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In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.

This research focuses on co-cations modified Cu-zeolite catalyst CuCe/SSZ-13. The NOx conversion and hydrothermal stability of fresh and aged Cu/SSZ-13 and CuCe/SSZ-13 were evaluated to conclude the impact of Ce on the zeolite stability and mechanism of NH3-SCR reaction. For fresh samples, CuCe/SSZ-13 exhibited more than 80% efficiency at 225 °C to 600 °C, and showed higher NOx conversion below 225 °C and above 450 °C than Cu/SSZ-13. For aged catalysts, CuCe/SSZ-13 exhibited higher efficiency at all test temperatures than Cu/SSZ-13, and exhibited over 80% conversion at 225 °C to 350 °C, whereas, that was only 250 °C to 300 °C over fresh Cu/SSZ-13 sample. Furthermore, the effect of co-cation Ce on the phase structure changes, acid sites, redox capacity, transformation of various Cu species, and framework stability of the samples were evaluated by several techniques, such as TPD, TPO, TPR, BET, and XRD.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

People and their communities are looking for transportation solutions that reduce travel time, improve well-being and accessibility, and reduce emissions and traffic congestion. Although new mobility services like ride-hailing advertise improvements in these areas, closer inspection has revealed a discrepancy between industry claims and reality. Key decision-makers, including citizens, cities and enterprise, and mobility service providers have the opportunity to leverage connected vehicle and connected device data through cloud-based APIs. We propose a GHG data analytics framework that functions on top of a cloud platform to provide unique system-level perspectives on operating transportation services, from procuring the most environmentally and people friendly vehicles to scheduling and designing the services based on data insights.

Aiming at the high altitude operation problems for piston-type aero-engines and to improve the practical ceiling and high altitude dynamic performance, this thesis analyzes a controllable three-stage composite supercharging system, using a two-stage turbocharger coupled supercharger method. The GT-Power simulation model of a four-cylinder boxer engine was established, and the control strategy of variable flight height was obtained. The simulation research of engine performance from 0 to 20,000 meters above sea level has been carried out, which shows that the engine power is at the same level as the plain condition, and it could still maintain 85.28 percent of power even at the height of 20,000 meters, which meets the flight requirements of the aircraft.

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.

Over half of the greenhouse gas (GHG) emissions in the United States come from the transportation and electricity generation sectors. To analyze the potential impact of cross-sector cooperation in reducing these emissions, we formulate a bi-level optimization model where the transportation sector can purchase renewable energy certificates (REC) from the electricity generation sector. These RECs are used to offset emissions from transportation in lieu of deploying high-cost fuel efficient technologies. The electricity generation sector creates RECs by producing additional energy from renewable sources. This additional renewable capacity is financed by the transportation sector and it does not impose additional cost on the electricity generation sector. Our results show that such a REC purchasing regime significantly reduces the cost to society of reducing GHG emissions. Additionally, our results indicate that a REC purchasing policy can create electricity beyond actual demand.

Three-phase sequential turbocharging system with two unequal-size turbochargers is developed to improve fuel economy performance and reduce emission of the automotive diesel engine, which satisfies wide range of intake flow demand. However, it results in complicated transient control strategies under frequently changing operating conditions. The present work aims to optimize the control scheme of boost system and fuel injection and evaluate their contributions to the improvement of transient performance. A mean value model for diesel engine was built up in SIMULINK environment and verified by experiment for transient study. Then a mathematical model of optimization issue was established. Strategies of control valves and fuel injection for typical acceleration and loading processes are obtained by coupled calculating of the simulation model and optimization algorithm.

Multidisciplinary Design Optimization (MDO) has been widely used in the automotive industry to balance overall weight and stringent vehicle attributes, such as safety, NVH, durability, etc. To improve product quality and shorten product development cycle, a comprehensive MDO-based platform for vehicle attribute integration is developed in this paper. Some key issues in the platform development are addressed: Parameter model synchronization, Metamodel predictive capabilities, and Pre/post processing, etc. In addition, a strategy for body design is proposed to achieve weight targets while meeting other vehicle attributes. Lastly, the proposed methodology is demonstrated by a real world example for vehicle body design.

The level of boost pressure has a significant effect on optimizing the steady-state and transient performance of turbocharged diesel engines. However the problem of matching the wide speed range diesel engine and the high pressure turbocharging system has to be resolved. The regulated two-stage (RTS) system is an effective method to improve the fuel economy, transient response and smoke emissions. Compared with the difficult matching problem of the RTS system, the problem of boost pressure control is more complex due to the frequently changing operating conditions. To overcome the limitations of an open-loop control strategy, a closed-loop boost pressure control strategy was studied numerically using a mean value model of a diesel engine with RTS system. The system identification was conducted for the transient response from the turbine bypass opening command to the boost pressure.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

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.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

Response Surface Model (RSM)-based optimization is widely used in engineering design. The major strength of RSM-based optimization is its short computational time. The expensive real simulation models are replaced with fast surrogate models. However, this method may have some difficulties to reach the full potential due to the errors between RSM and the real simulations. RSM's accuracy is limited by the insufficient number of Design of Experiments (DOE) points and the inherent randomness of DOE. With recent developments in advanced optimization algorithms and High Performance Computing (HPC) capability, Direct Multidisciplinary Design Optimization (DMDO) receives more attention as a promising future optimization strategy. Advanced optimization algorithm reduces the number of function evaluations, and HPC cut down the computational turnaround time of function evaluations through fully utilizing parallel computation.

In the internal combustion engine, about 25%-40% of the energy released by burned fuel is taken away by the exhaust gas. The part of the usable energy in the exhaust can be used in the turbocharged engine. So, at present, turbocharged diesel engine hasn't made full use of exhaust gas energy. The authors propose a model of the 4-stroke turbocharged diesel engine of split exhausting system. Adding a rapidly on-and-off exhaust control valve between exhaust passage and manifold in the 4-stroke turbocharged diesel engine can improve the utilization rate of the usable energy in the exhaust. By utilizing the mean effective pressure (MEP), this paper is to calculate the maximum usable energy, the energy provided by exhaust and the energy required by intake. The results gets that the new type of exhausting system can help engine to increase usage rate of the exhaust gas energy to around 20% at the rated condition compared to the existing vehicle diesel engines with VNT.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.