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As the world's largest auto producer and consumer, China is both the most promising and complex market given the country's rapid economic growth, huge population, and many regional and segment preference differences. This research is aimed at developing data-driven demand models for customer preference analysis and prediction under a competitive market environment. Regional analysis is first used to understand the impact of geographical factors on customer preference. After a comprehensive data exploration, a customer-level mixed logit model is built to shed light on fast-growing vehicle segments in the Chinese auto market. By combining the data of vehicle purchase, consideration, and past choice, cross-shopping behaviors and brand influence are explicitly modeled in addition to the impact of customer demographics, usage behaviors, and attributes of vehicles.

For achieving viable mass customization of products, product configuration is often performed that requires deep understanding on the impact of product features and feature combinations on customers’ purchasing behaviors. Existing literature has been traditionally focused on analyzing the impact of common customer demographics and engineering attributes with discrete choice modeling approaches. This paper aims to expand discrete choice modeling through the incorporation of optional product features, such as customers’ positive or negative comments and their satisfaction ratings of their purchased products, beyond those commonly used attributes. The paper utilizes vehicle as an example to highlight the range of optional features currently underutilized in existing models. First, data analysis techniques are used to identify areas of particular consumer interest in regards to vehicle selection.

A robust design procedure is applied to achieve improved vehicle handling performance as an integral part of simulation-based vehicle design. This paper presents a hybrid robust design method, the robust design process strategy (RDPS), which makes full use of the intense complementary action of characteristics between the Response Surface Methodology (RSM) and the Taguchi method, to get the robust design of the vehicle handling performance. The vehicle multi-body dynamic model is built in the platform that is constructed by the software of iSIGHT, ADAMS/CAR, and MATLAB. The design-of-experiment method of the Latin Hypercube (LHC) is used to obtain the approximate area values, and then the RDPS is utilized to achieve improved vehicle handling performance results. The validation is made by the Monte Carlo Simulation Technique (MCST) in terms of the effectiveness of the RDPS in solving robust design problems.

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties.

To design and optimize the vehicle driveline is necessary to decrease the fuel consumption and improve dynamic performance. This paper describes a methodology to optimize the driveline design including the axle ratio, transmission shift points and transmission shift ratios considering uncertainty. A new and flexible tool for modeling multi-domain systems, Modelica, is used to carry out the modeling and analysis of a vehicle, and the multi-domain model is developed to determine the optimum design in terms of fuel economy, with determinability. Secondly, a robust optimization is carried out to find the optimum design considering uncertainty. The results indicate that the fuel economy and dynamic performance are improved greatly.

This paper described a survey that was designed to examine the seat discomfort and travel time factors for Dutch truck driver seat to reduce discomfort. Truck drivers were at risk of body discomfort for long hours sitting, and experience significant discomfort at different body parts. For the truck seat questionnaire, 217 Dutch truck drivers completed self administered questionnaires. Statistical methods were applied to analyze the surveys. Body discomfort level after one hour and five hours were studied. The outcomes from the analytical results were important and required more attention to reduce the body discomfort for long hour sitting.

Hydraulic power steering system, which can reduce the steering hand force by applying the output from a hydraulic actuator, has been widely used in vehicles. In this paper, a detailed steer model including steering column, steering trapezium, and detailed hydraulic power steering system which is consisting of steering cylinder, relief valve, rotary valve, pump and hydraulic lines were established, and a multi-body model of a heavy truck was established to connect with the hydraulic power steering system. Modelica simulation language, which can be efficiently used to investigate multi-domain problems, was used to in the modeling and simulation of the power steering system and the vehicle. The simulation was carried out to identify the effects of design variables on the lateral stability of the vehicle. The application of Modelica for investigating multi-domain problems is also demonstrated.

Hydraulic systems are popular on vehicles, such as power steering, shock absorbers, brakes, etc. Many previously works have been done on the modeling and simulation of the hydraulic systems. However, these models and parameters are usually established on the basis of plans, drawings, measurements, observations, experiences, expert knowledge and standards, and so on. In general, certain information and precise values do not exist. Uncertainty may result, e.g., from human mistakes and errors in the manufacture, from the use and maintenance of constructions, from expert evaluations, and from a lack of information. Actually, many uncertain factors will lead to great errors, and may have great effect on the hydraulic system, so the research on the hydraulic system under uncertainties is very necessary. In this paper, fuzzy algorithm is introduced to analysis the response of the hydraulic system with uncertain parameters.

Metamodeling approach has been widely used due to the high computational cost of using high-fidelity simulations in engineering design. Interpretation of metamodels for the purpose of design, especially design under uncertainty, becomes important. The computational expenses associated with metamodels and the random errors introduced by sample-based methods require the development of analytical methods, such as those for global sensitivity analysis and uncertainty propagation to facilitate a robust design process. In this work, we develop generalized analytical formulations that can provide efficient as well as accurate global sensitivity analysis and uncertainty propagation for a variety of metamodels. The benefits of our proposed techniques are demonstrated through vehicle related robust design applications.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

The automatic mechanical transmission (AMT) was designed by automobile manufacturers to provide a better driving experience, especially in cities where congestion frequently causes stop-and-go traffic patterns. It uses electronic sensors, processors, hydraulic or pneumatic actuators execute clutch actuation and gear shifts on the command of the driver. Such systems coupled with various physical domains have great influence on the dynamic behavior of the vehicle, such as shift quality, driveability, acceleration, etc. This paper presents a detailed AMT model composed of various components from multi-domains like mechanical systems (clutch, gear pair, synchronizer, etc.), pneumatic actuator systems (clutch actuation system, gear select actuation system, gear shift actuation system, etc.). Various components and subsystem models, such as the vehicle, engine, AMT, wheels, etc., are integrated into an overall vehicle system model according to the transmission power flow and control logic.

On-center handling is one of the most important test conditions which are used to evaluate the handling performance of both passenger cars and commercial vehicles. This paper aims at investigating and verifying the influence of parameters on on-center handling of articulated trucks. A full vehicle model, including the steering system, suspension system, cab, frame, trailer and so on, was established in first by measuring the parameters of each component. The comparison of simulation and test results shown that the simulation precision of the vehicle model was up to 80%. Based on the model, the influence analysis of parameters, such as stiffness of steering drag link, steering ratio, kingpin friction, were carried out and were verified through the handling test. The analysis results indicated that larger stiffness of steering drag link, smaller gear ratio could enhance the steer sensitivity and steer stiffness, small kingpin friction is beneficial to the steering return ability.

The thermal management system of the water medium retarder using engine coolant (water and ethylene glycol) as transmission medium, omits oil-water heat exchanger in the structure. When the hydraulic retarder is operated, the valve is connected with the retarder and water pump, and then the engine coolant enters the working chamber. The kinetic energy of the vehicle is converted into internal energy of the coolant, and the heat is discharged to the external environment through the engine thermal management system. The braking torque of the water medium hydraulic retarder is determined by the water medium flow rate in the working chamber. The smaller the valve opening degree, the greater the braking torque and the faster the heating transmission fluid. Small valve opening is not conducive to the loss of heat. It will affect the normal working of the engine and hydraulic retarder.

Ride comfort is simply defined as the vibration performance of the vehicle which is excited by road surface roughness, generally as the vehicle moves at specific constant velocity over the road profile. Ride comfort was an important index for heavy truck, due to long distance transfer and long time driving. In order to improve the ride comfort of a heavy truck, a detailed model, including flex frame, chassis suspension, cab suspension, powertrain, etc., was built and assembled by MSC.ADAMS software. Simulation and testing data were consistent very well, which showed the correctness of the model. The optimization of chassis and cab suspension including the stiffness of the leafspring, the damping of the shock absorber, etc. was carried out to improve the ride comfort of the vehicle. The ride comfort testing was carried out on the proving ground to verify the effectiveness the optimization results. The testing results shows that the ride comfort has been improved after tuning.

Sensitivity analysis plays an important role to help engineers gain knowledge of complex model behaviors and make informed decisions regarding where to spend engineering effort. In design under uncertainty, probabilistic sensitivity analysis (PSA) is performed to quantify the impact of uncertainties in random variables on the uncertainty in model outputs. One of the most challenging issues for PSA is the intensive computational demand for assessing the impact of probabilistic variations. An efficient approach to PSA is presented in this article. Our approach employs the Kolmogorov-Smirnov (KS) distance to quantify the importance of input variables. The saddlepoint approximation approach is introduced to improve the efficiency of generating cumulative distribution functions (CDFs) required for the evaluation of the KS distance.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Diesel emission aftertreatment system is usually designed to meet stringent packaging constraints, rendering a difficult situation to achieve perfect flow distribution inside the catalytic unit. The non-uniform flow pattern leads to a mal-distribution of flow velocity, temperature, and gas species in catalyst unit. Some catalysts are exposed to harsh working environment, while the rest catalysts are underutilized. This lowers the efficiency of overall catalyst unit and thus requires an oversized system to meet emission requirements. The flow mal-distribution also accelerates the uneven catalyst degradation, lowering the system durability. Hence, a quantitative description of packaging impact on catalyst performance is critical to assess the system efficiency and durability. In the present work, a mapping method is developed to combine catalyst performance with computational fluid dynamics (CFD) simulation.

This paper presents the relationship between suspension and steering systems and wheels, and proposes the vehicle dynamics modeling method. A vehicle dynamics model combined with the suspension K&C test data of a concrete vehicle was built based on the method. The simulation results show that the method is correct and feasible, and the dynamics model performed characteristics of the suspension and steering systems with high precision can be used for the followup simulation and optimization.

Transmission gear whine noise is one of the main noise problems in powertrain NVH, which is caused by dynamic meshing force of gear pairs, it acts as transmission error. Due to the coupling effects of transmission gears, shaft, bearings and housing, it needs comprehensive management from many aspects to solve the problem of gear whine noise. Aiming at gear whine noise of a 4-speed AMT used in electric bus, the main noise sources is identified by using the order tracking analysis approach firstly. Secondly, gear misalignment and contributions of system deformation to the misalignment is analyzed by means of simulation tools, and the factor is taken into account in the subsequent gear modification design. At last, based on the improved Smith slice method, the calculation model of transmission error of helical gears is established.

The leaf spring has significant hysteresis characteristics due to the interleaf friction. The traditional three-link model could not simulate the hysteresis characteristics at all. According to the dynamic load test results one can find that the dynamic stiffness of leaf spring has a nonlinear relationship with the travel distance and the load frequency has a tiny influence on it. Based on the traditional three-link model, this paper proposed a simulation modeling method by introducing torsional friction on the revolute joints. The key parameters including torsional spring stiffness, friction torque preload, stiction transition velocity and max stiction deformation are optimized by combining the ADAMS and OPTIMUS. The comparison analysis between the simulation and test results of front and rear leaf springs have revealed that the maximum average errors are 4.84% and 6.41%, respectively.