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In this paper, thermal models are developed based on experimental test data, and the physics of thermal systems. If experimental data is available, the data can be fitted to mathematical models that represent the system response to changes in its input parameters. Therefore, empirical models which are based on test data are developed. The concept of time constant is presented and applied to development of transient models. Mathematical models for component temperature changes during transient vehicle driving conditions are also presented. Mathematical models for climate control system warm up and cool-down are also discussed. The results show the significance of adopting this concept in analysis of vehicle test data, and in development of analytical models. The developed models can be applied to simulate the system or component response to variety of changes in input parameters. As a result, significant testing and simulation time can be saved during the vehicle development process.

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.

Cab mounts and suspension bushings are crucial for ride and handling characteristics and must be durable under highly variable loading. Such elastomeric bushings exhibit non-linear behavior, depending on excitation frequency, amplitude and the level of preload. To calculate realistic loads for durability analysis of cars and trucks multi-body simulation (MBS) software is used, but standard bushing models for MBS neglect the amplitude dependent characteristics of elastomers and therefore lead to a trade-off in simulation accuracy. On the other hand, some non-linear model approaches lack an easy to use parameter identification process or need too much input data from experiments. Others exhibit severe drawbacks in computing time, accuracy or even numerical stability under realistic transient or superimposed sinusoidal excitation.

Two on-line algorithms have been developed to acquire driveline component loads in terms of revolutions at torque and rainflow cycle counting matrix. These algorithms have been implemented in real-time on a standard engine controller unit and have been optimized for fast run-time and low memory requirements. The revolutions at torque algorithm is intended to count the number of driveshaft revolutions in each torque level for each gear and store the number of counts in the engine controller memory. The rainflow cycle counting algorithm is intended to count driveshaft torque cycles and to store the number of counts in a two dimensional “from-to” matrix format in the engine controller memory. The revolutions at torque histogram data and the rainflow cycle counting matrix are then downloaded from the vehicle using the data collection device. Download occurs when the vehicle is serviced at a dealership.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

An accurate representation of proving ground loading is essential for nonlinear Finite Element analysis and component fatigue test. In this paper, a rainflow counting based multiple blocks loading development procedure is described. The procedure includes: (1) Rainflow counting analysis to obtain the relationship between load range and cumulative repeats and the statistical relationship between load range and mean load; (2) Formation of preliminary multiple loading blocks with specified load range, mean load, and the approximate cycle repeats, and construction of the preliminary multiple loading blocks; (3) Calibration and finalization of the repeats for preliminary multiple loading blocks according to the equivalent damage rule, meaning that the damage value due to the block loads is equivalent to that from a PG loading.

Contacts or interactions commonly exist between adjacent components in automotive structures, and most of the time they dominate stress status of the components. However, when the routine pseudo stress approach is employed in fatigue life estimations, simulating contacts present special challenges. This may result in coarse stress status and corresponding coarser fatigue life estimations at the contact locations. In this paper, concept, development and procedures of two techniques to consider contacts in fatigue life estimations of automotive structures are described in detail. One is still pseudo stress approach based, but employs additional 1-D connection elements to simulate contacts. The other is nonlinear stress approach based, but equivalent constantly repeating cyclic critical load cases are introduced and utilized. The contacts are simulated by interface setup provided in the software.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

This paper presents the design of a hybrid powertrain damping control algorithm using the sliding mode control (SMC) scheme. Motor control-based active damping control strategy is used to ensure smooth drive line operation and provide the driver with seamless driving experience. In the case of active damping control, motor and engine speeds are measured to monitor the driveline state, and corrective motor torques are generated to dampen out drive line vibrations. Drive lines are prone to internal vibration (engine, clutches and motors) as well as external disturbances caused by road inputs. As such, fast-response actuator-based damping control systems are desirable in a hybrid powertrain application, where a torque converter is generally not used. The most significant aspect of an active damping control algorithm is the error calculation, based on proper states information, and torque determination based on the adaptive control gain applied to the nonlinear system.

Fatigue analysis in the time domain using the rainflow cycle counting algorithm is considered the most accurate method for estimating damage. Dirlik's method has been found to be very accurate for damage estimation in the frequency domain. Previous studies have demonstrated the usefulness of Dirlik's method for ocean engineering and wind turbines but few have shown how well Dirlik performs in automotive applications. This study compares Dirlik's method with the rainflow cycle counting and with other frequency domain methods. The study analyzes measured data for an automotive component subjected to five test track load conditions. In addition, fourteen of Dirlik's original spectra and seven additional spectra which combine sine and random spectra are studied. It was found that Dirlik's method predicts more damage than the rainflow cycle counting method when applied to the original data used in creating the method.

In this paper, a transient thermal analysis model for Diesel fuel systems is presented. The purpose of this work is to determine the fuel temperature at various locations along the system, especially inside the tank and at the returned fuel inlet to the tank. Due to the fact that the fuel level is continuously changing during any driving condition, the fuel mass inside the tank is also continuously changing. Consequently, the fuel temperature will change even under steady driving or idle conditions, therefore, this problem should be analyzed using transient thermal analysis models. Effective thermal management requires controlling the surface temperature of the fuel tank, fuel lines and the fuel temperature at the fuel return line as well as inside the tank [1, 2]. Based on the thermal analysis results, it is possible to determine the major source of heat input at several locations of the fuel system.

One method of reducing the number and/or severity of vehicle crashes is to warn the driver of a potential crash. The theory is that there will be driving conditions in which the drivers are unaware of a potential crash and a warning system will allow them to, in some manner, avoid the accident or reduce the severity. In an attempt to develop an analytical understanding of Forward Collision Warning systems (FCW) for frontal impacts a 2-d mathematical/kinematic model representing a set of pre-crash vehicle dynamic maneuvers has been built. Different driving scenarios are studied to explore the potential improvement of warning algorithms in terms of headway reduction and minimization of false alarm rates. The results agree with the field data. NHTSA's new NCAP active safety criteria are evaluated using the model. The result from the analysis indicates that the NHTSA criteria may drive higher false alarm rates. Opportunities of minimizing false positive rates are discussed.