Citation

Hwang G, Hwang Y, Shin S, Park J, Lee S, Kim M. Int. J. Automot. Technol. 2022; 23(4): 983-992.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12239-022-0085-z

PMID

unavailable

Abstract

Given the vehicle speed during actual driving, it is possible to apply an advanced energy management strategy for achieving better efficiency and less emission. We conducted a study to predict the future speed while driving of city buses, where only a few bus driving data and bus stop IDs are used without external complex traffic information. The speed prediction models were developed based on long time short memory (LSTM) and a gated recurrent unit (GRU), and a deep neural network (DNN) is also adopted for the bus stop ID processing. The performances of the models were analyzed and compared such that we found the LSTM-based model presents remarkable and practical prediction ability in accuracy and time spent. Adopting the proposed speed prediction model would make it a reality sooner, application of the optimal energy control strategy in the real world.

Language: en

Keywords

Energy management strategy (EMS); Gated recurrent unit (GRU); Hybrid electric bus (HEB); Long short-term memory (LSTM); Neural network; Speed prediction