Abstract
Optimization-based control methods for plug-in hybrid electric vehicles require knowledge about an entire driving cycle and an elevation profile to obtain optimal performance over a fixed driving route. This paper details our investigation into the method of using traffic information to predict the future driving cycle, as well as an examination of the optimal control strategy based on Pontryagin’s Minimum Principle, in order to minimize fuel consumption on a given trip distance and to develop a real-time implementable control strategy. To predict future driving patterns, the Dynamic Programming theory is proposed for the calculation of vehicle speed with respect to driving distance, under the assumption that data about traffic conditions are obtained from external traffic information, such as Intelligent Transportation Systems. Prediction of future driving speed is achieved by minimizing the proposed cost function on each segment. The results of the generated speed profile can properly estimate the driving pattern of the driver. Also, a co-state generation algorithm is applied to determine the parameters with respect to the required power deduced from the predicted driving cycle. The proposed co-state generation model can find the estimated initial co-state that is similar to the optimal co-state. Simulation results indicate that this approach guarantees the best efficiency under reasonable conditions and the minimization of fuel consumption on the trip distance between the origin and destination.