Open Access
Analysing capacity challenges in the Multi-Airport System of Mexico City
(Dime University of Genoa, 2022) Mújica Mota, Miguel; Faulín Fajardo, Javier; Izco Berastegui, Irene; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
The relentless growth in Mexico City’s aviation traffic has inevitably strained capacity development of its airport, raising the dilemma between the possible solutions. In the present study, Mexico’s Multi-Airport System is subjected to analysis by means of multi-model simulation, focusing on the capacity-demand problem of the system. The methodology combines phases of modelling, data collection, simulation, experimental design, and analysis. Drawing a distinction from previous works involving two-airport systems. It also explores the challenges raised by the Covid-19 pandemic in Mexico City airport operations, with a discrete-event simulation model of a multi-airport system composed by three airports (MEX, TLC, and the new airport NLU). The study is including the latest data of flights, infrastructures, and layout collected in 2021. Therefore, the paper aims to answer to the question of whether the system will be able to cope with the expected demand in a short-, medium-, and long term by simulating three future scenarios based on aviation forecasts. The study reveals potential limitations of the system as time evolves and the feasibility of a joint operation to absorb the demand in such a big region like Mexico City
Open Access
Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data
(MDPI, 2025-07-11) Al-Rahamneh, Anas; Izco Berastegui, Irene; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Institute of Smart Cities - ISC
In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems.

Faulín Fajardo, Javier

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