Optimal Control Strategy for PHEVs using Prediction of Future Driving Schedule

Optimization based control methods for Plug-in Hybrid Electric Vehicles require the knowledge of an entire driving cycle and an elevation profile to obtain the optimal performance over fixed driving route. In this paper, the method using traffic information to predict the future driving cycle and the optimal control strategy based on Pontryagin’s Minimum Principle (PMP) are investigated in order to minimize the fuel consumption on a given trip distance as well as develop a real-time implementable control strategy. To predict future driving patterns, Dynamic Programming theory is proposed for the calculation of the vehicle speed with respect to the driving distance with the knowledge of traffic condition received from the external traffic information like ITS. This is achieved by minimizing the proposed cost function on each segment. The result of the generated speed profile can well estimate the driving pattern of the real driver. Also, 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 out the estimated initial co-state similar to the optimal co-state. Simulation results show that this approach guarantees the best efficiency under reasonable condition and the minimization of the fuel consumption on the trip distance between origin and destination.