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In the current Ford Pro-Trailer Backup Assist (TBA) system, trailer hitch angle is determined utilizing the reverse camera of the vehicle. In addition to being sensitive to environmental factors such as lighting conditions and occlusion, the vision-based approach is difficult to be applied to gooseneck or fifth wheel trailers. In this paper, a yaw rate based hitch angle observer is proposed as an alternative sensing solution for TBA. Based on the kinematic model of the vehicle-trailer, an instantaneous hitch angle is first derived by utilizing vehicle yaw rate, trailer yaw rate, vehicle velocity and vehicle/trailer parameters provided by the TBA system. Due to signal errors and parameter uncertainties, this instantaneous hitch angle may be noisy, especially at lower vehicle speed.

Onboard, embedded cellular modems are enabling a range of new connectivity features in vehicles and rich, real-time data set transmissions from a vehicle’s internal network up to a cloud database are of particular interest. However, there is far too much information in a vehicle’s electrical state for every vehicle to upload all of its data in real-time. We are thus concerned with which data is uploaded and how that data is processed, structured, stored, and reported. Existing onboard data processing algorithms (e.g. for DTC detection) are hardcoded into critical vehicle firmware, limited in scope and cannot be reconfigured on the fly. Since many use cases for vehicle data analytics are still unknown, we require a system which is capable of efficiently processing and reporting vehicle deep data in real-time, such that data reporting can be switched on/off during normal vehicle operation, and that processing/reporting can be reconfigured remotely.

Sheet metal forming of automobile body panel consists of two processes performed in series: binder forming and punch forming. Due to differences in deformation characteristics of the two forming processes, their analysis methods are different. The binder wrap surface shape and formed part shape are calculated using different mathematical models and different finite element codes, e.g., WRAPFORM and PANELFORM, respectively. The output of the binder forming analysis may not be directly applicable to the subsequent punch forming analysis. Interpolation, or approximation, of the calculated binder wrap surface geometry is needed. This surface representation requirement is carried out using computer aided geometric design tools. This paper discusses the use of such a tool, SURFPLAN, to link WRAPFORM and PANELFORM calculations.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

This paper reports a study of the dynamic characteristics of body mounts in body on frame vehicles and their effects on structural and occupant CAE results. The body mount stiffness and damping are computed from spring-damper models and component test results. The model parameters are converted to those used in the full vehicle structural model to simulate the vehicle crash performance. An effective body mount in a CAE crash model requires a set of coordinated damping and stiffness to transfer the frame pulse to the body. The ability of the pulse transfer, defined as transient transmissibility[1]1, is crucial in the early part of the crash pulse prediction using a structural model such as Radioss[2]. Traditionally, CAE users input into the model the force-deflection data of the body mount obtained from the component and/or full vehicle tests. In this practice, the body mount in the CAE model is essentially represented by a spring with the prescribed force-deflection data.

This paper presents a Short Time Fourier Transform based algorithm to identify unknown parameters in fuel dynamics system during engine cold start and warm-up. A first order system is used to model the fuel dynamics in a port fuel injection engine. The feed forward transient fuel compensation controller is designed based on the identified model. Experiments are designed and implemented to verify the proposed algorithm. Different experiment settings are compared.

Time to torque (TTT) is a quantity used to measure the transient torque response of turbocharged engines. It is referred as the time duration from an idle-to-full step torque command to the time when 95% of maximum torque is achieved. In this work, we seek to control multiple engine actuators in a collaborative way such that the TTT is minimized. We pose the TTT minimization problem as an optimization problem by parameterizing each engine actuator’s transient trajectory as Fourier series, followed by minimizing proper cost function with the optimization of those Fourier coefficients. We first investigate the problem in CAE environment by constructing an optimization framework that integrates high-fidelity GT (Gamma Technology) POWER engine model and engine actuators’ Simulink model into ModeFrontier computation platform. We conduct simulation optimization study on two different turbocharged engines under this framework with evolutionary computation algorithms.

Wavelets are a powerful mathematical tool used to multi-resolution time-frequency decomposition of signals, in order to analyze them in different scales and obtain different aspects of the information. Despite being a relatively new tool, wavelets have being applied in several areas of human knowledge, especially in signal processing, with emphasis in encoding and compression of image, video and audio. Based on a previous successful applications (FRAZIER, 1999) together a commitment to quality results, this paper evaluates the use of the Discrete Wavelet Transform (DWT) as an compression algorithm to reduce the amount of data collected in road load signals (load history) which are used by the durability engineering teams in the automotive industry.

A detailed description of the oxidative stability of GM's DEXRON®-VI Factory Fill Automatic Transmission Fluid (ATF) is provided, which can be integrated into a working algorithm to estimate the end of useful oxidative life of the fluid. As described previously, an algorithm to determine the end of useful life of an automatic transmission fluid exists and is composed of two simultaneous counters, one monitoring bulk oxidation and the other monitoring friction degradation [1]. When either the bulk oxidation model or the friction model reach the specified limit, a signal can be triggered to alert the driver that an ATF change is required. The data presented in this report can be used to develop the bulk oxidation model. The bulk oxidation model is built from a large series of bench oxidation tests. These data can also be used independent of a vehicle to show the relative oxidation resistance of this fluid, at various temperatures, compared to other common lubricants.

Flanging is a secondary operation in sheet metal forming processes. Traditionally, the design of flange shape and trim line is based on an engineer's experience. It takes several iterations to achieve the desired flange geometry because of potential splits. In this paper, an efficient CAE-based tool is developed to quickly predict the formability of a given flange design and enable the optimization of trim lines. A numerical algorithm is formulated in this CAE tool to convert the 3D flanging process into an equivalent in-plane deformation problem. The developed CAE tool is also integrated with the optimization software LS-OPT for trim line design.

It is estimated that up to 30% of traffic in cities is due to drivers searching for parking. Research suggests that drivers spend an average of 6-14 minutes looking for an available space in London. This increases individual stress levels as well as congestion and pollution. Parking Guidance Systems provide an effective way to reduce parking search time by presenting drivers with dynamic information on parking. An accurate prediction and recommendation analytics algorithm is the key part of the system combining real time cloud-based analytics and historical data trends that can be integrated into a smart parking user application. This paper develops a prediction algorithm based on transient queuing theory and Laplace transform to predict parking occupancy thus predicting open parking locations.

Characterizing or reconstructing incidents ranging from light to heavy crashes is one of the enablers for mobility solutions for fleet management, car-sharing, ride-hailing, insurance etc. While crashes involving airbag deployment are noticeable, light crashes without airbag deployment can be hidden and most drivers do not report these incidents. In this paper, we are using vehicle responses together with a dynamics model to trace back if abnormal forces have been applied to a vehicle so as to detect light crashes. The crash location around the perimeter of the vehicle, the direction of the crash force, and the severity of the crashes are all determined in real-time based on on-board sensor measurements which has further application in accident reconstruction. All of this information will be integrated to a feature called “Incident Report”, which enable reporting of minor accidents to the relevant entities such as insurance agencies, fleet managements, etc.

Loosely coupled transformers are commonly used in inductive power transfer (IPT) systems which are inevitable part of electrified transportation. Since efficiency of these systems is mainly dependent on alignment of primary (ground side) and secondary (vehicle side) coils, estimation of coupling coefficient has a significant impact on the performance of IPT chargers. However, despite the requisite need for a plausible estimation algorithm, the lack of a simple, optimal and unsusceptible to noise algorithm is noticeable. In this paper, we introduce a new online optimal prediction method for IPT systems allowing a precise real time estimation of the coupling coefficient in the presence of measurement noises and system uncertainties. Using IPT system dynamics, the estimation scheme is proposed based on Kalman filter algorithm. This algorithm is optimal, tractable and robust and its estimation are promising as simulation results reveal.

This study compares the model efficacy of Neural Network and Vector Auto Regression. Further it also analyses the impact of predictors controlling for total industry volume. Understanding both the methodologies has their distinctive advantages and disadvantages. Our empirical findings indicate that based on the characteristics of data such as non-stationary, non-linearity and non-normality paves the way for use of machine learning algorithm relative to econometrics technique. Our results suggest that data type and its characteristics are more important in determining the methodology than the methodology itself. In industry, econometrics methodologies are widely used due to their usage simplicity and its ability to explain the relationships in simple terms.

Electrical Computer Aided Engineering (CAE) is necessary and useful for the automotive industry [1,2]. It provides the user with necessary information that helps him/her make faster and more certain design decisions. CAE facilitates for the user options and the means to locate and choose optimums [6]. It requires models that best represent the actual system while avoiding unnecessary numerical overhead. Therefore, it is necessary to build the mathematical model in a systematic way that captures dynamics of the actual system [3]. Also, an optimal solution for the system parameters is required to increase the accuracy of the model and to make a correct decision while designing, testing, and validating [3,6]. This paper studies the CAE analysis of a PMDC motor. It develops a systematic approach to model, simulate and analyze PMDC motors with robust output. It enhances the accuracy of PMDC to a 6-Sigma level.

Drivers often engage in secondary in-vehicle activity that is not related to vehicle control. This may be functional and/or to relieve monotony. Regardless, drivers believe they can safely do so when their perceived workload is low. In this paper, we describe a data acquisition system and machine learning based algorithms to determine perceived workload. Data collected were from on-road driving in light and heavy traffic, and individual physiological measures were recorded while the driver also performed in-vehicle tasks. Initial results show how the workload function can be personalized to an individual, and what implications this may have for vehicle design.

In modern automotive engines, Variable Displacement Oil Pump (VDOP) is becoming the pump of choice to enable reduction in friction and delivery of stringent fuel economy. However, this pump creates pressure ripples, at the outlet port during oil pump shaft rotation, causing oscillating forces within the system and leading to the generation of tonal noises and vibrations. In order to minimize the level of noise, different porting geometries and vane spacing are used. This paper describes an optimization method intended to identify the best possible spacing of the vanes in the conventional 7-vanes, 9-vanes and 11-vanes oil pumps. The method integrates a Matlab routine with the modeFRONTIER software to create the required design space in order to perform a multi-objective optimization using a genetic algorithm. Results of this optimization method are discussed and a design guideline for the VDOP vane spacing is disclosed.

Variable Displacement Oil Pump (VDOP) is becoming the design of choice for engine friction reduction and fuel economy improvement. Unfortunately, this pump creates excessive pressure ripples, at the outlet port during oil pump shaft rotation, causing oscillating forces within the lubrication system and leading to the generation of objectionable tonal noises and vibrations. In order to minimize the level of noise, different vanes spacing and porting geometries are used. Moreover, an oil pressure relief groove can be added, at the onset of the high pressure port, to achieve this goal. This paper presents an optimization method to identify the best geometry of the oil pressure relief groove. This method integrates adaptive meshing, 3D CFD simulation, Matlab routine and Genetic Algorithm based optimization. The genetic algorithm is used to create the required design space in order to perform a multi-objective optimization using a large number of parameterized groove geometries.

Computer modeling has played important roles and gained great momentum in product development as numerical methods, computer software and hardware technologies advance rapidly. Computer models (e.g. MADYMO) that simulate vehicle interior, restraint system and occupants in various crash modes have been widely used to improve occupant safety. However, to build good occupant models, engineers often have to spend tremendous time on model correlation. The challenge of model correlation for occupant safety is that it requires matching numerous injury curves with tests, for examples: head G, chest G, chest deflection, shoulder belt load, femur loads, neck load and moment. Traditionally, this model correlation task is done by a trial and error method. This paper attempts to solve the problem systematically by using a genetic algorithm. It demonstrates that the genetic algorithm is a valuable optimization tool to obtain a high quality MADYMO model.

The impact of fuel droplets on heated surfaces is of great importance in internal combustion engines. In engine computational fluid dynamics (CFD) simulations, the drop-wall interaction is usually considered by using models derived from experimental data and correlations rather than direct simulations. This paper presented a numerical method based on smoothed particle hydrodynamics (SPH), which can directly simulate the impact process of fuel droplets impinging on solid surfaces. The SPH method is a Lagrangian meshfree particle method. It discretizes fluid into a number of SPH particles and governing equations of fluid into a set of particle equations. By solving the particle equations, the movement of particles can be obtained, which represents the fluid flows. The SPH method is able to simulate the large deformation and breakup of liquid drops without using additional interface tracking techniques.