Search Results

Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.

A new realistic motion control algorithm for digital human models is presented in this paper based on the principle of effort minimization. The proposed algorithm is developed through an innovative mathematical model to make the applications more flexible and more global, especially for the visualization of human motions in automotive assembly operations. The central idea of this unique model is to interpret the solution of the homogeneous Lagrange equation for a mannequin as the origin of dynamic motion. Furthermore, a digital human possesses about 42 joints over the main body except the head, fingers and toes, and offers a large room of kinematic redundancy. We have found 14 new 3-D independent motion markers assigned over the human body to constitute a Cartesian coordinate system, under which a minimum-effort based dynamic control scheme is developed using a state-feedback linearization procedure.

This paper examines the conformability of elastic piston rings to a distorted cylinder bore. Several bounds are available in the literature to help estimate the maximum allowable Fourier coefficient in a Fourier expansion of bore distortion: the analytically derived bounds in [7] and [8], and the semi-empirically derived bounds discussed in [9]. The underlying assumptions for each set of analytic bounds are examined and a multiple order algorithm is derived. The proposed algorithm takes account of multiple orders of distortion at once. It is tested with finite element (FE) data and compared to the classical bound approach. The results indicate that the bounds in [7] are compatible with linear elasticity theory (LET), whereas the bounds in [8] are not. Furthermore, numerical evidence indicates that the present multiple order algorithm can predict seal breaches more accurately than either of the other analytic bounds.

Transmission system models require a high level of fidelity and details in order to capture the transient behaviors in drivability and fuel economy simulations. Due to model fidelity, manufacturing tolerances, frictional losses and other noise sources, parametrization and tuning of a large number of parameters in the plant model is very challenging and time consuming. In this paper, we used particle swarm optimization as the key algorithm to fast correlate the open-loop performance of an automatic transmission system plant model to vehicle launch and coast down test data using vehicle control inputs. During normal operations, the model correlated well with test data. For error states, due to the lack of model fidelity, the model cannot reproduce the same error state quantitatively, but provided a valuable methodology for qualitatively identifying error states at the early stages.

Recently, papers have been published purporting to study the effect of rear axle tramp during tread separation events, and its effect on vehicle handling [1, 2]. Based on analysis and physical testing, one paper [1] has put forth a mathematical model which the authors claim allows vehicle designers to select shock damping values during the development process of a vehicle in order to assure that a vehicle will not experience axle tramp during tread separations. In the course of their work, “lumpy” tires (tires with rubber blocks adhered to the tire's tread) were employed to excite the axle tramp resonance, even though this method has been shown not to duplicate the physical mechanisms behind an actual tread belt separation. This paper evaluates the theories postulated in [1] by first analyzing the equations behind the mathematical model presented. The model is then tested to see if it agrees with observed physical testing.

Automated driving is currently one of the most active areas of research worldwide. While the general progress in developing specific algorithms for perception, planning and control tasks is very advanced, testing and validation of the resulting functions is still challenging due to the large number of possible scenarios and generation of ground-truth. Currently, real world testing and simulations are used in combination to overcome some of these challenges. While real world testing does not suffer from imperfect sensor models and environments, it is expensive, slow and not accurately repeatable and therefore unable to capture all possible scenarios. However, simulation models are not sophisticated enough to fully replace real world testing. In this paper, we propose a workflow that is capable of augmenting real sensor-level data with simulated sensor data.

This paper describes the issues encountered in using conventionally acquired tire test data for dynamic total vehicle handling simulations and the need for improved methodology. It describes the new test procedure that was used to acquire all three forces and three moments in a transient mode for a matrix of loads, slip and camber angles. A study of the test data supports the premises that the overturning moment, Mx, should not be neglected in dynamic simulations, and that the effects of camber should not be treated as only an independent, linearly additive, camber thrust. Instead of the conventional application of a bi-cubic regression fit to a six region data division, a new algorithm is applied. The data is divided differently into five regions in the α - Fz plane, and a variable format regression equation is applied as appropriate. The resulting regression coefficients matrix is readily usable in dynamic simulations, and is shown to have a superior curve fit to the test data.

This paper investigates the use of an adaptive proportional-integral-derivative (APID) controller to reduce a combustion engine crankshaft speed pulsation. Both computer simulations and engine test rig experiments are used to validate the proposed control scheme. The starter/alternator (S/A) is used as the actuator for engine speed control. The S/A is an induction machine. It produces a supplemental torque source to cancel out the fast engine torque variation. This machine is placed on the engine crankshaft. The impact of the slowly varying changes in engine operating conditions is accounted for by adjusting the APID controller parameters on-line. The APID control scheme tunes the PID controller parameters by using the theory of adaptive interaction. The tuning algorithm determines a set of PID parameters by minimizing an error function. The error function is a weighted combination of the plant states and the required control effort.

One important design goal for spark-ignited engines is to minimize cyclic variability. A small amount of cyclic variability (slow burns) can produce undesirable engine vibrations. A larger amount of cyclic variability (incomplete burns) leads to increased hydrocarbon consumption/emissions. Recent studies have reported deterministic patterns in cyclic variability under extremely lean (misfiring) operating conditions. The present work is directed toward more realistic non-misfiring conditions. Production engine test results suggest that deterministic patterns in cyclic variability are the consequence of incomplete combustion, hence control algorithms based on the occurrence of these patterns are not expected to be of significant practical value.

Distinguishing physical modes from mathematical modes in the modal analysis of complex systems, such as full vehicle structures, is a difficult and time-consuming process. The major tools frequently used are stabilization diagrams, mode indicator functions, or modal participation factors. When closely spaced modes are to be identified, the stabilization diagrams and mode indicator functions are no longer effective. Even the reciprocities of mode shapes and modal participation factors cannot be well satisfied to indicate whether a mode is a physical one, when measurement errors are large. To overcome these difficulties, an optimization procedure is developed, whereby physical modes can be sorted out in a given frequency range while the error between measured and synthesized frequency responses is minimized. An optimal subset selection algorithm is used in this procedure.

In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal assignments with the directional costs as the output. The tool has been tested for several plants at Ford and has proved its robustness by saving millions of dollars. The developed tool can easily be applied to other manufacturing systems and original equipment manufacturers (OEMs).

We describe an optimization model developed by Ford Motor Company to reallocate stamped parts between facilities when business conditions change. How can the business meet new targets when demand starts to exceed existing capacity? Likewise, how can it respond when demand is lower than expected? Sometimes the business can reduce costs by transferring production to a different location or by outsourcing parts. We describe in this paper how mathematical optimization can identify solutions that balance both logistical and outsourcing costs. We explain the algorithm and demonstrate with a small example how it recommends sourcing plans that minimize cost.

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.

This paper presents an approach of using neural network and fuzzy logic methods for the diagnosis of fault root causes in a manufacturing environment. As the first step in this approach, data from all the valid test points were collected and studied based on their statistical characteristics. An information-gain-based procedure was then followed to quantitatively evaluate the relevance of each test point to the diagnosis process. Accordingly, an objective rank of all relevant test points was generated for a particular reject. The root cause of rejects was then identified by a procedure based on an information-gain-weighted radial basis function neural network and a fuzzy multiple voting classification algorithm. This method has been tested with the top five rejects of the transmission main control component at Ford and promising results have been obtained.

This paper presents the development and applications of engine friction algorithms to quickly estimate performance, optimum geometry of critical engine components, and packaging for rapid engine concept assessments. The development and implementation of some knowledge-based design rules will also be presented to quickly estimate the critical geometry of engine components and component weight such as valve sizing, piston weight, crankshaft geometry, etc. Some examples of powertrain concept design, such as the estimation of friction and packaging will be presented. The simulation results of the friction algorithms will be compared to some of available experimental data and also other friction estimation methods.

Simulink is a very successful and popular method for modelling and auto-coding embedded automotive features, functions and algorithms. Due to its history of success, university feeder programs, and large third party tool support, it has, in some cases, been applied to areas of the software system where other methods, principles and strategies may provide better options for the software and systems engineers and architects. This paper provides approaches to determine when best to apply UML and when best to apply Simulink to a typical automotive feature. Object oriented software design patterns as well as general guidelines are provided to help in this effort. This paper's intent is not to suggest a replacement for Simulink but to provide the software architects and designers additional options when decomposing high level requirements into reusable software components.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality.

Automotive companies have invested a fortune over the last three decades developing real-time embedded control strategies and software to achieve desired functions and performance attributes. Over time, these control algorithms have matured and achieved optimum behavior. The companies have vast repositories of embedded software for a variety of control features that have been validated and deployed for production. These software functions can be reused with minimal modifications for future applications. The companies are also constantly looking for new ways to improve the productivity of the development process that may translate into lower development costs, higher quality and faster time-to-market. All companies are currently embracing Model Based Design (MBD) tools to help achieve the gains in productivity. The most cost effective approach would be to reuse the available legacy software for carry-over features while developing new features with the new MBD tools.