By Topic

Energy-Efficient Locomotive Operation for Chinese Mainline Railways by Fuzzy Predictive Control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Yun Bai ; MOE Key Lab. for Urban Transp. Complex Syst. Theor. & Technol., Beijing Jiaotong Univ., Beijing, China ; Tin Kin Ho ; Baohua Mao ; Yong Ding
more authors

With the increasing energy consumption in Chinese mainline railways amid the worldwide carbon emission concerns, the need for energy-efficient locomotive operation becomes urgent. Locomotive operation is directly linked to speed limits imposed by the train ahead through signaling. In China's mainline railways, speed limits for locomotive operation change frequently because of relatively short headways in a highly congested network. Whenever the speed limit changes, the locomotive operation must be determined again quickly to adapt to the new speed limit. As a result, the energy-efficient locomotive operation is a real-time optimization problem with time-varying constraints, in which the tradeoff between solution optimality and computational time is essential, but it has not been considered adequately in previous studies. This study develops a fuzzy predictive control approach, continuously providing locomotive operation instructions, with respect to the prevailing speed limits, to reduce energy consumption of train movement. The proposed approach is implemented in an onboard decision support system to assist drivers. The system is tested on the Ning'xi line in China. The results indicate that energy consumption on train operations is reduced by 4%, without increasing the runtime between stations, while the computational requirement satisfies the demand of real-time solutions. Extensive simulations show that the proposed approach is able to provide sufficient solution optimality in reasonable computational time under different operation settings.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:15 ,  Issue: 3 )