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Sustainability scores can be used to assess manufacturing strategies, going one step beyond a standard economic assessment. This work uses a previously proposed methodology to evaluate two of the most common welding processes for aluminium alloys that are specifically used in the fabrication of components for the transport industry based on their advantages in generating lightweight and dimensionally efficient parts. For comparison and as proof of concept, two welding methods were selected: Friction Stir Welding (FSW) and Gas Tungsten Arc Welding (GTAW). FSW attained a higher overall sustainability score. Values were calculated for an existing aluminium product, which was part of the opening and closing system of an electric train door, and the final score was 0.78 from FSW compared to 0.69 from GTAW, which was 11% higher in FSW compared to the conventional arc welding process. The analysis carried out included economic, physical, social, and environmental impacts. Finally, an example pertinent to a current EV component is described and considered along with a plan to determine the best welding process for a particular application, and with the calculations, the score obtained for GTAW was 0.43 and 0.68 for FSW, which was 36% higher that the result for the conventional arc welding process.

Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios.

In recent decades, scientific interest has grown in tackling the vehicle routing problem with a sustainable approach (Green VRP). There are numerous studies in the literature addressing environmental problems from the point of view of efficient planning that allows visualizing the benefits associated with the use of the new technologies in electric vehicles. This paper focuses on the electric vehicle routing problem and considers the batteries’ state of charge (SoC). The problem considers a set of customers, where each one has a specific demand and a time window. Deliveries are performed through a homogeneous fleet of electric vehicles with a fixed charging capacity and limited autonomy. In particular, when the vehicle is traveling, it consumes an amount of energy proportional to the distance it travels; therefore, it must visit battery recharging stations to continue and complete its route. The objective is to determine the performed routes with the minimum cost (time), while seeking to visit the recharging stations as many times as possible. In this way, overcharging and deep discharges are avoided by protecting the battery from degradation. In this paper, four models are proposed: the first model requires that the battery be fully charged in the stations; the second model allows partial recharging; the third formulation limits deep discharge; and a fourth formulation adheres to a limitation associated with overcharging and tries to keep the battery in its most comfortable place. The efficiency of the proposed formulations is tested in structured instances of different sizes. The results obtained show the efficiency of the formulations proposed for the electric vehicle routing problem when considering battery degradation.

Electric vehicles are considered as one of the most effective technologies for reducing current greenhouse gas emissions from the transport sector. Although in many countries, local and national governments have introduced incentives and subsidies to facilitate the electric vehicle market penetration, in Sweden, such benefits have been limited. Results from a survey carried out among private owners of electric vehicles are presented in this paper, including the analysis of the respondents socio-demographic characteristics, reasons for choosing an electric vehicle, charging locations and driving preferences, among others. The main results characterize current electric vehicle drivers as male, well-educated, with medium-high income; electric vehicles are used mainly for private purposes and charged at home during night time. Furthermore, the paper presents an analysis of the impact of large-scale penetration of electric vehicles on existing power distribution systems. The findings presented in this paper provide important insights for assuring a sustainable large-scale penetration of electric vehicles by learning from the experiences of early adopters of the technology and by analyzing the impact of different EV penetration scenarios on the power distribution grid.

Au cours des dernières décennies, le développement des infrastructures de transport a connu un grand développement, bien que les problèmes de circulation continuent de s'étendre en raison de l'augmentation de la population dans les zones urbaines qui nécessitent l'utilisation de ces moyens de transport. Cela a entraîné une augmentation des problèmes liés au contrôle de la congestion, qui a un impact direct sur les citoyens : pollution de l'air, consommation de carburant, violation des règles de circulation, pollution sonore, accidents et perte de temps. En Amérique latine, la croissance désordonnée des villes augmente les distances et les itinéraires, de même, il y a une augmentation accélérée du nombre de voitures et de motos, ce qui augmente le problème. En ce sens, les systèmes de transport intelligents sont une alternative pour améliorer l'environnement de trafic, ils intègrent l'internet des objets et des algorithmes intelligents, pour la collecte de données provenant de sources multiples et le traitement de l'information, respectivement, afin d'améliorer l'efficacité du flux de transport. Cependant, le traitement et la modélisation des données de trafic sont difficiles en raison de la complexité des réseaux routiers, des dépendances spatio-temporelles entre eux et des schémas de trafic hétérogènes. Dans cette étude de revue, (i) les techniques intelligentes utilisées pour l'analyse des données de mobilité dans la prédiction du flux de trafic dans les zones urbaines sont regroupées, de même, (ii) les résultats de la mise en œuvre desdites techniques sont présentés, en outre, (iii) les procédures effectuées sont décrites et analysées pour comprendre les avantages et les limites de ces techniques intelligentes. Compte tenu de ce qui précède, (iv) les ensembles de données utilisés dans la littérature et disponibles pour utilisation sont présentés, en outre, (v) les résultats quantifiables de précision des différentes techniques ont été comparés, mettant en évidence les avantages et les limites, ce qui nous permet (vi) d'identifier les défis connexes et, à partir de là, (vii) de proposer une taxonomie générale dans laquelle les connaissances acquises dans cette revue des flux de trafic convergent à partir d'une approche computationnelle. En las últimas décadas, el desarrollo de la infraestructura de transporte ha tenido un gran desarrollo, aunque los problemas de tráfico continúan extendiéndose debido al aumento debido al aumento de la población en las zonas urbanas que requieren el uso de estos medios de transporte. Esto ha llevado a un aumento de los problemas relacionados con el control de la congestión, lo que tiene un impacto directo en los ciudadanos: contaminación del aire, consumo de combustible, violación de las normas de tráfico, contaminación acústica, accidentes y pérdida de tiempo. En América Latina, el crecimiento desordenado de las ciudades aumenta las distancias y las rutas, así mismo, hay un aumento acelerado en el número de automóviles y motocicletas, lo que aumenta el problema. En este sentido, los sistemas de transporte inteligentes son una alternativa para mejorar el entorno del tráfico, incorporan el internet de las cosas y algoritmos inteligentes, para la recopilación de datos de múltiples fuentes y el procesamiento de la información, respectivamente, con el fin de mejorar la eficiencia del flujo de transporte. Sin embargo, el procesamiento y modelado de los datos de tráfico es un desafío debido a la complejidad de las redes de carreteras, las dependencias espacio-temporales entre ellas y los patrones de tráfico heterogéneos. En este estudio de revisión, (i) se agrupan las técnicas Smart utilizadas para el análisis de datos de movilidad en la predicción del flujo de tráfico en áreas urbanas, así mismo, (ii) se muestran los resultados de la implementación de dichas técnicas, además, (iii) se describen y analizan los procedimientos realizados para comprender los beneficios y limitaciones de estas técnicas Smart. Dado lo anterior, (iv) se muestran los conjuntos de datos utilizados en la literatura y disponibles para su uso, además, (v) se compararon los resultados cuantificables de precisión de las diversas técnicas, destacando ventajas y limitaciones, lo que nos permite (vi) identificar los desafíos relacionados y, a partir de ahí, (vii) proponer una taxonomía general en la que converjan los conocimientos adquiridos en esta revisión de flujo de tráfico desde un enfoque computacional. In recent decades, the development of transport infrastructure has had a great development, although traffic problems continue to spread due to increase due to the increase in the population in urban areas that require the use of these means of transport. This has led to increased problems related to congestion control, which has a direct impact on citizens: air pollution, fuel consumption, violation of traffic rules, noise pollution, accidents and loss of time. In Latin America, the disorderly growth of cities increases distances and routes, likewise, there is an accelerated increase in the number of cars and motorcycles, which increases the problem. In this sense, intelligent transport systems are an alternative to improve the traffic environment, they incorporate the internet of things and intelligent algorithms, for the collection of data from multiple sources and information processing, respectively, in order to improve the efficiency of the transport flow. However, the processing and modeling of traffic data is challenging due to the complexity of road networks, the space–time dependencies between them, and heterogeneous traffic patterns. In this review study, (i) the smart techniques used for the analysis of mobility data in the prediction of traffic flow in urban areas are grouped, likewise, (ii) the results of implementing said techniques are shown, in addition, (iii) The procedures performed are described and analyzed to understand the benefits and limitations of these smart techniques. Given the above, (iv) the data sets used in the literature and available for use are shown, in addition, (v) the quantifiable results of precision of the various techniques were compared, highlighting advantages and limitations, which allows us to (vi) identify the related challenges and, from there, (vii) propose a general taxonomy in which the knowledge acquired in this traffic flow review converges from a computational approach. في العقود الأخيرة، شهد تطوير البنية التحتية للنقل تطوراً كبيراً، على الرغم من استمرار انتشار مشاكل المرور بسبب الزيادة في عدد السكان في المناطق الحضرية التي تتطلب استخدام وسائل النقل هذه. وقد أدى ذلك إلى زيادة المشاكل المتعلقة بالسيطرة على الازدحام، والتي لها تأثير مباشر على المواطنين: تلوث الهواء، واستهلاك الوقود، وانتهاك قواعد المرور، والتلوث الضوضائي، والحوادث، وضياع الوقت. في أمريكا اللاتينية، يزيد النمو غير المنضبط للمدن من المسافات والطرق، وبالمثل، هناك زيادة متسارعة في عدد السيارات والدراجات النارية، مما يزيد من المشكلة. وبهذا المعنى، فإن أنظمة النقل الذكية هي بديل لتحسين بيئة حركة المرور، فهي تدمج إنترنت الأشياء والخوارزميات الذكية، لجمع البيانات من مصادر متعددة ومعالجة المعلومات، على التوالي، من أجل تحسين كفاءة تدفق النقل. ومع ذلك، فإن معالجة ونمذجة بيانات حركة المرور أمر صعب بسبب تعقيد شبكات الطرق، وتبعيات الزمكان بينها، وأنماط حركة المرور غير المتجانسة. في هذه الدراسة الاستعراضية، (1) يتم تجميع التقنيات الذكية المستخدمة لتحليل بيانات التنقل في التنبؤ بتدفق حركة المرور في المناطق الحضرية، وبالمثل، (2) يتم عرض نتائج تنفيذ التقنيات المذكورة، بالإضافة إلى ذلك، (3) يتم وصف الإجراءات المنفذة وتحليلها لفهم فوائد وقيود هذه التقنيات الذكية. بالنظر إلى ما سبق، (4) يتم عرض مجموعات البيانات المستخدمة في الأدبيات والمتاحة للاستخدام، بالإضافة إلى ذلك، (5) تمت مقارنة النتائج القابلة للقياس الكمي لدقة التقنيات المختلفة، مع تسليط الضوء على المزايا والقيود، مما يسمح لنا (6) بتحديد التحديات ذات الصلة، ومن هناك، (7) اقتراح تصنيف عام تتقارب فيه المعرفة المكتسبة في مراجعة تدفق حركة المرور هذه من نهج حسابي.

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao’s electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao’s power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line.

Despite its negative impacts, freight transportation is a primary component of all supply chains. Decision makers have considered diverse strategies, such as Horizontal Collaboration (HC) and the usage of alternative types of vehicles, to reduce overall cost and the related environmental and social impacts. This paper assesses the implementation of an electric fleet of vehicles in urban goods distribution under HC strategy between carriers. A biased randomisation based algorithm is used to solve the problem with a multi-objective function to explore the relationships between both delivery and environmental costs. Real data from the city of Bogota, Colombia are used to validate this approach. Experiments with different costs and demands projections are performed to analyse short- and medium-term impacts related to the usage of electric vehicles in collaborative networks. Results show that the optimal selection of vehicle types depends considerably on the time horizon evaluation and demand variation. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P and TRA2015-71883-REDT) and the Ibero-American Program for Science and Technology for Development (CYTED2014-515RT0489). Likewise, we want to acknowledge the support received by the Special Patrimonial Fund from Universidad de La Sabana (Colombia) and the doctoral grant from the UOC-Open University of Catalonia (Spain).

Electric vehicles (EVs) offer a great alternative for decarbonizing the transport sector. However, insufficient recharging infrastructure and limited range increase the driver’s ‘range anxiety’. Furthermore, the autonomy information provided by vehicle manufacturers differs from the range obtained under real-driving conditions. In order to estimate the actual range of an EV under different driving profiles, accurate computational modeling is required. This paper presents a library for modeling and simulating EVs using the object-oriented modeling language Modelica that allows calculating the energy consumption and the impact of different driving behaviors on the vehicle’s driving range. Each vehicle’s model only requires generic parameters that can be obtained from the vehicle’s manufacturer’s specification sheet. The parameters of the example models have been calibrated using vehicle parameters found in the literature for several commercial vehicles.