Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels. The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured. To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error. Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior. The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons. / Doctor of Philosophy / Even though the study of the longitudinal motion of vehicles spanned over several decades leading to the development of more precise and complex car-following models, an important aspect was constantly overlooked in those models. In fact, due to the complexity of modeling the human-in-the-loop, the vehicle and the driver were almost always assumed to represent a single entity. More specifically, ignoring driver behavior and integrating it to the vehicle allowed avoiding to deal with the challenges related to modeling human behavior. The difficulty of mathematically modeling the vehicle and the driver as two independent components rather than one unique system is due to two main reasons. First, there are numerous car models and types that make it difficult to determine the different parameters impacting the performance of the vehicle as they differ from vehicle to vehicle. Second, different driving patterns exist and the fact that they are mostly dependent on human behavior and psychology makes them very difficult to replicate mathematically. The research presented in this thesis provides a comprehensive investigation of the human-in-the-loop component in car-following theory leading to a better understanding of the human-vehicle interaction. This study was initiated due to the noticeable overlooking of driver behavior in the existing literature which, as a result, fails to capture the effect of human control and perception errors.

Battery electric vehicles (BEVs) could reduce CO2 emissions from the transport sector but their limited electric driving range diminishes their utility to users. The effect of the limited driving range can be reduced in multi-car households where users could choose between a BEV and a conventional car for long-distance travel. However, to what extent the driving patterns of different cars in a multi-car household’s suit the characteristics of a BEV needs further analysis. In this paper we analyse the probability of daily driving above a fixed threshold for conventional cars in current Swedish and German car driving data. We find second cars in multi-car households to require less adaptation and to be better suited for BEV adoption compared to first cars in multi-car households as well as to cars in single-car households. Specifically, the share of second cars that could fulfil all their driving is 20 percentage points higher compared to first cars and cars from single-car households. This result is stable against variation of driving range and of the tolerated number of days requiring adaptation. Furthermore, the range needed to cover all driving needs for about 70% of the vehicles is only 220 km for second cars compared to 390 km for the average car. We can further confirm that second cars have higher market viability from a total cost of ownership perspective. Here, the second cars achieve a 10 percentage points higher market share compared to first cars, and to cars in single-car households for Swedish economic conditions, while for Germany the corresponding figure is 2 percentage points. Our results are important for understanding the market viability of current and near-future BEVs.

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Cette thèse porte sur l’évaluation de plusieurs stratégies de gestion d’énergie pour un nouveau concept de moteur hybride pneumatique. Ce concept combine un moteur à combustion interne avec un système de stockage d’énergie sous forme d’air comprimée. Une soupape supplémentaire relie alors la chambre de combustion à un réservoir d’air et permet un fonctionnement en mode moteur pneumatique ou pompe pneumatique (récupératif). La première stratégie, Causale, est basée sur des principes heuristiques. La deuxième, à Coefficient de Pénalité Constant, vise la minimisation d’un critère énergétique global. Un coefficient de pondération permet de mettre en opposition, pour un travail donné, les coûts énergétiques d’un mode pneumatique d’une part et d’un mode thermique d’autre part. Le mode offrant le coût le plus faible sera choisi. La troisième stratégie, à Coefficient de Pénalité Variable, sur le même principe utilise un coefficient de pondération variable selon la quantité d’énergie pneumatique disponible. Une stratégie, à reconnaissance de situation de conduite, permet d’adapter les stratégies à la situation reconnue (par exemple, embouteillage, autoroutier). Enfin, la dernière stratégie tente de recopier la solution optimale de référence (obtenue par programmation dynamique) à l’aide d’un modèle. Toutes les stratégies ont été validées en simulation sur cycles standards. De plus une méthode, basée sur les chaînes de Markov, de constructions de cycle de conduite « artificiels » mais réalistes est proposée. Les consommations obtenues avec les différentes stratégies proposées sont comparées en référence aux consommations minimales atteignables. Les résultats montrent que 40% de gain de consommation peuvent être atteints. / This thesis presents a study of several energy management strategies for a novel hybrid pneumatic engine concept. The concept combines an internal combustion engine with a system of compressed air for energy storage. An additional charge valve connects the combustion chamber to an air pressure tank, enabling the engine to function in pneumatic motor mode or as a pneumatic pump (recuperation mode). The first strategy is called Causal and implements a rule-based control technique. The second one, called Constant Penalty Coefficient, is derived from optimal control theory and is based on an equivalent consumption minimization strategy. A penalty coefficient is introduced to evaluate, for a given torque demand, the respective energy costs of the two modes, pneumatic and conventional, enabling the mode offering the lowest cost to be chosen. The third strategy, called Variable Penalty Coefficient, is based on the same principle but uses a variable penalty coefficient depending on the amount of pneumatic energy available in the compressed air tank. Another strategy investigated, called Driving Pattern Recognition, adapts the strategies to the driving situation recognized (for example, traffic jam, or highway). The last strategy studied attempts to reproduce the optimal reference solution obtained by dynamic programming, using a neural mode. All the strategies have been validated by simulation on standard driving cycles. In addition, a method based on the Markov chain process have been develop to make ‘artificial’ yet realistic driving cycles. The consumptions obtained with the various strategies are compared with the minimal consumptions achievable. Results demonstrate that 40% of fuel saving can be achieved on certain cycles. Several of the strategies proposed give results that are close to optimal.

In early design phase for new hybrid electric vehicle (HEV) powertrains, simulation isused for the estimation of vehicle fuel consumption. For hybrid electric powertrains,fuel consumption is highly related to powertrain efficiency. While powertrainefficiency of hybrid electric powertrain is not a linear product of efficiencies ofcomponents, it has to be analysed as a sequence of energy conversions includingcomponent losses and energy interaction among components.This thesis is aimed at studying the energy losses and flows and present them in theform of Sankey diagram, later, an adaptive energy management system is developedbased on current rule-based control strategy. The first part involves developing energycalculation block in GT-SUITE corresponding to the vehicle model, calculating allthe energy losses and flows and presenting them in Sankey diagram. The secondpart involves optimizing energy management system control parameters according todifferent representative driving cycles. The third part involves developing adaptiveenergy management system by deploying optimal control parameter based on drivingpattern recognition with the help of SVM (support vector machine).In conclusion, a sturctured way to generate the Sankey diagram has been successfullygenerated and it turns out to be an effective tool to study HEV powertrain efficiencyand fuel economy. In addition, the combination of driving pattern recognition andoptimized control parameters also show a significant potential improvement in fuelconsumption. / Under den tidiga utvecklingsfasen av nya elektrifieradedrivlinor for hybridapplikationer (HEV) används simulering för uppskattning avfordonets bränsleförbrukning. För dess drivlinor är bränsleförbrukningen i hög gradkopplad till drivlinans verkningsgrad. Även om drivlinans verkningsgrad inte ären linjär prokukt av komponenternas verkningsgrad behöve rden analyseras somen sekvens av energiomvandlingar, inklusive förluster och energipåverkan mellankomponenter.Detta examensarbete syftar till att undersöka energiförluster och flöden samtpresentera dessa i form av sankey diagram. Senare utvecklas ett anpassningsbartenergihanteringssystem baserat på nuvarande regelbaserad kontrollstrategi. Deninledande delen involverar utvecklandet av energianalys i GT-SUITE som motsvararfordonsmodellen, beräkningar av totala energiförluster och flöden samt presentationav dessa i ett sankey diagram. Den andra delen innefattar optimering avenergihanteringssystems kontrollparametrar enligt olika representativa körcykler.Den tredje delen involverar utveckling av anpassningsbara energihanteringssystemgenom användning av optimala kontrollparameterar baserad på detektering avkörbeteende med hjälp av SVM ( stödvektormaskin).Slutligen, ett strukturerat sätt att generera sankey diagrammet har med framgånggenererats och visat sig vara ett effektivt verktyg för studier av HEV drivlinorseffektivitet och bränsleekonomi. Dessutom visar kombinationen av detektering avkörbeteende och optimerade kontrollparametrar på en markant potentiell förbättringi bränsleförbrukning.

La génération de chemins lisses pour les voitures intelligentes est l’une des conditions les plus importantes pour faire accepter et faciliter la navigation autonome de ces véhicules. Cette thèse propose plusieurs méthodes de génération de chemins lisses pour les véhicules non-holonomes qui permet une continuité intrinsèque de la courbure de navigation et offre par ailleurs une flexibilité accrue pour diverses conditions aux limites. Le chemin de courbure continue est construit en composant plusieurs clothoids, comprenant notamment des segments de lignes et/ou d’arcs, et où chaque clothoid est obtenue par une régulation appropriée de ses paramètres. À partir de ces propriétés, le chemin obtenu est nommé pCCP (parametric Continuous Curvature Path). Le pCCP fournit un diagramme de courbure qui facilite une commande en orientation du véhicule, ce qui permet d'obtenir une évolution lisse de sa trajectoire. Le problème du pCCP local est défini par des configurations initiales et finales (caractérisées pour chacune par une posture et un angle de braquage). Le problème a été étendu pour être aussi général que possible en incluant plusieurs cas. La génération locale de pCCPs, pour des cibles statiques, est spécifiquement décrite, les problèmes ont été divisés en trois problèmes et chaque problème a été décomposé par la suite en plusieurs sous-classes possibles. Pour avoir une flexibilité importante des pCCPs proposés, des cibles dynamiques ont été considérées, obtenant ainsi le dynamic-pCCP (d-pCCP). Un cadre simple mais efficace pour analyser l'état futur de l'évitement des obstacles est appliqué en configuration 4D (3D avec l’ajout d’un axe temporel) en mettant en exergue deux manoeuvres d’évitement possibles, car les évolutions avant et arrière sont appliquées et validées avec plusieurs exemples. Selon une méthodologie similaire pour atteindre les critères de performance liés à la génération des pCCPs, le h-CCP (pour human-pCCP) est proposé en utilisant des modèles expérimentaux comportementaux d’échantillons de conducteurs humains. À partir de quelques sous-expériences, le modèle de conduite humain pour l’évitement d’obstacles, les changements de voie et les mouvements en virage sont extraits et ces modèles ont été inclus pour créer ainsi le h-CCP (obtenu d’une manière similaire au pCCP mais avec différents critères d’optimisation) qui permet d’améliorer considérablement le confort des passagers. / Smooth path generation for car-like vehicles is one of the most important requisite to facilitate the broadcast use of autonomous navigation. This thesis proposes a smooth path generation method for nonholonomic vehicles which has inherently continuity of curvature and having important flexibility for various boundary conditions. The continuous curvature path is constructed by composing multiple clothoids including lines and/or arc segments, and where each clothoid is obtained by parameter regulation. From those properties the path is named pCCP (parametric Continuous Curvature Path) and provides curvature diagram which facilitates a smooth steering control for path following problem. Local pCCP problem is defined by initial and final tuple configurations (vehicles posture and steering angle). The problem is expanded to be as general as possible by including several cases. The local pCCP generation for steady target pose is specifically described, where the problem is divided into three problems and each problem is also decomposed into several sub-cases. To give more flexibility to the proposed pCCP, dynamic target is considered to obtain dynamic-pCCP (d-CCP). A simple but efficient framework to analyze the future status of obstacle avoidance is applied in 4D (3D with the addition of time axis) configuration and two avoidance maneuvers as front and rear avoidance are applied and validated with several examples. Under the similar methodology in performance criteria of pCCP generation, the human-CCP (h-CCP) is derived from experimental patterns of human driver samples. From several subexperiments, human driving pattern for obstacle avoidance, lane change and cornering motion are extracted and those pattern were included to make the h-CCP (which is obtained with similar way as pCCP but with different optimization criteria) to enhance considerably the passenger comfort.