Automotive Vehicle Design Research Articles

Electromechanical processes in electric power steering system based on permanent magnet synchronous motor

Relevance. Electric power steering of vehicles successfully competes with hydraulic boosters of similar purposes, showing high efficiency, energy saving and environmental friendliness. Aim. To study and research electromechanical processes of electric power steering system based on permanent magnet synchronous motor. Methods. Mathematical and numerical modeling using the Matlab/Simulink software package. Results. The article discusses the operating modes of an electric power steering based on an electric motor with permanent magnets. The authors analyzed two classic schemes for constructing steering systems with electric power steering – with the electric motor placed on the steering column and on the steering rack. Block diagrams of the mechanical and electrical parts of the steering system have been developed. Based on mathematical dependencies characterizing the operating modes of the steering system, a complete dynamic simulation model of the electric power amplifier was obtained on the MATLAB/Simulink platform. Based on a preliminary analysis of control methods and algorithms in the system to solve the problem of control stability, PI controllers with low-pass filters are implemented to regulate the current and voltage of the electric motor depending on the speed of the vehicle and the angle of rotation of the steering wheels. The simulation results were used to design a real electric power steering for a specific vehicle. Conclusions. The complete simulation model of an electric booster based on a synchronous motor with permanent magnets in a car steering system obtained by the authors adequately reflects the dynamic control processes and can be used in the design of automotive vehicles.

Statistical Model Verification and Validation Concept in Automotive Vehicle Design

Abstract The automotive design process is highly complex and is getting more diverse. With increasing possibilities in modeling, computing and analyzing, simulation driven design is essential to support this process. It has the advantage to reduce costs, time and the risk of wrong decisions. To achieve these benefits verification and validation is crucial, as they define the trustworthiness of simulation models. In the automotive domain, modeling and simulation is growing rapidly, while efforts for the theoretical examination of verification and validation are decreasing. There is a lack in describing the models’ credibility, because conventional validation neglects uncertainties. Since uncertainty in vehicle production is mostly documented especially in times of Industry 4.0, this potential is not used. To fill the gap, this paper develops a concept of a statistical framework, which integrates uncertainties in the verification and validation process of total vehicle simulations with Monte-Carlo sampling. Uncertainties of parameters due to measuring error and calibration, numerical and model uncertainties are considered. The application of the statistical framework in a longitudinal consumption simulation proves that uncertainty can be introduced through statistical validation. The evaluation of the framework demonstrates that it improves the reliability quantification of models and their simulation results. The increased knowledge about the product characteristics supports the simulation driven design process, reduces the risk of wrong decisions and raises the sustainability of products. It provides the basis for statistical validation in vehicle simulation to support transparent decision-making in the automotive design process.

Optimization of Magnesium Alloy Wheel Dynamic Impact Performance

Designing lightweight and comfortable automotive vehicles is a primary aim of the industry. Lightweight wheel designs can have a negative effect on the dynamic impact performance of the wheel; therefore, striking a balance between these two factors is a key objective in the design of automotive vehicles. Magnesium alloy wheels were investigated as magnesium alloy has damping performance advantages over some metal materials. Damping test methods were designed to establish the damping performance parameters of the magnesium alloy material. A finite element analysis model of magnesium alloy wheels was established with certain boundary conditions and constraints. The applicability of the model was verified by free modal evaluation of the wheel. Dynamic impact simulation analysis of the designed wheels was carried out, and the dynamic speed responses of magnesium alloy wheels under the impact of a dynamic load on the road surface were obtained. Comparison of the dynamic impact performance of magnesium and aluminum alloy wheels with the same structure showed that the magnesium alloy wheel achieved the target weight reduction of 32.3%; however, the dynamic impact performance was reduced. In order to realize the lightweight design, the dynamic impact performance of the magnesium alloy wheel should not be inferior to that of the aluminum alloy wheel; therefore, the design of the magnesium alloy wheel structure was optimized. The structural design optimization of the magnesium alloy wheel was carried out by defining the structural parameters of the wheel and using the acceleration and shock response of the wheel as the outputs. The optimization of weight reduction and dynamic impact performance of magnesium alloy wheels was achieved. Consequently, the designed magnesium alloy wheel was shown to have improved ride comfort while satisfying wheel structural performance standards and providing lightweight design.

Multiple Target Exploration Approach for Design Exploration Using a Swarm Intelligence and Clustering

In engineering design problems, performance functions evaluate the quality of designs. Among the designs, some of them are classified as good designs if responses from performance functions satisfy a target point or range. An infinite set of good designs in the design space is defined as a solution space of the design problem. In practice, since the performance functions are analytical models or black-box simulations which are computationally expensive, it is difficult to obtain a complete solution space. In this paper, a method that finds a finite set of good designs, which is included in a solution space, is proposed. The method formulates the problem as optimization problems and utilizes gray wolf optimizer (GWO) in the way of design exploration. Target points of the exploration process are defined by clustering intermediate solutions for every iteration. The method is tested with a simple two-dimensional problem and an automotive vehicle design problem to validate and check the quality of solution points.

The prediction of measurement variability in an automotive application by the use of a coherence formulation

Variability between nominally identical vehicles is an ever-present problem in automotive vehicle design. In this paper, it is shown that it is possible to quantify and, therefore, separate the measurement variability arising from a number of tests on an individual vehicle from the vehicle-to-vehicle variability arising from the manufacturing process with a series of controlled experiments. In this paper, coherence data is used to identify the measurement variability and, thus, to separate these two variability sources. In order to illustrate the methodology, a range of nominally identical automotive vehicles have been tested for NVH (noise, vibration and harshness) variability by exciting the engine mount with an impact hammer and measuring the excitation force and corresponding velocity responses at different points on the vehicle. Normalised standard deviations were calculated for the transfer mobility data, giving variability values of 25.3%, 33.5% and 37.3% for the responses taken at the suspension strut, upper A-pillar and B-pillar, respectively. The measurement variability was determined by taking repeat measurements on a single vehicle, and was found to be 2.9%. The measurement variability predicted by the coherence data on the multi-vehicle tests was compared with the directly taken repeat measurements taken on a single vehicle and these were shown to agree well with one another over the frequency range of interest.

State-of-the-knowledge on TWIP steel

High Mn twinning induced plasticity (TWIP) steel is a new type of structural steel, characterised by both high strength and superior formability. TWIP steel offers an extraordinary opportunity to adjust the mechanical properties of steel by modifying the strain hardening. The use of TWIP steel may therefore lead to a considerable lightweighting of steel components, a reduction of material use and an improved press forming behaviour. These key advantages will help implement current automotive vehicle design trends which emphasise a reduction of greenhouse gas emissions and lowering of fuel consumption. In addition, high strength TWIP steel will effectively contribute to weight containment in vehicles equipped with hybrid and electric motors, as these are considerably heavier than conventional motors. The present review addresses all aspects of the physical metallurgy of the high strength TWIP steel with a special emphasis on the properties and key advantages of TWIP sheet steel products relevant to automotive applications.

Perceptual Attributes in Product Design: Fuel Economy and Silhouette-Based Perceived Environmental Friendliness Tradeoffs in Automotive Vehicle Design

The quest for improved fuel efficiency and environmental friendliness is transforming automotive vehicle design. In addition to new energy sources and management, new powertrain technologies offer increased flexibility in the spatial arrangement (packaging) and exterior shape (styling) of a vehicle. Design choices in packaging and styling are closely linked to consumer preferences, particularly those that influence consumers’ decisions about the objective qualities of a product (i.e., perceptual attributes). The ability to include perceptual attributes into a design optimization study is a valuable extension of the more traditional engineering approach that looks at only functional attributes. Previous work has studied the quantification of perceived environmental friendliness (PEF) in vehicle silhouette design. In this paper, empirically validated PEF silhouette attributes are included as constraints in a vehicle optimization model that maximizes fuel economy. Results indicate that there is a tradeoff between PEF preferences and the attainable fuel economy for a given vehicle, where increasing vehicle length leads to increasing PEF and decreasing fuel economy.

Recent research on vehicle noise and vibration

Vehicle noise, vibration and harshness (NVH) is usually among the top five attributes in terms of its priority in the design of automotive vehicles. Its priority in other types of vehicles (e.g., aerospace) is also important. Like other attributes of safety, performance, dynamics, fuel economy, NVH has to be considered closely in the design process. This manuscript presents a summary of recent research in the general area of NVH with an emphasis in the automotive field. It follows up on a previous review and classifies the phenomena by the main sources of NVH into powertrain, road and tyre, wind and other NVH. The last includes brake and chassis, squeak and rattle, electromechanical NVH, exterior (or drive-by) NVH and others. The paper provides a review of some of the recent literature in this field.

Impact detection for smart automotive damage mitigation systems

Occupant safety and severity of vehicle damage are important factors in automotive vehicledesign. Smart automobiles of the future could potentially use distributed smart materialsensors and actuators in order to identify impact and take appropriate evasive or mitigativeactions. This provides the motivation for this study.The first part of this study is focused on detecting the location and magnitude of impact,particularly for the case where the automotive structure is subjected to minimal damage.This is accomplished by developing a generalized algorithm using the Reissner–Mindlinplate theory, the Rayleigh–Ritz energy approach, and the Lagrangian–Hamilton principle.The level of performance of this methodology is demonstrated for impacts on a simplysupported rectangular plate. Different case studies for static as well as impact loading withpoint as well as area contacts are presented. An algorithm using deconvolution foridentifying impact location and magnitude has been developed and implemented.Additionally, the influence of damage on the structural vibratory content is studied via afrequency analysis. Modal analyses for undamaged and damaged plates, with ninedifferent damage locations and six different damage sizes, are performed. Changes infrequency and mode shapes are observed as regards the severity of the damage.

Analytical Target Cascading in Automotive Vehicle Design

Target cascading in product development is a systematic effort to propagate the desired top-level system design targets to appropriate specifications for subsystems and components in a consistent and efficient manner. If analysis models are available to represent the consequences of the relevant design decisions, analytical target cascading can be formalized as a hierarchical multilevel optimization problem. The article demonstrates this complex modeling and solution process in the chassis design of a sport-utility vehicle. Ride quality and handling targets are cascaded down to systems and subsystems utilizing suspension, tire, and spring analysis models. Potential incompatibilities among targets and constraints throughout the entire system can be uncovered and the trade-offs involved in achieving system targets under different design scenarios can be quantified.

ニューロ非線形多変量解析に基づく自動車防錆設計

ニューロ非線形多変量解析に基づく自動車防錆設計