An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Nowadays, a growing interest is in improving the efficiency of a parallel hybrid electric bus. In this article, we propose an adaptive energy management strategy (EMS) based on an equivalent consumption minimization strategy (ECMS) by using real-time traffic information described by the average speed, average acceleration, and standard deviation of speed for different road sections. The whole adaptive energy management system consists of offline and online components. Three velocity characteristic parameters are derived by utilizing the data from the vehicle. First, the road segments are classified through the K -means. Then, three Markov transition matrices on different types of roads are constructed with the velocity feature parameters, which can be used to predict the speed in the following time. On the other hand, the online component implements the EMS involved. The EMS incorporates predicted vehicle speed and road information into the ECMS. Based on this, the predicted vehicle speed and traffic information are applied to make adaptive changes to the equivalent factors in the ECMS. Finally, the performance of the proposed strategy is evaluated through a comparative simulation study and the results show the superiority of the approach in improving fuel economy. Comparative outcomes from hardware-in-the-loop tests additionally illustrate that the proposed strategy's fuel efficiency is compatible with the original design intent.