Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 89-90). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 89-90). Also available on the Internet.

Thesis (Ph.D.)--Ohio State University, 2008. / Includes vita. Includes bibliographical references (p. 184-193). Also available online.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2006. / Dr. Jerome Meisel, Committee Member ; Dr. Bill Wepfer, Committee Member ; Dr. Samuel V. Shelton, Committee Chair.

Thesis (M.S.)--University of Toledo, 2009. / Typescript. "Submitted as partial fulfillment of the requirements for The Master of Science in Mechanical Engineering." "A thesis entitled"--at head of title. Bibliography: leaves 75-78.

Vehicle powertrain electrification, i.e. combining the internal combustion engine (ICE) with an electric motor (EM), is a potential way of meeting the increased demands for efficient and low emission transportation, at a price of increased powertrain complexity since more degrees of freedom (DoF) have been introduced. Optimal control is used in a series of studies of how to best exploit the additional DoFs. In a diesel-electric powertrain the absence of a secondary energy storage and mechanical connection between the ICE and the wheels means that all electricity used by the EMs needs to be produced simultaneously by the ICE, whose rotational speed is a DoF. This in combination with the relatively slow dynamics of the turbocharger in the ICE puts high requirements on good transient control. In optimal control studies, accurate models with good extrapolation properties are needed. For this aim two nonlinear physics based models are developed and made available that fulfill these requirements, these are also smooth in the region of interest, to enable gradient based optimization techniques. Using optimal control and one of the developed models, the turbocharger dynamics are shown to have a strong impact on how to control the powertrain and neglecting these can lead to erroneous estimates both in the response of the powertrain as well as how the powertrain should be controlled. Also the objective, whether time or fuel is to be minimized, influences the engine speed-torque path to be used, even though it is shown that the time optimal solution is almost fuel optimal. To increase the freedom of the powertrain control, a small energy storage can be added to assist in the transients. This is shown to be especially useful to decrease the response time of the powertrain, but the manner it is used, depends on the time horizon of the optimal control problem. The resulting optimal control solutions are for certain cases oscillatory when stationary controls would have been expected. This is shown to be neither an artifact of the discretization used nor a result of the modeling assumptions used. Instead it is for the formulated problems actually optimal to use periodic control in certain stationary operating points. Measurements show that the pumping torque is different depending on whether the controls are periodic or constant despite the same average value. Whether this is beneficial or not depends on the operating point and control frequency, but can be predicted using optimal periodic control theory. In hybrid electric vehicles (HEV) the size of the energy storage reduces the impact of poor transient control, since the battery can compensate for the slower dynamics of the ICE. For HEVs the problem instead is how and when to use the battery to ensure good fuel economy. An adaptive map-based equivalent consumption minimization strategy controller using battery state of charge for feedback control is designed and tested in a real vehicle with good results, even when the controller is started with poor initial values. In a plug-in HEV (PHEV) the battery is even larger, enabling all-electric drive, making it it desirable to use the energy in the battery during the driving mission. A controller is designed and implemented for a PHEV Benchmark and is shown to perform well even for unknown driving cycles, requiring a minimum of future knowledge. / Elektrifiering av drivlinan i fordon är ett sätt att möta kraven på transporter med hög effektivitet och låga utsläpp. Att byta ut förbränningsmotorn mot en elmotor kan ge vinningar avseende effektivitet, prestanda och utsläpp, men till en kostnad av lägre mobilitet på grund av eletriska energilagers relativt låga energitäthet i jämförelse med fossila bränslen. Att istället komplettera förbränningsmotorn med en elmotor erbjuder möjligheten att kombinera de två systemens fördelar och samtidigt undvika nackdelarna. Att använda mer än en motor i drivlinan ökar komplexiteten eftersom fler frihetsgrader har introducerats. Detta ställer ökade krav på utformningen av reglersystemet för att få ut det mesta av potentialen i drivlinan. I optimal styrning använder man matematiska modeller och optimeringsalgoritmer för att beräkna hur man bäst styr det modellerade systemet. Storleken på det elektriska energilagret påverkar dock valet av optimal styrnings-metod samt vilken detaljnivå på modellerna som behövs. I avhandlingen används optimal styrning i en serie studier av hur man bäst utnyttjar de extra frihetsgraderna som elektrifieringen har introducerat. I en diesel-elektrisk drivlina finns det ingen mekanisk koppling mellan motorn och hjulen, likt en växellåda i ett vanligt fordon, vilket gör att dieselmotorns varvtal är en frihetsgrad som måste styras. Avsaknaden av elektriskt energilager leder också till att all elektrisk energi till elmotorn måste produceras av förbränningsmotorn exakt då den behövs. Dessa två egenskaper, i kombination med den långsamma dynamiken hos turboaggregatet, ställer detta höga krav på god transientreglering. För att studera optimal styrning krävs bra modeller med goda extrapoleringsegenskaper. Med avseende på detta utvecklas två fysik-baserade modeller som uppfyller dessa krav och dessutom är tillräckligt glatta i det relevanta arbetsområdet för att möjliggöra gradient-baserade optimeringstekniker. Med optimal styrning och en av de utvecklade modellerna visas turbons dynamik ha stor påverkan på hur drivlinan bör styras. Att försumma turbodynamiken kan leda till felaktiga uppskattningar, både av drivlinans responstid, men även hur den bör styras. Kriteriet, det vill säga om bränsle eller tidsåtgången minimeras, påverkar också vilken motorvarvtal-motormoment-väg som är optimal, även om det visas att den tidsoptimala lösningen är nästan bränsleoptimal. För att ytterligare öka frihetsgraden i drivlinan kan ett elektriskt energilager användas för att assistera i transienterna. Detta visar sig vara särskilt användbart för att minska responstiden hos drivlinan, men hur det ska använda beror på tidshorisonten på optimeringsproblemet De resulterande optimala styrsignalerna är i vissa fall oscillerande där konstanta styrsignaler förväntas. Detta visas vara vare sig en effekt av den använda diskretiseringen eller modelleringsvalen som är gjorda. Istället är det för de lösta problemen faktiskt optimalt att använda periodiska styrsignaler för vissa stationära arbetspunkter. I experiment visas att pumparbetet skiljer sig beroende på om periodiska eller konstanta styrsignaler används, även om medelvärdet är detsamma. Huruvida detta ökar effektiviteten eller inte beror på arbetspunkt och periodtid. För hybridelektriska fordon (HEV) så minskar batteriets storlek effekten av dålig transientreglering då batteriet kan användas för att kompensera för den långsamma förbränningsmotordynamiken. Istället blir problemet i huvudsak hur mycket och när batteriet ska användas för att få god bränsleekonomi. En adaptiv mapp-baserad ekvivalentförbruknings-minimerande styrlag (ECMS) med återkopplad reglering baserad på batteriets laddningsnivå, utvecklas och testas i riktigt fordon med gott resultat, även vid dålig initialisering av regulatorn. För plug-in hybrider (PHEV) är batteriet större och kan dessutom laddas från elnätet, vilket medför möjlighet till rent elektrisk drift och att det är önskvärt att använda energin i batteriet under köruppdraget. För att minska energiåtgången är det däremot ofta lönsamt att blanda energin från bränsle och batteriet kontinuerligt under köruppdraget och se till att batteriet töms lagom till slutet av köruppdraget. För att åstadkomma detta måste då även urladdningstakten bestämmas. En regulator utvecklas för att minimera energiåtgången för en PHEV, det vill säga som försöker använda lagom av batteriet så det ska räcka hela vägen, men inte längre. Denna regulator implementeras för ett referensproblem, med gott resultat även för okända körcykler, trots ett minimum av framtidskunskap.

<p>The lithium ion battery is today mainly used in cell phonesand laptops. In the future, this kind of battery might beuseful in hybrid electric vehicles as well.</p><p>In this work, the main focus has been to gain more knowledgeabout the lithium ion battery in the hybrid electric vehicle(HEV) and more precisely to examine what processes of thebattery that are limiting at HEV currents. Both experiments andmathematical modelling have been used. In both cases, highrate, pulsed currents typical for the HEV, have been used.</p><p>Two manuscripts have been written. Both of them concern thebehaviour of the battery at HEV load, but from different pointsof view. The first one concerns the electrochemical behaviourof the battery at different ambient temperatures. Theexperimental results of this paper were used to validate amathematical model of a Li-ion battery. Possiblesimplifications of the model were identified. In this work itwas also concluded that the mass transfer of the electrolyte isthe main limiting process within the battery. The mass transferof the electrolyte was further studied in the second paper,where the concentration of lithium ions was measured indirectlyusing in situ Raman spectroscopy. This study showed that themathematical description of the mass transfer of theelectrolyte is not complete. One main reason of this issuggested to be the poor description of the physical parametersof the electrolyte. These ought to be further studied in orderto get a better fit between concentration gradients predictedby experiments and model respectively.</p>

No description available.

A research report submitted to the Faculty of Humanities University of the Witwatersrand In partial fulfilment of the requirements for the degree of Masters of Arts in Organisational Psychology 2016 / Recent environmental awareness has led to an expanding interest surrounding environmental consciousness and a greater social shift world over towards energy efficiency and the sustainability of technologies and resources. Consequently, there has been the development of sustainable technologies within the automobile industry including that of hybrid electric vehicles (HEVs). With the development of these technologies, it becomes necessary to investigate the factors that underpin the use and adoption of them within our society, so as to ensure their greater diffusion, use and adoption. In this light, this study aimed to investigate the factors that function in predicting the Intention to Adopt the sustainable technology of HEVs. This has been investigated in accordance with the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This model comprises the constructs of: Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. This study also intended to examine these constructs and determine whether they are moderated by the constructs of Pro-Environmental Behaviour and Dispositional Resistance to Change in predicting the Intention to Adopt HEVs. The sample for this study was comprised of 133 final year Law students from the University of the Witwatersrand. The adapted UTAUT Scale, the adapted Dispositional Resistance to Change Scale and the Pro-Environmental Scale were utilised as the measures within this study. Several subscales of the UTAUT Scale as well as the Pro-Environmental Behaviour (PEB) Scale had low Internal Consistency Reliabilities within both the Pilot and Main study. However, the researcher chose to run the analyses taking this into consideration. Several subscales of the UTAUT Scale as well as the Dispositional Resistance to Change (DRC) Scale had acceptable levels of Internal Consistency Reliabilities for use in conducting analyses. Multiple regression equations and moderated multiple regression equations were run in order to investigate the effects of these constructs in predicting the Intention to Adopt HEVs. The results drawn from this study illustrated that there was a positive, significant effect of two questions concerning lifestyle factors and a reduced taxed levy of the construct Facilitating Conditions on Intention to Adopt HEVs. The results also showed that the constructs of PEB and DRC had no direct moderating influence on Intention to Adopt HEVs. / MT2017

Although there are different HEV configurations, they are all based on same kinds of components. After introducing the main components HEVs use, we build up a model which can illustrate the basic idea of HEVs. The analysis of the model helps us to reveal the essential problem of HEV power control. The performance of a HEV depends not only on the individual components but also on how the components are coordinated. The power control system must determine operating points for the components during driving to save energy. The proposed power control approach is based on model predictive control and trying to solve the nature problem of HEV power control by an optimization concept, which makes the approach applicable for all kinds of HEVs. A number of different simulations have been executed to prove the feasibility of the approach. By changing some operational weights, the power control system can achieve different performances. / Another key concept adopted in the power control system is based on the premise that future driving load would affect fuel consumption, as well as the operating modes of the vehicle and the driver behavior do. The proposed power control approach incorporates a driving load forecasting algorithm whose role is to assess the driving environment, the driving style of the driver, and the trend of the vehicle using long and short term statistical features of the past drive cycle. This future driving load information is subsequently used to change the operational weights of the power control approach, such as engine efficiency, battery State of Charge (SOC), engine speed, etc. By this way, the power control approach leads to improved the vehicle's overall performance. / One of the major crises that the world is facing today is the problems of energy. With the beneficial effect on the environment and high energy transformation efficiency in hybrid electric vehicle technology, automobile manufacturers have begun to look more seriously into vehicles with alternative power sources. Aimed at solving the more and more serious problems of energy, HEV has been one of the best practical applications for transportation with high fuel economy. / This dissertation proposes a new power control approach for all kinds of hybrid electric vehicles (HEVs). / To obtain better performance, we use particle swarm optimization (PSO) to find optimal weights for different drive loads. Then, by integrating MPC controller and load forecasting algorithm, a realtime HEV power control system, model predictive power control with load forecasting system (MPC-LF), is developed. Experimental results prove the feasibility of the control system. / Wang, Zhancheng. / Adviser: Xu Yangsheng. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3631. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 132-140). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.