Mingjie Zhao

Mingjie Zhao

Blending gear shift strategy design and comparison study for a battery electric city bus with Automated Manual Transmission

Mingjie Zhao, PhD candidate, Beijing Institute of Technology (China)
Giorgio Rizzoni, faculty mentor


  • Hometown: Beijing, China
  • Degrees received: Master of Science in engineering, Beijing Institute of Technology, China

What is the issue or problem addresses in your research?

To improve the performance of heuristic strategy used in most of the electric city buses equipped with Automated Manual Transmission (AMT) currently, this study 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.

What methodology did you use in your research?

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.

What are the purpose/rationale and implications of your research?

In order to overcome the inherent deficiencies in the conventional design process of gear shift schedule, a systematic blending extraction method is proposed to obtain the online available optimal gear shift rules for a city bus with multi-speed transmission. The results demonstrate that the proposed strategy can be implemented in real time successfully improving the energy consumption performance by 10.78%, and it is efficient and flexible enough in practical application.