reduce driving energy consumption in EV in real world. In this work, the driving data was collected by self-developed OBD-II device from a test EV and it was utilized for developing some models. The collected operational information includes basic information related to electric motors, batteries and GPS, while elevation, traffic and weather information were also collected based on vehicle location information in conjunction with the vehicle's external information collection system. Based on comprehensive information, an energy consumption model was developed to convert power energy into electrical energy by estimating key parameters about vehicle dynamics with motor characteristics. To provide the optimal speed, which is a determinant of the energy consumption model, Markov chain-based driving pattern was learned to develop a speed prediction model and optimization was performed to present a speed that minimizes energy consumption relative to the predicted speed. The developed model conducted an experiment on the Pyeonghwa-ro section of Jeju Island, which has a complex environment. In the experiment, the optimal speed was applied at 25% of the total driving time, resulting in an increase of 3.2 to 3.8%. The optimum speed was determined by the traffic condition and slope, and more driving energy was reduced as the regenerative braking rate increased.