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This paper presents a response to the question: Simulation - mathematical manipulation or useful design tool? A mathematical model of a modulating valve in a transmission control system was developed to predict clutch pressure modulation characteristics. The transmission control system was previously reported in SAE Paper 850783 - “Electronic/Hydraulic Transmission Control System for Off-Highway Vehicles”. The comparison of simulation predictions with test data illustrates the effectiveness of simulation as a design tool. THE EVOLUTION OF COMPUTER hardware and simulation software has resulted in increased interest and usage of simulation for dynamic analysis of hydraulic systems. Most commercially available software is relatively easy to learn to use. The application of such software and the modeling techniques involved require a longer learning curve.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

Twent-two participants of varying ages detected roadside targets in two consecutive dynamic evaluations of a horizontally swiveling headlamp vehicle and a vehicle with the same headlamps that did not swivel. Participants detected targets as they drove unlighted low-speed public roads. Scenarios encountered were intersection turns, and curves with approximate radii of 70-90m, 120-140m, 170-190m, and 215-220m. Results from the first study found improved detection distances from the swiveling headlamps in left curves, but unexpectedly decreased detection distances in larger radius right hand curves. The swiveling algorithm was altered for the second study, and the headlamps used did not have the same beam pattern as in the first study. Results from the second study again found improved detection distances from the swiveling headlamps while in the larger radius right hand curves fixed and swivel were not statistically different.

The main objective of this paper is to simulate the springback using combined kinematic/isotropic hardening model. Material parameters in the hardening model are identified by an inverse method. Three-point bending test is conducted on 6022-T4 aluminum sheet. Punch stroke, punch load, bending strain and bending angle are measured directly during the tests. Bending moments are then computed from these measured data. Bending moments are also calculated based on a constitutive model. Material parameters are identified by minimizing the normalized error between two bending moments. Micro genetic algorithm is used in the optimization procedure. Stress-strain curves is generated with the material parameters found in this way, which can be used with other plastic models. ABAQUS/Standard 5.8, which has the combined isotropic/kinematic hardening model, is used to simulate draw-bend of 6022-T4 series aluminum sheet. Absolute springback angles are predicted very accurately.

As both the number and complexity of electronic control system applications on earthmoving equipment and on-highway trucks increase, so does the effort associated with developing and maintaining control strategies implemented in embedded systems. A new tool was recently introduced by Sigma Technology of Ann Arbor, Michigan, that provides the capability to perform rapid prototyping of production embedded systems. The rapid prototyping process includes system modeling, control algorithm synthesis, simulation analysis, source code generation and vehicle implementation. The results of incorporating this tool in the control system design process include improved control performance, improved system reliability/robustness, and significantly reduced development/maintenance costs.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

Faced with competitive environments, pressure to lower development costs and aggressive timelines engineers are not only increasingly adopting numerical simulation techniques but are also embracing design optimization schemes to augment their efforts. These techniques not only provide more understanding of the trade-offs but are also capable of proactively guiding the decision making process. However, design optimization and exploration tools have struggled to find complete acceptance and are typically underutilized in many applications; especially in situations where the algorithms have to compete with existing swift decision making processes. In this paper we demonstrate how the type of setup and algorithmic choice can have an influence and make optimization more lucrative in a new product development atmosphere. We also present some results from a design exploration activity, involving linkage and structural development, of an earth moving machine application.

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

The paper examines how the issue of lengthy development times can be mitigated by adopting a multivariable physics based control method for the development and deployment of complex engine control algorithms required for modern diesel engines equipped with Lean NOx Trap aftertreatment technology. The proposed approach facilitates manufacturers to consider lower cost powertrain configurations for selected markets while maintaining higher performance configurations for other markets. The contribution includes on-engine results from joint work between General Motors and Honeywell. The Honeywell OnRAMP Design Suite which applies model predictive control techniques was used for model identification, control design (using model predictive control) and its calibration. With no prior work on the engine this process of calibrating an engine model and achieving transient drive cycle control on the engine required ten days in the test cell and five days of offline work using the OnRAMP software.

A driveline differential gear housing or diff-case is the heaviest component of a driveline that rotates at high velocities. core shift during diff-case casting is a major source of imbalance as casting cores can never be placed at the exact intended location. Core shift in the present case is defined as combination of pure translation along the parting plane and tilting about two orthogonal axes. Given the ranges of variation of these shift parameters, large numbers of random sampling of these variations are generated through Monte Carlo method where normal distribution of each of the core shift parameters is assumed. Static unbalance values of the diff-case from each of the instances of core shift is calculated using Boolean operation in MSC Adams View and a nonlinear data set is created. Next, a statistical model is created based on a neutral network-based fitting method to appropriately represent the set.

Electro-mechanical actuators are widely used in different forms in the automotive industry these days and are very effective in translating rotary motion of the motor to a linear motion with precision as per the requirement. With onset of electric drivelines and other complex driveline configurations, there is an increase in actuator applications. In this study, high fidelity analytical model was developed for a ball ramp clutch actuation system as per the proposed mechanical design and the software controls. The controls architecture is built in SIMULINK environment to drive the plant model of the BLDC motor. Whereas, the entire mechanical system which includes the gear train, ball ramp mechanism, clutch pack stiffness, etc. is modelled in ADAMS tool. The Ball Ramp consists of metal balls positioned in between opposing paired arc-shaped ramp grooves and guided by a cage to keep them equally separated in the same plane.