This article presents a control framework designed to manage the speed, dwell time, and charging schedules of electric, automated, and connected buses operating on mixed-traffic lines without reserved lanes. The control architecture is composed of two layers, each defined by distinct objectives, levels of detail, and time scales, resulting in a multiscale control framework. The high-level control layer periodically solves a multiobjective optimal control problem to ensure the adequate transport service while considering a prediction of traffic conditions along the bus line. In contrast, the low-level control layer employs a model predictive control to accurately track the high-level control actions. The MPC-based low-level controller accounts for the detailed dynamic behavior of the bus, ensuring robust execution of the high-level directives. The effectiveness of the proposed control scheme is assessed through its application to a realistic case study in Italy, demonstrating improved service reliability and energy efficiency.

Optimal Speed and Charging Control for Electric Buses: A Multiscale MPC-Based Framework

Ferrara, A.
2026-01-01

Abstract

This article presents a control framework designed to manage the speed, dwell time, and charging schedules of electric, automated, and connected buses operating on mixed-traffic lines without reserved lanes. The control architecture is composed of two layers, each defined by distinct objectives, levels of detail, and time scales, resulting in a multiscale control framework. The high-level control layer periodically solves a multiobjective optimal control problem to ensure the adequate transport service while considering a prediction of traffic conditions along the bus line. In contrast, the low-level control layer employs a model predictive control to accurately track the high-level control actions. The MPC-based low-level controller accounts for the detailed dynamic behavior of the bus, ensuring robust execution of the high-level directives. The effectiveness of the proposed control scheme is assessed through its application to a realistic case study in Italy, demonstrating improved service reliability and energy efficiency.
2026
The AI, Robotics & Automatic Control category is concerned with resources on the research and techniques of artificial intelligence; that is, the creation of machines that exhibit characteristics of human intelligence (e.g., efficient representation of knowledge, reasoning, deduction, problem solving, heuristics, and analysis of contradictory or ambiguous information). Related AI technologies include expert systems, fuzzy systems, natural language processing, speech and pattern recognition, computer vision, decision-support systems, knowledge-bases, and neural networks. Robotics resources are concerned with the design, construction, and operation of robots. Automatic Control resources cover the design and development of regulating processes and systems that replace the necessity of human intervention. Topics include adaptive control, robust control, discrete-event control, dynamic control, fuzzy control, and optimal control. Cybernetics resources are concerned with the control and communication within and between artificial (machine) systems and living or natural systems.
Esperti anonimi
Inglese
Internazionale
ELETTRONICO
34
3
1435
1450
16
Electric and automated buses; model predictive control; optimal bus charging; optimal bus speed; Pareto-optimal solution
https://ieeexplore.ieee.org/document/11455236
no
7
info:eu-repo/semantics/article
262
Basile, G.; Bozzi, A.; Bracco, S.; Pasquale, C.; Sacone, S.; Siri, S.; Ferrara, A.
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1548626
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