Abstract

[ES] La disponibilidad creciente de vehículos eléctricos en el mercado propone una nueva alternativa a propietarios de flotas que necesitan adquirir nuevos vehículos, y existe una necesidad de herramientas que apoyen la decisión de electrificar dichas flotas. En el contexto de la planificación de rutas de técnicos en un entorno urbano, muchos parámetros que tienen un impacto en el consumo de energía de los vehículos eléctricos cambian a diario. Por ello, se propone un modelo de simulación basado en agentes para generar diferentes escenarios que puedan proporcionar un enfoque más realista para el cálculo de los costes operacionales de varios grados de implementación de vehículos eléctricos, así como su huella medioambiental en términos de emisiones de CO2. El modelo de simulación propuesto integra una heurística ALNS para resolver el problema operacional de la planificación de rutas de técnicos. Además, el modelo de consumo de energía propuesto incluye un componente auxiliar debido a calefacción o aire acondicionado que no había sido considerado previamente en muchos estudios de optimización. Los resultados obtenidos en un caso proporcionado por una empresa demuestran que la introducción de electromovilidad en la logística urbana ofrece considerables beneficios económicos y medioambientales, aunque quizá el ahorro en costes operacionales no sea suficiente cuando es comparado con la gran diferencia en costes de adquisición. [EN] The growing availability of electric vehicles (EVs) in the market brings a new alternative to fleet managers that need to purchase new vehicles, and there is a need for tools to assess the decision of fleet electrification. In the context of the technician routing in an urban setting, many parameters that have an impact on the energy consumption of EVs change on a daily basis. Because of this, we propose an agent-based simulation model to generate different scenarios that can provide a more realistic approach to the computation of the operational costs of various degrees of EV implementation, as well as the environmental footprint of those fleets in terms of CO2 emissions. Our simulation model integrates an adaptive large neighborhood search heuristic to solve the operational technician routing problem. In addition, our energy consumption model includes an auxiliary component due to heating or air conditioning, which had not been included in many optimization studies. Our results on a case study show that the introduction of electro-mobility in urban logistics offers substantial economic and environmental benefits, although operational cost savings may not be enough when compared to the high difference in acquisition costs. Ramírez Martí, D. (2019). Análisis de la sostenibilidad de la electromovilidad para la planificación de rutas de técnicos de servicios. http://hdl.handle.net/10251/142245 TFGM

Abstract

[EN] In this thesis, an “Anxiety Model” is developed, by simulating EV driver´s charging decision under different degrees of anxiety, to give a more accurate prediction of where charging events are most likely to happen. [ES] En esta tesis se desarrolla un Modelo de Ansiedad, mediante la simulación de la decisión de carga del conductor del vehículo electrónico, EV. Se proponen diferentes grados de ansiedad, para hacer una predicción más exacta de donde se produjeron los eventos de carga con mayor probabilidad. Zhou, L. (2014). Modeling of ev (electric vehicles) charging station locations. http://hdl.handle.net/10251/59789 Archivo delegado

Abstract

A great deal of work on lightweight cost-effective strategies has been underway in the last decades to improve the electric vehicle efficiency and driving range. At the time, and according to recent EU project results, up to 30-40% weight reduction was achieved so far in the vehicle structure at prototype level, but further efforts need to be invested to push the technology up to the market. Besides, the adoption of circular economy principles across the entire life-cycle is needed to enable, on one hand, the integration of environmental and cost considerations at the early design stages and, on the other hand, new options for the end-of-life recovery, repair, reuse and recycling. 

ALMA project aims to apply eco-design principles to redefine the vehicle architecture and develop a new 26% lighter multi-material body structure using novel advanced high strength steel grades, high performance composites and advanced steel-hybrid laminates. Specifically, Designing for Assembly and Disassembly (DFA/DFD) and Design for Recycling (DFR) methodologies have been targeted to ensure cost-efficient separation, recycling and recovery at the end-of-life (EoL). The ALMA project has also considered aspects for the selection of the best material and production process for the right application at the design stage. The integration of regulatory considerations from early stages of design, especially forthcoming legislative requirements on emissions and the forthcoming ELV directive was also part of the design process.

As a result, the ALMA project intends to foster the adoption of a circular approach in the automotive sector, reinforced by the use of eco-design methodologies from early stages of the car conception and supported by LCA methodologies, helping the automotive industry to harmonize with circular economy principles: design to reduce the production of waste and pollution and extend life cycle for materials and products.

Abstract

In recent times, polymer matrix composite material technologies have undergone a genuine revolution, driven by the surge in demand—particularly in the transportation and energy sectors. Composites are being increasingly incorporated due to the significant weight reduction they enable in structural components, which in turn leads to a decrease in energy consumption in transportation as a result of this weight savings. Ambitious EU sustainability policies (such as the EU Green Deal or the New Industrial Strategy) are further accelerating this shift toward efficient and sustainable solutions, with a significant impact on the transportation sector, especially the automotive industry—both in conventional and emerging propulsion systems (the use of lightweight materials is expected to increase from the current 30% to 70% by 2030, with composites accounting for approximately 20%). However, the automotive sector is one of the most demanding in terms of processing times. Therefore, the optimization and monitoring of processing parameters through embedded sensors in molds is presented as a valuable tool for adapting processing times and enabling continuous quality control of parts. This paper presents the results obtained within the framework of the COMPCERTO project, which aims to develop solutions that facilitate the optimized manufacturing of components with structural requirements through Sheet Molding Compound (SMC) thermoforming processes. To achieve this, the implementation of sensors for monitoring curing, pressure, and temperature in molds is proposed, enabling the acquisition of representative values at critical points and in real time for the phenomena occurring during the thermoforming of parts in the press. Preliminary tests are carried out to optimize the curing cycle in order to determine the most suitable manufacturing parameters, and based on the results, a Design of Experiments (DoE) is proposed. Finally, mechanical, thermal, and microstructural characterizations are performed on the tests defined in the DoE. This will generate a database that serves to correlate the real-time data obtained from the parts with potential defects in the SMC components.