Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information

Abstract

Pontryagin's minimum principle (PMP) based energy management strategy, which describes the optimal power distribution of hybrid electric vehicle as Hamiltonian minimization problem, gains the ability to ensure real-time performance and near-optimal solutions, but demonstrates poor cycle adaptability. Therefore, this paper proposes a novel fuzzy adaptive method for the PMP-based optimal strategy by utilizing real-time traffic information that is described by the average velocity and the standard deviation of the velocity on different road segments. The two velocity feature parameters are derived by the data of floating vehicles. A three-layer back-propagation neural network (BP-NN) is constructed to predict the average power with the velocity feature parameters. On the basis of battery charging sustainability, the fuzzy adaptive law is designed to calculate the co-state of the PMP-based strategy using the predicted average power and the actual battery SOC. Finally, the performance of the proposed strategy is evaluated by comparative simulation studies. It is validated that the average velocity and the standard deviation of the velocity can be well evaluated by the information of floating vehicles. Tested by the standard and practical sampled driving cycles, the BP-NN demonstrates good performance in predicting the average power with the selected velocity feature parameters. Compared with the strategy whose co-state is just corrected by the battery SOC in the feed-back manner, the proposed PMP-based fuzzy adaptive method demonstrates superiority in improving the vehicle fuel economy and maintaining the battery charging sustainability under various driving cycles.