Processing math: 100%
Decentralized Control of Intercity Electric Automated Buses via Time-Varying Objective Prioritization | IEEE Conference Publication | IEEE Xplore

Decentralized Control of Intercity Electric Automated Buses via Time-Varying Objective Prioritization


Abstract:

This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timeta...Show More

Abstract:

This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timetable. Some of these stops are provided with a charging infrastructure allowing to charge the bus batteries. The paper proposes a decentralized control scheme for determining the optimal speed profiles, the dwell and charging times of the buses, by taking into account the traffic conditions along the road through a suitable traffic flow prediction model. Two objectives are considered contemporarily: the minimization of the deviations from the timetable and the minimization of the energy lack at the end of the bus route. To attain both these conflicting objectives, a lexicographic approach is adopted to design the controller which considers that, depending on the system state, the priority of the two objectives can change. Accordingly, the proposed control scheme changes the objective prioritization in real time and switches between two different lexicographic-based optimal control solutions. Some tests are discussed in the paper to show the effectiveness of the proposed control scheme.
Date of Conference: 13-15 December 2023
Date Added to IEEE Xplore: 19 January 2024
ISBN Information:

ISSN Information:

Conference Location: Singapore, Singapore
References is not available for this document.

I. Introduction

One of the main challenges of our society is undoubtedly the protection of the environment and the health of our planet. Among the human activities causing damage to the environment, road transport represents one of the main sources. Electric mobility seems to represent a viable alternative to reduce the environmental impact of road transport, especially public transport [1]. The mobility transition for smart cities [2] is not only represented by electrification but also by the development of connected and automated vehicles [3]. In this paper we consider electric automated buses which have to follow a given line in extra-urban roads. Each vehicle has to visit, in given time windows, specific stops which can be provided or not with charging infrastructures. Our main goal is to devise a control strategy in order to regulate the speed of buses along the route, as well as the dwell and charging times at stops. Since each bus is traveling in inter-urban roads, without dedicated lanes, it is important to base the control strategy on the traffic prediction, i.e. considering the traffic state that the bus will encounter along its path. Some research works found in the literature aim at defining eco-driving strategies for electric buses traveling in urban areas where the presence of reserved lanes allows to neglect the influence of traffic [4], [5].

Select All
1.
S. Borén, "Electric buses' sustainability effects noise energy use and costs", International Journal of Sustainable Transportation, vol. 14, pp. 956-971, 2020.
2.
Q.-S. Jia, H. Panetto, M. Macchi, S. Siri, G. Weichhart and Z. Xu, "Control for smart systems: Challenges and trends in smart cities", Annual Reviews in Control, vol. 53, pp. 358-369, 2022.
3.
M. Azad, N. Hoseinzadeh, C. Brakewood, C.R. Cherry and L.D. Han, "Fully Autonomous Buses: A Literature Review and Future Research Directions", Journal of Advanced transportation, 2019.
4.
J. Flores Paredes, G. Padilla Cazar and M.C.F. Donkers, "A shrinking horizon approach to eco-driving for electric city buses: Implementation and experimental results", IFAC-PapersOnLine, vol. 52, pp. 556-561, 2019.
5.
R. Lacombe, S. Gros, N. Murgovski and B. Kulcsár, "Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines", IEEE Transactions on Intelligent Transportation Systems, vol. 23, pp. 10662-10679, 2021.
6.
C. Pasquale, S. Sacone, S. Siri and A. Ferrara, "Optimal charging and speed control of electric buses based on traffic flow predictions", Proc of. 6th IEEE CCTA, pp. 1011-1016, 2022.
7.
C. Pasquale, S. Sacone, S. Siri and A. Ferrara, "Traffic-prediction-based optimal control of electric and autonomous buses", IEEE Control Systems Letters, vol. 6, pp. 3331-3336, 2022.
8.
C. Pasquale, S. Sacone, S. Siri and A. Ferrara, " Multi-objective optimization of electric autonomous bus trajectories based on the varepsilon constraint method ", 31 st MED, pp. 472-477, 2023.
9.
M. Ehrgott, Multicriteria optimization, Berlin-Heidelberg:Springer, 2005.
10.
S. Peitz and M. Dellnitz, "A Survey of Recent Trends in Multi-objective Optimal Control - Surrogate Models Feedback Control and Objective Reduction", Mathematical and computational applications, vol. 23, no. 30, 2018.
11.
A. Bemporad and D.M. de la Peña, "Multiobjective model predictive control", Automatica, vol. 45, pp. 2823-2830, 2009.
12.
A. Kotsialos, M. Papageorgiou, C. Diakaki, Y. Pavlis and F. Middelham, "Traffic Flow Modeling of Large-Scale Motorway Networks Using the Macroscopic Modeling Tool METANET", IEEE Transactions on Intelligent Transportation Systems, vol. 3, pp. 282-292, 2002.
13.
S. Siri, C. Pasquale, S. Sacone and A. Ferrara, "Freeway traffic control: a survey", Automatica, vol. 130, pp. 109655, 2021.
14.
J. Löfberg, "YALMIP: A Toolbox for Modeling and Optimization in MATLAB", Proc. of the CACSD Conference, 2004.

Contact IEEE to Subscribe

References

References is not available for this document.