Energy-aware predictive control for electrified bus networks
Journal article, 2019
with possibility of bunching. The proposed decentralized, busĀ fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework.
The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefifined, periodic timetable. The second objective is timetable tracking, and passenger waiting time minimization. In case of high passenger demand, it is desirable to haste the bus in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Alternative weighting strategies are compared and evaluated through realistic scenarios, in a calibrated microscopic traffic simulation environment. Simulation results confirm of 3-8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses.
Bus bunching
Multiobjective optimization
Receding control
Passenger wait
Energy consumption
Author
Balázs Varga
Budapest University of Technology and Economics
Tamas Tettamanti
Budapest University of Technology and Economics
Balázs Adam Kulcsár
Chalmers, Electrical Engineering, Systems and control
Applied Energy
0306-2619 (ISSN) 18729118 (eISSN)
Vol. 252 113477Public transit shared mobility - connected and safe solutions
Chalmers, 2020-03-01 -- 2021-03-01.
Chalmers, 2019-03-01 -- 2020-02-29.
OPerational Network Energy managemenT for electrified buses (OPNET)
Swedish Energy Agency (46365-1), 2018-10-01 -- 2021-12-31.
Driving Forces
Sustainable development
Areas of Advance
Transport
Energy
Subject Categories
Energy Engineering
Transport Systems and Logistics
Control Engineering
Environmental Sciences
DOI
10.1016/j.apenergy.2019.113477