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Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Significant improvement in fuel consumption, torque delivery and emission could be achieved through flexible control of the valve timings, duration and lift. In most existing electro-hydraulic variable valve actuation systems, the desired valve lift within every engine cycle is achieved by accurately controlling of the solenoid-valve opening interval; however, due to slow response time, precision control of these valves is difficult particularly during higher engine speeds. In this paper a new lift control strategy is proposed based on the hydraulic supply pressure and flow control. In this method, in order to control the peak valve lift, the hydraulic pump speed is precisely controlled using a two-input gearbox mechanism. This eliminates the need for precision control of the solenoid valves opening interval within every cycle.

This paper describes the impact of noise on the civil aircraft design process. The challenge to design ‘silent’ aircraft is the development of efficient airframe-engine technologies, for which integration is essential to produce an optimum aircraft, otherwise penalties such as higher fuel consumption, and, or noise are a concern. A description of work completed by Cranfield University will cover design methodologies used for a Broad delta airframe concept, with reference to future studies into alternate concepts. Engine cycle designs for ultra-high bypass ratio, constant volume combustor, and recuperated propulsion cycles are described, with a discussion of integration challenges within the airframe.

As aviation is one of the fastest growing industrial sector world wide, air-traffic emissions are projected to increase their stake in the contribution to global warming. According to recent studies, both CO2 and contrails will be the principal air-traffic pollutants. Since the environmental impact of contrails is potentially larger, their avoidance is becoming discussed in the aeronautical community. Work on this topic has been carried out at Cranfield University in form of a PhD project. A project summary is given in this paper where contrail avoidance strategies and the different aspects of contrail avoidance are highlighted. The first section provides an overview on the formation principles of contrails based on a literature review. Different technologies are given in the second part, and their introduction is discussed in the last section.

A fuel cell hybrid powertrain design is implemented and optimized by the University of Waterloo Alternative Fuels Team for the ChallengeX competition. A comprehensive set of bench-top and in-vehicle validation results are used to generate accurate fuel cell vehicle models for SIL/HIL control strategy testing and tuning. The vehicle is brought to a “99% buy-off” level of production readiness, and a detailed crashworthiness analysis is performed. The vehicle performance is compared to Vehicle Technical Specifications (VTS).

This paper gives an overview of the development of a number of loss models for the pushing metal V-belt CVT. These were validated using a range of experimental data collected from two test rigs. There are several contributions to the torque losses and new models have been developed that are based upon relative motion between belt components and pulley deflections. Belt slip models will be proposed based upon published theory, expanded to take account of new findings from this work. The paper introduces a number of proposals to improve the efficiency of the transmission based on redesign of the belt geometry and other techniques to reduce frictional losses between components. These proposed efficiency improvements have been modelled and substituted into a complete vehicle simulation to show improvements in vehicle fuel economy over a standard European drive cycle.

This paper investigates the application of standard formability testing results for aluminum alloy tailor welded blanks (TWB) to full size stampings. The limit strains obtained from formability testing are compared to measured strains in a larger scale part. The measured strains in the full scale part are also compared to predictions from finite element simulation.

Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal efforts. In previous work, dynamic Bayesian networks with heuristic features were used with the feature distributions trained using semi-supervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. First, the tracking formulation used in previous work has been corrected to prevent false positives in situations where only poor RANSAC hypotheses were generated. Second, the null hypothesis is reformulated to guarantee that detected hypotheses satisfy a minimum likelihood. Third, the computational requirements have been greatly reduced by computing an upper bound on the marginal likelihood of all part hypotheses upon generation and rejecting parts with an upper bound less likely than the null hypothesis.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The flow field and body aerodynamic loads on the DrivAer reference model have been extensively investigated since its introduction in 2012. However, there is a relative lack of information relating to the models wake development resulting from the different rear-body configurations, particularly in the far-field. Given current interest in the aerodynamic interaction between two or more vehicles, the results from a preliminary CFD study are presented to address the development of the wake from the Fastback, Notchback, and Estateback DrivAer configurations. The primary focus is on the differences in the far-field wake and simulations are assessed in the range up to three vehicle lengths downstream, at Reynolds and Mach numbers of 5.2×106 and 0.13, respectively. Wake development is modelled using the results from a Reynolds-Averaged Navier-Stokes (RANS) simulation within a computational mesh having nominally 1.0×107 cells.

The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range. This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle.

Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

The objective of this work is to investigate the thin water film characteristics by performing a range of experiments for different icing conditions. Our focus is on the SLD conditions where the droplets are larger and other effects like splashing and re-impingement could occur. Three features for the thin water film have been studied experimentally: the water film velocity, wave celerity and its wavelength. The experiments are performed in the icing facilities at Cranfiled University. The stability of the water film for the different conditions has been studied to find a threshold for transient from continues water film to non-continues form. A new semi-empirical method is introduced to estimate the water film thickness based on the experimental data of water film velocity in combination of theoretical analysis of water film dynamics. The outcome of this work could be implemented in SLD icing simulation but more analysis is needed.

Significant effort has been applied to the introduction of automation for the structural assembly of aircraft. However, the equipping of the aircraft with internal services such as hydraulics, fuel, bleed-air and electrics and the attachment of movables such as ailerons and flaps remains almost exclusively manual and little research has been directed towards it. The problem is that the process requires lengthy assembly methods and there are many complex tasks which require high levels of dexterity and judgement from human operators. The parts used are prone to tolerance stack-ups, the tolerance for mating parts is extremely tight (sub-millimetre) and access is very poor. All of these make the application of conventional automation almost impossible. A possible solution is flexible metrology assisted collaborative assembly. This aims to optimise the assembly processes by using a robot to position the parts whilst an operator performs the fixing process.

Water is always present in jet fuel, usually in a mixture of forms. At very low temperatures this phenomenon can lead to the formation of ice crystals within the aircraft fuel system, which can then stay in suspension within the entire volume of fuel. Pumps within the fuel system transfer fuel around the system. Pumps such as boost pumps that are typically used in fuel systems are protected by a weave type filter mesh at the inlet. Ice accretion on the surface of this mesh has operational implications as it can cause non optimal fuel flow. In this investigation, two fundamental tools are being used: 1) a high fidelity MATLAB model of a mesh strainer, pick-up line and pump, and 2) a test rig of the modelled system. The model is being used to investigate fuel system performance when exposed to fuel containing water/ice contaminants at cold temperatures.

The development of a comprehensive battery simulator is essential for future improvements in the durability, performance and service life of lithium-ion batteries. Although simulations can never replace actual experimental data, they can still be used to provide valuable insights into the performance of the battery, especially under different operating conditions. In addition, a single-cell model can be easily extended to the pack level and can be used in the optimization of a battery pack. The first step in building a simulator is to create a model that can effectively capture both the voltage response and thermal behavior of the battery. Since these effects are coupled together, creating a robust simulator requires modeling both components. This paper will develop a battery simulator, where the entire battery model will be composed of four smaller submodels: a heat generation model, a thermal model, a battery parameter model and a voltage response model.

Co-production of aircraft is resulting in demands for higher standards of manufacturing quality to ensure that parts and sub-assemblies from different companies and countries are compatible and interchangeable. As a result the existing method of building aerostructure using large numbers of dedicated manufacturing jigs and assembly tools, is now seen as being commercially undesirable, and technologically flawed. This paper considers an alternative, potentially more cost-effective, approach that embraces digital design, manufacturing, and inspection techniques, and in which reference and tooling features are incorporated into the geometry of the component parts. Within the aerospace industry this technology is known as ‘Flyaway Tooling’.

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.

In mobile R-744 A/C units mechanical expansion devices (e.g. orifice tubes) or electronic valves (e.g. PWM-valves) can be used. Besides the costs, aspects like coefficient of performance (COP), cooling capacity or control behavior - especially for extreme conditions - influence the choice of the valve type. This paper will present a comparison between an ideal electronic valve and a two stage mechanical orifice tube under full load and part load conditions. The influence of the expansion valve on COP and cooling capacity in different ambient conditions can be sufficiently described with steady-state simulations. The simulation tools used for this work are based on Modelica/Dymola. The simulation results show that for European climate conditions the use of two-stage orifices might increase fuel consumption.

This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.