The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions.

Vehicle electrification has been proposed as a strategy for decarbonizing the transport sector. However, companies operating fleets of light-duty internal combustion engine vehicles (ICEVs) for personnel and freight transportation still lack the data and decision-making tools necessary to evaluate the transition to electric vehicles (EVs). This study proposes a novel methodology that combines the use of web applications with longitudinal vehicle dynamics to determine energy consumption and regenerative braking potential. In addition, it incorporates energy consumption data, taxes, subsidies and vehicle discounts to conduct a comparative analysis of the total cost of ownership of EVs versus IECVs. The proposed methodology was applied to evaluate the feasibility of an energy transition in a fleet of vans and pickup trucks used for transporting personnel and materials. The results show that the model can estimate energy consumption with an average error of 7.6% compared to monitored data. Replacing 10 ICEVs with 5 electric vans and 5 electric pickup trucks could reduce energy consumption by up to 62%. The operating cost of the electric van is 8.5% lower than its ICEV counterpart, while the electric pickup achieves a 13.8% reduction in operating costs compared to the combustion model. The technical findings and the methodology of this study are expected to provide a solid basis for companies to evaluate the energy and economic feasibility of electrifying their fleets.

Ontologies enable the construction of formal and reusable semantic representations of domain knowledge; however, keeping them updated in response to the dynamic evolution of concepts remains a challenge, particularly in the leadership domain, where the diversity of approaches, theories, models, and styles generates conceptual ambiguity and information overlap. To address this limitation, this study proposes an ontology learning methodology based on topic modeling (TM) to create an adaptive component capable of identifying candidate concepts for enriching a previously developed leadership ontology. The methodology is structured into four stages: (1) corpus preprocessing; (2) manual labeling of the corpus evaluated through inter-annotator agreement using the Kappa index; (3) vocabulary construction by comparing five statistical weighting schemes (Inverse Document Frequency (IDF), Entropy, and three variants of Ochoa’s proposal) against a gold standard; and (4) sentence classification and concept construction using four probabilistic topic models (Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), and Biterm Topic Model (BTM)), taking as input both a corpus fully associated with the initial ontology and one partially associated. The IDF weighting scheme achieved the best balance between precision and recall (F-measure: 67.03%), and a Support Vector Machine (SVM) classifier reached an accuracy of 98.19% in sentence classification. The LDA and NMF models obtained the highest scores for thematic coherence. However, the expert evaluation revealed that the thematic coherence metric alone is insufficient for evaluating the resulting structures, as it favored the LDA model, while the NMF model generated more conceptually diverse structures. The proposed methodology enables the semi-automatic transformation of unstructured text into structured candidate concepts for subsequent ontology enrichment.

En Colombia, el sector transporte es responsable del 49% de las emisiones de CO₂ y del 43% del consumo total final de energía. Con el objetivo de mitigar estos impactos y avanzar hacia una transición energética más limpia, se ha impulsado la incorporación de flotas vehiculares eléctricas. En los últimos años, la venta de estos vehículos ha mostrado un crecimiento significativo, alcanzando 6 000 unidades en 2023. Este avance ha sido dado en parte por iniciativas gubernamentales orientadas a fomentar su adopción mediante planes y políticas públicas. No obstante, persiste la incertidumbre sobre si la tendencia actual del mercado será suficiente para alcanzar las metas nacionales, como la incorporación de 600 000 vehículos eléctricos para el año 2030. En este contexto, se destaca la importancia de analizar las tendencias del mercado de vehículos eléctricos. Este estudio propone el uso de un modelo de Máquina de Vectores de Soporte (SVM, por sus siglas en inglés) para proyectar la cantidad de vehículos livianos, y posteriormente, estimar el porcentaje de participación de los vehículos eléctricos en la flota vehicular durante el periodo 2030-2035. Para ello, se emplean datos históricos y variables exógenas como el Producto Interno Bruto (PIB), la población y el precio de la gasolina. Las proyecciones generadas a partir de este enfoque contribuyen a la planificación de infraestructura, la anticipación de necesidades de movilidad y el abordaje de desafíos relacionados con la sostenibilidad energética y la reducción de emisiones. Asimismo, facilitan la identificación de áreas de mejora y el diseño de estrategias alineadas con los compromisos climáticos internacionales.