The transport sector is currently facing challenges to reduce environmental impacts during the vehicle’s operation due to its reliance on fossil fuels. The introduction of new technologies such as alternative fuels or battery electric vehicles (BEVs) are therefore rapidly growing because they can significantly reduce the vehicle’s tailpipe emissions. There is however the concern that these could transfer environmental burdens to other life cycle phases such as production. Therefore, a development towards sustainable transport will require more than just the development of alternative fuels or EVs, but also a more sustainable production. Considering that 80% of the product related environmental impacts are determined during the design phase of a product, the significance of product design is studied. Scania offers the opportunity to customize trucks with a high level of detail through customized design, also called S-order design. Design engineers want to know if their customized solutions have the potential to reduce environmental impacts within the production of a truck. Therefore, the life cycle assessment (LCA) framework is used to know the environmental impacts of a truck designed with S- and A-order design and to compare them in order to determine if there is an environmental performance difference between these two designs. The results show that the production of a truck with a S-order design has on average 3% lower environmental impacts on all impact categories than when it’s produced with an A-order design. This is due to the S-order design’s great level of flexibility to consider small details of the truck’s functionality. Nevertheless, this design flexibility can lead to multiple configurations for one truck, thus meaning that the results will vary from product to product since the customer decides the specifications of the truck. The main conclusion is that the early implementation of adaptations through S-order design in heavy truck development at Scania can potentially reduce resource consumption and environmental impacts, and aid to sustainable production. / Transportsektorn står för närvarande inför utmaningar för att minska miljöpåverkan under fordonets drift på grund av dess beroende av fossila bränslen. Introduktionen av ny teknik som alternativa bränslen eller elektriska fordon (BEV) växer därför snabbt eftersom de avsevärt kan minska fordonets utsläpp från avgasröret. Det finns emellertid oro för att dessa skulle kunna överföra miljöbelastningar till andra livscykelfaser som exempelvis produktionen. Därför kommer en utveckling mot hållbara transporter att kräva mer än bara utveckling av alternativa bränslen eller eldrift, men också en mer hållbar produktion. Med tanke på att 80% av de produktrelaterade miljöeffekterna bestäms under en produkts designfas studeras därför produktens design. Scania erbjuder möjligheten att skräddarsy lastbilar med hög detaljnivå genom skräddarsydd design, även kallat S-orderdesign. Designingenjörer vill veta om deras skräddarsydda lösningar har potential att minska miljöpåverkan inom tillverkningen av en lastbil. En livscykelanalys (LCA) används därför för att känna till miljöpåverkan från en lastbil konstruerad med S- och A-orderdesign och för att jämföra dem för att avgöra om det finns en skillnad i miljöprestanda mellan dessa två konstruktioner. Resultaten visar att tillverkningen av en lastbil med S-orderdesign har i genomsnitt 3% lägre miljöpåverkan på alla kategorier av miljöpåverkan än en med A-orderdesign. Detta beror på S- orderdesignens stora flexibilitet för att ta hänsyn till små detaljer gällande lastbilens funktionalitet. Dock kan denna konstruktionsflexibilitet leda till flera konfigurationer för en lastbil, vilket innebär att resultaten kommer att variera från produkt till produkt eftersom kunden bestämmer lastbilens specifikationer. Huvudslutsatsen är att det tidiga genomförandet av anpassningar genom S- orderdesign vid utvecklingen av tunga lastbilar hos Scania potentiellt kan minska resursförbrukningen och miljöpåverkan och stöd till hållbar produktion.

Depuis plusieurs années les chiffres de l'insécurité routière montrent la nécessité de développer un outil technologique qui vise à connaitre le comportement de véhicules deux roues motorisés (2RM) afin d'améliorer leursécurité. De cette problématique découle nos travaux de recherche qui s'inscrivent dans le cadre de projet METRAMOTO (MEsure du TRAfic des deux-roues MOTOrisés pour la sécurité routière et l'évaluation des risques)L’objectif principal visé concerne la modélisation, le dimensionnement et l'optimisation d'un capteur hybride constitué d'une part de boucles électromagnétiques et d'autre part de capteurs de chocs piézo-électriques. L’idée consiste à utiliser les boucles électromagnétiques pour discriminer la présence des 2RM de celle des autresvéhicules, associées à des segments piézo-électriques pour détecter le choc produit par le passage des roues desdifférents véhicules, le tout associé à un algorithme de gestion et de traitement de données. Aujourd'hui redimensionnement des systèmes de détection à base de boucles électromagnétiques ou de câble piézo-électriquese fait de manière expérimentale sur des sites contrôlés en utilisant plusieurs types de véhicules. Cette technique gourmande en temps nécessite une série importante d'essais couteux et dangereux. La présente étude a pour butde modéliser les différentes interactions entre le capteur hybride étudié et les véhicules en vue de son dimensionnement. Des modèles électromagnétiques et électromécaniques sont développés pour décrire les différentes interactions se produisant entre les boucles électromagnétiques d'une part, les boucles électromagnétiques et les véhicules d'autre part ainsi que celles des roues de véhicules et le câble piézoélectrique.Sur la base de ces modèles établis, une démarche générale est élaborée conduisant au dimensionnement du capteur hybride pour une route donnée. Nous nous sommes également intéressés au traitement des données issues du capteur hybride pour lesquelles nous proposons une approche d'identification des différentes catégories de véhicules dont les 2RM. Deux configurations de capteurs pour deux routes différentes sont alors proposées.Elles sont associées à des algorithmes de traitement de données permettant l'acquisition des signaux de boucles et de câbles, la distinction des différents types de véhicules, l'estimation de la position des véhicules sur la chaussée ainsi que la mesure de la vitesse de véhicules. / For several years the road safety numbers show the need to develop a technological tool that aims to know thepowered two wheelers vehicles behavior (2PW) to improve their security. From this problematic arise our researchworks that come under the project METRAMOTO (Powered two wheelers traffic measurement for road safety andrisks assessment). The main objective is modeling, sizing and optimization of an hybrid sensor consisted ofelectromagnetic loops and piezoelectric shock sensors. The idea is to use electromagnetic loops to discriminate the2PW presence from the other vehicles, combined with piezoelectric segments to detect the shocks produced by thevehicles wheels passage, all associated with a management algorithm and data processing. Today the sizing of thedetection systems based on electromagnetic loops or piezoelectric cables is done experimentally on controlled sitesusing several vehicles types. This technique requires significant test series which are costly and dangerous. Thisstudy aims to modeling the different interactions between the studied sensor and vehicles in order to its sizing.Electromagnetic and electromechanical models are developed to describe the different interactions that take placebetween electromagnetic loops on the one hand, between electromagnetic loops and vehicles on the other handand those between vehicle wheels and piezoelectric cable. On the basis of established models, a general approachis elaborated driving to sizing the hybrid sensor for any given road. We are also interested to the data processingcoming from the hybrid sensor for which we propose an approach to identifying the different vehicle categoriesincluding the 2PW. Two sensor configurations for two different roads are then proposed. They are associated atdata processing algorithms allowing the acquisition of loops and cables signals, the distinction between the differentvehicles classes, the estimation of the vehicles positions on the road and the measuring of their speed.

<p>  </p> <p>The trucking industry seems to be resistant to electrification, even though truck electrification can lead to large societal as well as user benefits. This dissertation develops a framework to inform policy making and enhance electric vehicle (EV) preparedness in the trucking industry through the study of two interrelated elements: (a) the adoption of electric trucks and (b) the appropriate implementation of electric truck technology. These two elements cover the user perspective, which is not adequately studied, and the agency perspective, which is pivotal in the decision-making process. Specifically, this study addressed the following research questions: (i) which factors affect the purchase decisions of truck fleet managers or owners for electric trucks? (ii) what is the ranking of and interrelationships between the barriers to the adoption of electric trucks? (iii) which location criteria should be considered for the strategic implementation of dynamic wireless charging (DWC) in a freight transportation network and where should this technology be located based on these criteria, and (iv) what is the impact of electric truck adoption on highway revenue and potential of alternative funding mechanisms to recover the revenue loss?</p> <p>For the adoption of electric trucks, a stated preference survey was designed and distributed online to truck fleet managers/owners in the U.S., gathering 200 completed responses. Statistical and multi-criteria decision-making approaches were employed to identify the factors that affect the purchase intentions of truck fleet managers and explore the barriers to electric truck adoption. The results showed that the purchase intentions of truck fleet managers are affected by trucking firm and truck fleet characteristics, behavioral factors/opinions regarding electric trucks, and awareness of innovative charging technologies. Furthermore, electric truck adoption would be accelerated if stakeholders focused on the barriers related to the business model, product availability, and charging time. Additionally, electric truck adopters and non-adopters may not be viewed as one homogenous group, since differences were found in the ranking and interrelationships of barriers to electric truck adoption between these two groups. </p> <p>The implementation of electric truck technology was examined based on the truck fleet managers’ survey, secondary data sources and the case of Indiana, U.S. A multi-criteria decision-making spatial approach was proposed to identify the candidate locations for the deployment of DWC. It was concluded that the most suitable locations for DWC lanes were on interstates, near airports and ports and away from EV charging stations. A data-driven framework was also developed to quantify the impact of electric truck adoption and estimate the optimal fee for each truck to recover the revenue loss. Using the market penetration levels estimated based on the survey data collected, the average annual fuel tax revenue loss for Indiana was approximately $349M. To maintain the same tax revenue per vehicle, annual fees ranging from $969 (in 2021) to $1,243 (in 2035) for single-unit trucks and $6,192 to $7,321 for combination trucks would be needed. To address public relations problems of EV fee implementation, this study also discussed alternative mitigation measures: a vehicle-miles-traveled fee and a pay-as-you-charge fee.</p> <p>In summary, this dissertation contributes to the body of literature by providing significant insights regarding the perspectives of truck fleet managers for electric trucks as well as a comprehensive list of all the location criteria for DWC. The proposed frameworks and study findings can be used by policymakers and other major stakeholders of the EV ecosystem to frame certain strategies to accelerate electric truck adoption, identify the most suitable locations for charging infrastructure, better understand the impact of electric trucks on the highway revenue, and provide the groundwork for developing EV roadmaps.</p>