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Optimization of heavy materials like steel, in order to create a lighter vehicle, it is a major goal among most automakers, since heavy vehicles simply cannot compete with a lightweight model's fuel economy. Thinking this way, this paper shows a case study where the Size Optimization technique is applied to a front floor reinforcement. The reinforcement is used by two different vehicles, a subcompact and a crossover Sport Utility Vehicle (SUV), increasing the problem complexity. The Size Optimization technique is supported by Finite Element Method (FEM) tools. FEM in Computer Aided Engineering (CAE) is a numerical method for solving engineering problems, and its use can help to optimize prototype utilization and physical testing.

The stabilizer bar attachments problem can not be simply analyzed by using linear FEA methodology. The large deformation in the bushing, the elastic-plastic material property in the bushing retainer bracket, and the contact between different parts all add complexity to the problem and result in the need for an analysis method using a non-linear code, such as ABAQUS. The material properties of the bushing were experimentally determined and applied to the CAE model. It was found that using strains to estimate the fatigue life was more accurate and reliable than using stress. Many modeling techniques used in this analysis were able to improve analysis efficiency.

The driver judges his vehicle based on subjective aspects. Vehicle dynamics characteristics including ride and handling have a major impact on this evaluation. For this reason, vehicle manufactures have grown investments in order to improve vehicle dynamics behavior. Subjective evaluation and customer satisfaction research show which dynamic characteristics need to be improved. CAE models, after being validated based on experimental measures, give a good insight on vehicle dynamic behavior and guide change proposals. At end, new subjective evaluations and measures are carried out in order to check the real improvement of CAE proposals. This work shows the use of the described methodology for a pickup vehicle dynamics evaluation. One of the major complains of pickup drives is related to ride quality. Thinking of that feature the evaluation process considers several phenomena, such as abruptness, front topping, front bottoming, head toss and rear aftershake.

Usually in Computer Aided Engineering (CAE) analyses, Computer Aided Design (CAD) data is meshed and analyzed with regard to displacements and stresses. So far, it is not common to account for residual stresses due to the manufacturing process in these analyses. This work proposes a methodology based on simplified abaqus Standard/Explicit models to evaluate residual stresses due to stamping and bending manufacturing process in truck rails and suggests a methodology to use this residual stress data in truck frame CAE durability analysis making it possible to compare how different a predicted fatigue life can be when residual stresses are considered.

The development costs that new design requires are subject to everyday discussions and saving opportunities are mandatory. Using CAE to predict design changes can avoid excessive costs with prototypes parts, considering the high reliability those current mathematical models can provide. This paper presents the methodology used during the development of a parabolic leaf spring for the rear suspension of a commercial truck, considering mainly the parabolic profiles and stress distribution on the leaves, calculated using CAE software (ANSYS) and experimental tests to measure the actual stress on each leaf, certifying the correlation between computational calculations and real stress on the parts during bench and vehicle evaluations.

The growing competition of the automotive market makes more and more necessary the reduction of development time and consequently, the increase of the capacity to quickly respond to the launching of the competitors. One of the most costly phases on the vehicle development process is the field durability test, both in function of the number of prototypes employed and the time needed to its execution. More and more diffused, the fatigue life prediction methods have played an important part in the durability analysis via CAE. Nevertheless, in order they can be reliable and really being able to reduce the development time and cost, they need to be provided with load cases that can accurately represent the field durability tests. This work presents a CAE approach used for light trucks in order to get a reasonable understanding of component durability behavior due to payload increase. In general, road load data is not available for a new payload condition.

Ford's continued effort to improve fuel economy in automotive applications has emphasized the need for lightweight components that retain all the toughness associated with Ford truck vehicle characteristics. The application of an impact extrusion process to wheel design and manufacture, for Ford Aerostar, provides strength, performance and style more efficiently than other traditional processes. It results in a valuable 33% weight saving over comparable HSLA steel wheels, and provides the customer with uncompromised value. The Ford Aerostar Impact Extruded Aluminum Wheel was designed to be of one-piece construction, manufactured from a less than 1″ thick aluminum wafer-shaped blank. The process permits manufacture in half the steps of a conventional stamped steel wheel, and eliminates extensive machining required with forged or cast aluminum wheels.

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved.