Energy-aware predictive control for electrified bus networks

Abstract

For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus 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, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle 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. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3 − 8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses. Formulating the multi-objective cost functionBuses operate on a route based on their timetable. When the schedule is tight, their trajectory shall be carefully planned while considering the schedule, passenger demand, and energy efficiency.The proposed bus velocity control algorithm (referred hereafter as control algorithm) creates an optimal trajectory within a predefined prediction horizon, considering the schedule, the number of passengers waiting and the location of other buses. The trade-off in the optimization is the total energy consumed, which is modeled as a physical energy consumption model. During operation, due to irregular dwell times and traffic disturbances, they tend to get out of sync with the schedule and start bunching. The goal of the control algorithm is to ensure timetable and

Robustness and disturbances in public transport

“…Examples for using a MPC approach to avoid bunching are Andres and Nair ( 2017 ) and Varga et al. ( 2019 ) (see Sect. 4.4 for a brief introduction to this type of approach).…”

“…( 2019 ) Platooning Impact analysis regarding traffic control and energy consumption Sun and Schmöcker ( 2018 ) Passenger behavior Considering overtaking being allowed (or not) Tian ( 2021 ) Use of short-turning strategy to alleviate bus bunching Case for Beijing (China) Varga et al. ( 2019 ) Model-predictive control Includes a passenger waiting model Verbich et al. ( 2016 ) Combining AVL and APC data to investigate the connection of bunching with dwell and running times Data from TriMet (Portland, USA) Zhao et al.…”

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

“…But in all those papers, the aim is to fulfill a serviceoriented objective, and no attention is paid to the eco-driving of the bus fleet as a result. To the best of our knowledge, only [29] includes an energy minimization aspect to the bus line control problem. In that paper, the authors develop a multiobjective MPC scheme with includes energy and servicerelated cost terms.…”

A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

“…Additionally, these models need to be validated and modified for the application region through national-level adjustment measures (e.g., the work by Smit and Ntziachristos [36] in Australia for modifying and adapting COPERT for Australian traffic fleet). For accurate vehicle modelling, Varga et al [37] proposed microsimulation environment to compare relative performance, headway, optimisation and platooning behaviour of different traffic management scenarios.…”