Inlet port flow characteristics are critical in determining the overall performance of diesel combustion systems. The relationship between inlet port geometry and performance has long been a subject of interest to many researchers, although as yet a comprehensive understanding remains elusive. The ongoing need to provide advanced powertrain design solutions in order to meet increasingly stringent emissions legislation, whilst meeting customer expectations and minimising engineering costs, has driven the development of new approaches to engine design. In particular, the fundamental advantages of multivalve technology, coupled with rapidly improving fuel delivery systems has placed new requirements on inlet port performance characteristics. Statistical methods and knowledge-based design are emerging as potentially powerful tools in this field of research, supported by rapid developments in computing power.

The previously introduced fuel droplet heating and evaporation models, taking into account temperature gradients, recirculations, and species diffusion within droplets, are further developed and generalised for the application to a broad range of automotive fuel droplets. The research has been conducted in three directions: modelling of biodiesel fuel droplets, modelling of Diesel fuel droplets, and modelling of gasoline fuel droplets.

<p> For automated driving vehicles to be accepted by their users and safely integrate with traffic involving human drivers, they need to act and behave like human drivers. This not only involves understanding how the human driver or occupant in the automated vehicle expects their vehicle to operate, but also involves how other road users perceive the automated vehicle&rsquo;s intentions. This research aimed at learning how drivers space themselves while driving around other vehicles. It is shown that an optimized lane change maneuver does create a solution that is much different than what a human would do. There is a need to learn complex driving preferences from studying human drivers. </p><p> This research fills the gap in terms of learning human driving styles by providing an example of learned behavior (vehicle spacing) and the needed framework for encapsulating the learned data. A complete framework from problem formulation to data gathering and learning from human driving data was formulated as part of this research. On-road vehicle data were gathered while a human driver drove a vehicle. The driver was asked to make lane changes for stationary vehicles in his path with various road curvature conditions and speeds. The gathered data, as well as Learning from Demonstration techniques, were used in formulating the spacing profile as a lane change maneuver. A concise feature set from captured data was identified to strongly represent a driver&rsquo;s spacing profile and a model was developed. The learned model represented the driver&rsquo;s spacing profile from stationary vehicles within acceptable statistical tolerance. This work provides a methodology for many other scenarios from which human-like driving style and related parameters can be learned and applied to automated vehicles</p><p>

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This research presents the development of a comprehensive and unified solution procedure for solving general problems in mechanism synthesis. The uniqueness of this approach lies in its ability to derive the exact gradient relationships for the purposes of finding optimal solutions to generic mechanism problems. In this approach, mechanism problems are modeled using a generalized matrix-based method and the exact gradients necessary for optimization are systematically generated for any type of mechanism. The availability of these exact gradients eliminates the use of highly time consuming finite difference techniques and the problems associated with gradient approximation. From the application point of view, the use of exact gradients significantly enhances the efficiency of optimization process, improves the accuracy of final solutions, and avoids premature termination of optimization algorithms due to bad gradient approximation. One of the main problems in the synthesis of mechanisms is the occurrence of circuit defects. They occur when the desired orientations of a linkage assembly fall in different mobility regions during its cycle of motion. In this research, a sub-Jacobian based approach has been developed and integrated into the mechanism synthesis process to guarantee circuit free optimal solutions. The systematic development of exact gradients for various design objectives, its integration in the mechanism synthesis process, and the ability of this approach to eliminate circuit defects offer a unified as well as comprehensive approach for general mechanism synthesis. Several examples are presented to illustrate the effectiveness of this approach for designing industrial mechanisms. The generality and efficiency of exact gradient methodology make it the most viable approach for mechanism synthesis. The potential application of this approach to other domains of engineering design with an example from structural shape optimization is presented.

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O presente trabalho destina-se a resumir o processo de desenvolvimento de automóveis, mostrando os vários itens que influenciam na velocidade deste desenvolvimento, e propor uma base de raciocínio para se diminuir o tempo total para o projeto. / The present volume is destined to summarize the development process of automobiles, showing the many items responsible for speed up this development, and make a proposal for a thinking basis to shorten development time.

The gradual decline in global oil reserves and the presence of ever so stringent emissions rules around the world have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing a real-time HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle. Reviewed literature indicates some research gaps and hence exploitable study areas for which this thesis intends to address. For example, despite the research advances made, HEV energy management is still lacking in several key areas: optimisation of braking energy regeneration; real-time sub-optimal control of HEV for robustness, charge sustenance and fuel reduction; and real-time vehicle speed control. Consequently, this thesis aims to primarily develop novel real-time near-optimal control strategies for a parallel HEV, with a view to achieving robustness, fuel savings and charge sustenance simultaneously, under various levels of obtainable driving information (no route preview information, partial route preview information). Using a validated HEV dynamic simulation model, the following novel formulations are proposed in this thesis and subsequently evaluated in real time: 1. A simple grouping system useful for classifying standard and real-world driving cycles on the basis of aggressivity and road type. 2. A simple and effective near-optimal heuristic control strategy with no access to route preview information. 3. A dynamic programming-inspired real-time near-optimal control strategy with no access to route preview information. 4. An ECMS (Equivalent Consumption Minimisation Strategy) inspired real-time near-optimal control strategy with no access to route preview information. 5. An ECMS-inspired real-time near-optimal control strategy with partial access to route preview information. 6. A dynamic programming based route-optimal vehicle speed control strategy which accounts for real-time dynamic effects like engine braking, while solving an optimisation problem involving the maximisation of fuel savings with little or no penalty to trip time. 7. A real-time vehicle speed control approach, which is based on smoothing the speed trajectory of the lead vehicle, consequently reducing the acceleration and deceleration events that the intelligent vehicle (follower vehicle) will undergo. This smoothing effect translates into reduced fuel consumption, which tends to increase with increasing traffic preview window. Among other studies performed in this thesis, the fuel savings potential of the proposed near-optimal controllers was investigated in real time over standard driving cycles and real-world driving profiles. Results from these analyses show that, over standard driving cycles, properly formulated near-optimal real-time controllers are able to achieve a fuel savings potential within 0.03% to 3.71% of the global optimal performance, without requiring any access to route preview information. It was also shown that as much as 2.44% extra fuel savings could be achieved over a driving route, through the incorporation of route preview information into a real-time controller. Investigations were also made into the real-time fuel savings that could be realised over a driving route, through vehicle speed control. Results from these analyses show that, compared to an HEV technology which comes at a bigger cost, far higher fuel savings, as much as 45.96%, could be achieved through a simple real-time vehicle speed control approach.

<p>Purdue University was selected as one of 15 universities to participate in a three year Advanced Vehicle Technology Competition (AVTC) called EcoCar2: Plugging Into the Future. The vehicle built by the Purdue team was a Parallel-Through-The-Road Plug-in Hybrid Electric Vehicle (PTTR PHEV). The vehicle utilized a B20 diesel powertrain to power the front wheels, as well as a custom electric drivetrain to power the rear wheels. Using this vehicle during the final year of the competition, the team was successful in placing 4th overall as well as 2nd in the category of Well-To-Wheel (WTW) Greenhouse Gas Emissions. A stock 2013 Chevrolet Malibu was given to all teams in the competition to use as a base vehicle. The Purdue team removed the stock 2.4L gasoline engine of the Malibu in order to make room for the diesel powertrain and switched the stock Malibu rear suspension assembly to that of a 2013 All-Wheel-Drive (AWD) Buick LaCrosse in order to make room for the electric drivetrain. The electric drivetrain utilized a 16.4 kWhr Lithium Ion battery pack, a 103 kW (peak) 45 kW (nominal) electric motor, and the driveline components of a 2013 AWD Buick LaCrosse in order to transfer power to the wheels. Significantcant challenges concerning the custom electric drivetrain during the competition included the design, fabrication, installation and operation of a rear suspension cradle, Energy Storage System (ESS) and a Thermal Management System for the ESS. Computer Aided Drawing (CAD) and Finite Element Analysis (FEA) were used heavily during the design stages of vehicle development in order to give the Purdue team and AVTC competition organizers sufficient confidence to allow the team to build the designs they had proposed. This work describes the design, analysis and fabrication procedures used by the Purdue team in order to create the electric drivetrain used in their vehicle for the EcoCar2 competition.