Search Results

Ford's thermal management with batteries. Presenter Robert Taenaka, Ford Motor Co.

Combustion engines are typically only 20-30% efficient at part-load operating conditions, resulting in poor fuel economy on average. To address this, LiquidPiston has developed an improved thermodynamics cycle, called the High-Efficiency Hybrid Cycle (HEHC), which optimizes each process (stroke) of the engine operation, with the aim of maximizing fuel efficiency. The cycle consists of: 1) a high compression ratio; 2) constant-volume combustion, and 3) over-expansion. At a modest compression ratio of 18:1, this cycle offers an ideal thermodynamic efficiency of 74%. To embody the HEHC cycle, LiquidPiston has developed two very different rotary engine architectures ? called the ?M? and ?X? engines. These rotary engine architectures offer flexibility in executing the thermodynamics cycle, and also result in a very compact package. In this talk, I will present recent results in the development of the LiquidPiston engines. The company is currently testing 20 and 40 HP versions of the ?M?

In this paper a diagnostic design process is proposed for developmental vehicles where mainstream design process is not well-suited. First a review of current practice in on-board vehicle fault diagnostics design is presented with particular focus on the application of this process to the development of the Ford Escape Hybrid Electric Vehicle (HEV) program and a demonstration Fuel Cell Electric Vehicle (FCEV) program. Based on the review and evaluation of these experiences, a new tool for diagnostics design is proposed that promises to make the design more traceable, to reduce the repetition of work, and to improve understandability and reuse.

Having, by now, introduced several new vehicles that comply with FMVSS 202a, manufacturers are reporting an increased number of complaints from consumers who find that the head restraint is too close; negatively affecting their posture. It is speculated that one of the reasons that head restraints meeting the new requirement are problematic is that the FMVSS backset measurement is performed at a back angle that is more reclined than the back angle most drivers choose and the back angle at which the seat / vehicle was designed. The objective of this paper is to confirm this hypothesis and elaborate on implications for regulatory compliance in FMVSS 202a.

Effects of fuel cell material properties on water management were numerically investigated using Volume of Fluid (VOF) method in the FLUENT. The results show that the channel surface wettability is an important design variable for both serpentine and interdigitated flow channel configurations. In a serpentine air flow channel, hydrophilic surfaces could benefit the reactant transport to reaction sites by facilitating water transport along channel edges or on channel surfaces; however, the hydrophilic surfaces would also introduce significantly pressure drop as a penalty. For interdigitated air flow channel design, it is observable that liquid water exists only in the outlet channel; it is also observable that water distribution inside GDL is uneven due to the pressure distribution caused by interdigitated structure. An in-situ water measurement method, neutron imaging technique, was used to investigate the water behavior in a PEM fuel cell.

A uniaxial stress-strain curve obtained from a conventional tensile test is only valid up to the point of uniform elongation, beyond which a diffuse neck begins to develop, followed by localized necking and eventual fracture. However Finite Element Analysis for sheet metal forming requires an effective stress-strain curve that extends well beyond the diffuse necking point. Such an extension is usually accomplished by analytical curve fitting and extrapolation. Recent advancement in Digital Image Correlation (DIC) techniques allows direct measurement of full-range stress-strain curves by continuously analyzing the deformation within the diffuse neck zone until the material ruptures. However the stress-strain curve obtained this way is still approximate in nature. Its accuracy depends on the specimen size, the gage size for analysis, and the material response itself.

The conventional forming limit curve (FLC) has been widely and successfully used as a failure criterion to detect localized necking in stamping. However, in stamping advanced high strength steels (AHSS), under certain circumstances such as stretching-bending over a small die radius, the sheet metal fails much earlier than predicted by the FLC. This type of failure on the die radius is commonly called “shear fracture.” In this paper, the laboratory Stretch-Forming Simulator (SFS) and the Bending under Tension (BUT) tester are used to study shear fracture occurring during both early and later stages of stamping. Results demonstrate that the occurrence of shear fracture depends on the combination of the radius-to-thickness (R/T) ratio and the tension/stretch level applied to the sheet during stretching or drawing. Based on numerous experimental results, an empirical shear fracture limit curve or criterion is obtained.

A detailed investigation of the influence of anisotropic hardening models on drawbead restraining force is presented in this paper. The recently modified Yoshida model is adopted to characterize the anisotropic hardening behavior for steels. A two-dimensional drawbead model is used and the restraining forces corresponding to several different bead penetrations are obtained and compared against experimental results. The comparison of the predicted results for the Modified Yoshida Model with isotropic hardening models indicates that the anisotropic hardening gives lower drawbead restraining forces in general. The impact of hardening models on springback is also presented, and it's demonstrated that the springback amount predicted by the modified Yoshida model is much closer to the experimental data than that predicted by conventional isotropic hardening model.

The main objective of this paper is to investigate the performance of partial filtration DPF substrates using 3-D Computational Fluid Dynamics (CFD) methods. Detailed 3-D CFD simulations were performed for real world sizes of DPF inlet and outlet channel geometries. Two concepts of partial filters were studied. The baseline geometry was a standard DPF with the front plugs removed. The second concept was to eliminate half of outlet plugs in addition to the inlet plugs to improve the pressure drop performance. The total filter efficiency was defined in current study to quantify the overall filter filtration efficiency which combines the effects from wall flow efficiency and flow through efficiency. For baseline case, 45% of total exhaust gas was found to go through the inlet channels, and the total trap efficiency was as high as 60%. However, only a 10% pressure loss reduction was found due to the removal of the outlet channel plugs from the DPF inlet side.

Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

This paper presents the extended Kalman filter-based sideslip angle estimator design using a nonlinear 5DoF single-track vehicle dynamics model with stochastic modeling of tire forces. Lumped front and rear tire forces have been modeled as first-order random walk state variables. The proposed estimator is primarily designed for vehicle sideslip angle estimation; however it can also be used for estimation of tire forces and cornering stiffness. This estimator design does not rely on linearization of the tire force characteristics, it is robust against the variations of the tire parameters, and does not require the information on coefficient of friction. The estimator performance has been first analyzed by means of computer simulations using the 10DoF two-track vehicle dynamics model and underlying magic formula tire model, and then experimentally validated by using data sets recorded on a test vehicle.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

When a vehicle with a partially filled fuel tank undergoes sudden acceleration, braking, turning or pitching motion, fuel sloshing is experienced. It is important to establish a CAE methodology to accurately predict slosh phenomenon. Fuel slosh can lead to many failure modes such as noise, erroneous fuel indication, irregular fuel supply at low fuel level and durability issues caused by high impact forces on tank surface and internal parts. This paper summarizes activities carried out by the fuel system team at Ford Motor Company to develop and validate such CAE methodology. In particular two methods are discussed here. The first method is Volume Of Fluid (VOF) based incompressible multiphase Eulerian transient CAE method. The CFD solvers used here are Star CD and Star CCM+. The second method incorporates Fluid-Structure interaction (FSI) using Arbitrary Lagrangian-Eulerian (ALE) formulation.

Clutch wear is dominantly manifested as the reduction of friction plate thickness. For dry dual clutch with position-controlled electromechanical actuators this affects the accuracy of normal force control because of the increased clutch clearance. In order to compensate for the wear, dry dual clutch is equipped with wear compensation mechanism. The paper presents results of experimental characterization and mathematical modeling of two clutch wear related effects. The first one is the decrease of clutch friction plate thickness (i.e. increase of clutch clearance) which is described using friction material wear rate experimentally characterized using a pin-on-disc type tribometer test rig. The second wear related effect, namely the influence of the clutch wear compensation mechanism activation at various stages of clutch wear on main clutch characteristics, was experimentally characterized using a clutch test rig which incorporates entire clutch with related bell housing.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

Failure mode and fatigue behavior of dissimilar laser welds in lap-shear specimens of aluminum and copper sheets are investigated. Quasi-static tests and fatigue tests of laser-welded lap-shear specimens under different load ranges with the load ratio of 0.1 were conducted. Optical micrographs of the welds after the tests were examined to understand the failure modes of the specimens. For the specimens tested under quasi-static loading conditions, the micrograph indicates that the specimen failed through the fusion zone of the aluminum sheet. For the specimens tested under cyclic loading conditions, two types of failure modes were observed under different load ranges. One failure mode has a kinked crack initiating from the interfacial surface between the aluminum and copper sheets and growing into the aluminum fusion zone at an angle close to 90°.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.