Blending gear shift strategy design and comparison study for a battery electric city bus with AMT

To improve the performance of heuristic strategy used in most of the electric city buses equipped with automated manual transmission (AMT) currently, this paper proposes a systematic blending extraction method to optimize and accelerate the shift schedule design process. The crucial related factors, including the shift time, transmission efficiency and various driving cycle features, are considered to assure the online practicability. Dynamic programming (DP) algorithm is applied over featured velocity profiles to explore the global optimal operating points offline. Then k-means clustering algorithm is adopted to extract the explicit optimal shift schedule, where the number of centroids is determined by hierarchical analysis process and a new distance calculation method is performed considering proper weighting factors to blend the shift points from different driving conditions. The stochastical driving cycle is generated randomly from the previous data and is used to validate the comprehensive performance by chassis dynamometer tests. A comparison study is conducted among the proposed and conventional shift strategies. Experimental results demonstrate that the extracted blending strategy can improve the energy consumption significantly and is proved to be efficient, flexible, and online implementable compared to the other strategies.