The thesis deals with the use of solar energy for electromobility. First, the potential of electromobility on the current market is theoretically discussed. This issue includes mainly the producers of electrically powered vehicles, the issue of electric energy storage and the real applications of fast charging and photovoltaic charging stations. The second part of the diploma thesis deals with the measurement of electric car consumption and the evaluation of measurement results. In the next part, electric cars are analyzed, which use solar energy for their function and their efficiency is compared with the effiency of combustion engines. The main part of this thesis is the design of photovoltaic charging station for electric vehicles. The final part deals with the economic evaluation of the proposed charging station.

QC 20111214

Des technologies avancées sont très demandées dans l'industrie automobile pour respecter les réglementations de consommation de carburant de plus en plus rigoureuses. La co-optimisation du dimensionnement et du contrôle des groupes motopropulseurs avec une efficacité de calcul améliorée est étudiée dans cette thèse.Les composants des groupes motopropulseurs, tels que le moteur, la batterie et le moteur électrique, sont modélisés analytiquement au niveau descriptif et prédictif afin de permettre une optimisation du contrôle rapide et une optimisation du dimensionnement scalable. La consommation d'énergie minimale des véhicules hybrides-électriques est évaluée par des nouvelles méthodes optimales. Ces méthodes – y compris Selective Hamiltonian Minimization et GRaphical-Analysis-Based energy Consumption Optimization – permettent d'évaluer une consommation minimale d'énergie avec une efficacité de calcul améliorée. De plus, la méthode de Fully-Analytic energy Consumption Evaluation (FACE) approxime la consommation d'énergie minimale sous forme analytique en fonction des caractéristiques de la mission et des paramètres de conception des composants du groupe motopropulseur. Plusieurs cas d’études sont présentées en détail par rapport aux approches de co-optimisation à bi-niveaux et à uni-niveau, ce qui montre une réduction efficace du temps de calcul requis par le processus global de co-optimisation. / Advanced technologies are highly demanded in automotive industry to meet the more and more stringent regulations of fuel consumption. Cooptimization of design and control for vehicle propulsion systems with an enhanced computational efficiency is investigated in this thesis.Powertrain components, such as internal combustion engines, batteries, and electric motor/generators, are analytically modeled at descriptive and predictive level correspondingly for the development of fastrunning control optimization and for the scalability of design optimization. The minimal fuel consumption of a hybrid-electric vehicle is evaluated through novel optimization methods. These methods – including the Selective Hamiltonian Minimization, and the GRaphical-Analysis-Based energy Consumption Optimization – are able to evaluate the minimal energy consumption with the enhanced computational efficiency. In addition, the Fully-Analytic energy Consumption Evaluation method approximates the minimal energy consumption in closed form as a function of the mission characteristics and the design parameters of powertrain components.A few case studies are presented in details via the bi-level and uni-level co-optimization approaches, showing an effective improvement in the computational efficiency for the overall co-optimization process.

This study investigates the underlying factors driving OKQ8’s electric vehicle charging sales in kWh and aims to understand their customers better by identifying the factors that impact sales in the electric vehicle charging market. The study focuses on transactions from 17 OKQ8 stations in Sweden and Denmark, from July 1st, 2022, to February 28th, 2023, categorized into five distinct subgroups based on similar attributes. The report uses a multiple linear regression model in the programming language R and macroeconomic principles to identify the variables that lead to additional sales of electric vehicle charging.  The final regression model includes the variables Sales, Station, Month, Weekday, and Time, with an Adjusted R2 of over 68% for all five groups. The study reveals that price does not have a significant impact on the sales of charging. Moreover, the analysis highlights the significance of variables related to time, month, day of the week, and individual charging stations, which demonstrate varying impacts on charging sales across different groups.  The study provides valuable insights into the influence of price and consumer behavior on electric vehicle charging sales, emphasizing the importance of adopting strategic approaches to encourage a wider adoption of electric vehicles, enhance charging infrastructure, and consider government support such as subsidies or tax incentives. The study has some limitations, including a lack of data for March to May and only covering a single year, limiting the ability to identify recurring patterns. Nonetheless, the study’s findings provide insights into the factors affecting OKQ8’s sales of electrical vehicle charging and ongoing research is necessary to validate and expand upon these findings, considering the constantly evolving electric vehicle market. / Denna studie syftar till att undersöka de underliggande faktorer som påverkar försäljningen av laddning av elektriska fordon i kWh hos OKQ8 och strävar vidare efter att få en djupare förståelse för deras kundgrupp. Studien bygger på data som sträcker sig från den 1a juli 2022 till den 28e februari 2023 och är i form av transaktioner från 17 olika OKQ8-stationer i Sverige och Danmark. Dessa 17 stationer delas sedan sedan in i fem olika undergrupper där stationerna i varje undergrupp erhåller liknande egenskaper. Rapporten använder sig av en multipel linjär regressionsmodell i programmeringsspråket R, och makroekonomiska principer för att identifiera variabler som leder till ökad försäljning av laddning av elektriska fordon. Den slutliga regressionsmodellen inkluderar variablerna Försäljning, Station, Månad, Veckodag och Tid, där Adjusted R2 har ett värde över 68% för samtliga fem grupper. Studien avslöjar att priset inte har en signifikant inverkan på försäljningen av laddning och belyser vidare betydelsen av variabler relaterade till tid, månad, veckodag och individuella laddningsstationer, vilka visar en varierande signifikans på försäljningen av laddning inom de olika grupperna. Studien ger värdefulla insikter om konsumentbeteende kopplat till försäljning av laddning för elektriska fordon. Den betonar också vikten av att vidta strategiska åtgärder för att främja en mer omfattande spridning av elektriska fordon, förbättra laddningsinfrastrukturen och överväga regeringsstöd såsom subventioner eller skattelättnader. Studien har vissa begränsningar att ta hänsyn till, däribland avsaknad av data för perioden mars till maj och att den endast täcker ett år. Dessa begränsningar påverkar möjligheten att identifiera eventuella återkommande mönster. Trots detta bidrar studiens resultat med värdefulla insikter om de faktorer som påverkar försäljningen av laddning för elektriska fordon som OKQ8 kan dra nytta av i sin verksamhet. På grund av marknadens ständiga utveckling är det dock nödvändigt med fortsatt analys för att validera och vidareutveckla resultaten.

The automotive and heavy-duty trucking industries are heading towards research and development of alternative powertrain solutions to meet the United Nations sustainability goals and cleaner solutions to aid climate change actions. This thesis project aligns with the vision of finding greener and sustainable modes of transport in the heavy long haulage trucking industry. This project aims to find and develop a method for creating drive cycles, getting the vehicular power requirements to drive on these selected routes and finally calculating the TCO of a vehicle. The scripts for these mentioned steps are developed in MATLAB. The approach used in this work could help both the vehicle manufacturer and the vehicle operator to predict or cater to upcoming customer demand on, in our case, routes pan EU, to receive information about energy, power and vehicular configuration needed to fulfil the mission, and also, optimize the powertrain configuration in collaboration with a parallel thesis work done here at Scania, and finally calculate a somewhat simplified TCO of the vehicle.  In this work, two different driving conditions has been used; summer or winter, and two different payload conditions, as well as two types of vehicle powertrains; FCEV and BEV. Finally, a comparison regarding TCO for FCEV and BEV has been done. / Fordonsindustrin, inklusive den kommersiella lastbilsindustrin, driver utvecklingen av alternativa drivlinor för att kunna uppfylla FN:s hållbarhetsmål kring miljövänligare lösningar, nödvändiga för att stödja det globala klimatarbetet. Detta examensarbete utgår från visionen att hitta miljövänligare fordonstyper inom den kommersiella lastbilssektorn. Detta projekt siktar på att utveckla och använda metoder för att kunna ta fram relevanta körcykler, fastställa nödvändig framdrivningseffekt för att fordonen ska kunna köra på utvalda rutter, samt att beräkna total ägandekostnad (TCO) för fordonsoperatören.  Skripten för dessa nämnda steg har utvecklats i MATLAB inom projektet. Tillvägagångssättet som har använts i detta arbete kan hjälpa både fordonstillverkare och fordonsoperatörer att förutspå framtida krav. I vårt fall har information om nödvändig energimängd, effekt och komponentkonfiguration, inklusive drivlineoptimering, tagits fram för rutter inom EU, tillsammans med ett parallellt examensarbete som också utförts på Scania. Slutligen beräknades den totala ägandekostnaden (TCO) för kunden.  I detta arbete har två olika användarfall analyserats; sommar och vinter, för två olika nyttolaster, samt två typer av drivlinor; FCEV och BEV. Slutligen, har en jämförelse gjorts gällande TCO för FCEV och BEV.

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

Global sustainability awareness and governmental regulations are pushing the automotive industry into finding alternatives to carbon dioxide emitting products. Solutions utilizing electricity in the vehicle powertrain is overtaking market share from internal combustion engines (ICE). This tendency has spread into the heavy-duty truck segment which poses questions regarding the future of the ICE. An alternative, electric motors, powered with batteries, fuel cells of even ICE’s, is thought to become a core part of future mobility. To mitigate discontinuities during a shift from ICE to electric motors, a study of possible factors affecting such transition has been performed. The result indicates 14 main factors which are thought to have a definite role in a major technology paradigm shift. These factors are: Supplier relations, Material management, Material availability, Available space, Scalability, Product flexibility, Risk management, External resource utilization, Internal relations, Demand estimation, Management endorsement, Appropriate methodology, Employee engagement, and Competence renewal. A structure using ISM methodology is established highlighting the factors’ influencing relation to each other. Anchored in the theory regarding paradigmatic shifts within industry, a tendency of technological, managerial, and institutional influence on organizational change can be discerned where the institutional level poses as the fundamental dimension of derived quality. The factors are identified from a Scania specific case but are broad enough to apply to similar situations facing challenges of a technological paradigm shift.

Cette thèse s'inscrit dans le cadre de la gestion d'énergie d'un Véhicule Hybride Électrique. Pour ce type de véhicule, l'optimisation énergétique est un enjeu majeur. Cela consiste à calculer les commandes optimales minimisant la consommation énergétique du véhicule sous un nombre fini de contraintes. Deux types de méthodes peuvent être utilisées pour résoudre ce problème d'optimisation. La première méthode et la plus utilisée, la méthode numérique, utilisant des modèles cartographiques basés sur des données. Elle présente deux inconvénients majeurs: temps de calcul et mémoire importants. La deuxième méthode, appelée analytique, qui permet de remédier à ces deux problèmes, a été utilisée dans cette thèse. Plus l'architecture du véhicule devient complexe (plusieurs machines électriques, moteur thermique, élévateur de tension), plus l'intérêt de cette approche sera important. La méthodologie analytique, proposée dans cette thèse, est composée principalement de trois étapes : la modélisation convexe, le calcul analytique des commandes et la validation des commandes analytiques sur un simulateur de véhicule. Cette méthodologie a été appliquée sur les trois configurations possibles du véhicule étudié : parallèle, bi-parallèle et série. Finalement, l'ajout de l'élévateur de tension dans la gestion d'énergie ainsi que l'étude de son impact sur la consommation énergétique du véhicule sont présentés dans le dernier chapitre. Les résultats obtenus en simulation montrent que la méthode analytique a permis de réduire considérablement le temps de calcul tout en ayant une sous-optimalité très faible. / This thesis focuses on the energy management of Hybrid Electric Vehicle. In this type of vehicle, energy optimization is a major challenge. It consists of calculating optimal commands that minimize the vehicle’s energy consumption under a finite number of constraints. The optimization issue could be solved using a digital method or an analytical method. This choice depends on the nature of energy models that monitor the optimization criteria: analytical or maps of experimental measurements. However, this method presents numerous disadvantages. Its calculation is extremely time-consuming for instance. Therefore, the works presented in this thesis were directed in order to develop an analytical solution where the calculation is lesstime consuming. The architecture of the vehicle is complex. In fact, the vehicle contains two electrical machines, a thermal engine and a step-up. These components have all a straight impact on the vehicle’s energy consumption so several optimization variables were defining. Consequently, working on an analytical solution was a natural choice. The proposed analytical methodology consists of three steps: convex modeling, the command analytical calculation as well as the analytical command validation on a vehicle simulator. This methodology was applied to three possible configurations of the studied vehicle: parallel, biparallel and in serial. Finally, the step-up addition to the energy management as well as the study of itsimpact on the vehicle’s energy consumption are presented in the last chapter. The simulation results show that the analytical method reduces considerably the computing time and has an extremely low suboptimality.

O principal objetivo dos veículos híbridos é diminuir o consumo de combustível em relação a veículos convencionais. Para isso, existe a necessidade de realizar a integração dos diferentes sistemas do trem-de-força e coordenar o seu funcionamento através de estratégias de controle. Tais estratégias são desenvolvidas e simuladas em conjunto com um modelo computacional da planta do veículo antes de serem aplicadas em uma unidade de controle eletrônica. O presente estudo tem como objetivo analisar o gerenciamento de energia em um veículo híbrido elétrico não-plugin do tipo série-paralelo visando à diminuição de consumo de combustível. O método de otimização global é utilizado para encontrar as variáveis de controle que resultam no mínimo consumo de combustível em um determinado ciclo de condução. Na primeira etapa, um modelo computacional da planta do veículo e da estratégia de controle não-ótima são criados. Os resultados obtidos da simulação são então comparados com dados experimentais do veículo operando em dinamômetro de chassis. A seguir, o método de otimização global é aplicado ao modelo computacional utilizando programação dinâmica e tendo como objetivo a minimização do consumo de combustível total ao final do ciclo. Os resultados mostram considerável redução do consumo de combustível utilizando otimização global e tendo como variável de controle não só a razão de distribuição de torque mas também os pontos de operação do motor de combustão. Os modelos computacionais criados nesse trabalho são disponibilizados e podem ser usados para o estudo de diferentes estratégias de controle para veículos híbridos. / The main goal of hybrid electric vehicles is to decrease engine emission and fuel consumption levels. In order to realize this, one must perform the powertrain system integration and coordinate its operation through supervisory control strategies. These control strategies are developed in a simulation environment containing the plant model of the powertrain before they can be implemented in a real-time control unit. The goal of this work is to analyze the energy management strategy which minimizes the fuel consumption in a series-parallel non-plugin hybrid electric vehicle. Global optimization is used for finding the control variables that result in the minimum fuel consumption for a specific driving cycle. In a first stage, a computational model of vehicle plant and non-optimal control strategy are created. The results from the simulation are compared against experimental data from chassis dynamometer tests. Next, a global optimization strategy is applied using dynamic programming in order to minimize total fuel consumption at the end of the driving cycle. The results from the optimization show a considerable fuel consumption reduction having as control variables not only the torque-split strategy but also the engine operating points. As contribution from this work, the computational models are made available and can be used for analyzing different control strategies for hybrid vehicles.

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases.