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The design optimization of vehicle body structure is addressed to reduce interior noise and improve customer satisfaction in this paper. The structural-acoustic model is developed by using finite element method. The frequency response of structural-acoustic system is computed by modal analysis method. The optimization problem is constructed to minimize the sound pressure level in the right ear of the driver. The sensitivity analysis is carried out to find the key panels to be optimized as design variables and improve the efficiency of optimization computation. Response Surface Method (RSM) is utilized to develop the surrogate model and optimize the vehicle Noise Vehicle and Harshness (NVH) behavior. A 9dB reduction of sound pressure level (SPL) in the right era of the driver is obtained through geometric optimization for panels. Furthermore, the topology optimization model is developed to search the optimal layout of constrained layer damping treatments in the front floor.

The design optimization of vehicle body structure is addressed to reduce interior noise and improve customer satisfaction in this paper. The structural-acoustic model is established and the response of sound pressure in frequency domain is obtained by using finite element method. The minimization of sound pressure near the driver's right ear depends on the geometry of vehicle body structure and the layout of damping treatments. The panel participation analysis is performed to find out the key panels as design variables and improve the efficiency of optimization computation. Response Surface Method (RSM) is utilized to optimize the vibro-acoustic properties of vehicle body structure instead of complex structural-acoustic coupling finite element model. Geometric optimization problem of panels is described and solved to minimize the interior noise in vehicle.

In order to measure the noise of auto shock absorbers, a test bench used to detect piston-rod vibration responses of shock absorbers and measuring analyzer named SANTS-I were developed. The vibration response data was detected by bench tests, which shows that there are high-frequency violent peaks on the sine curve of piston-rod oscillating with relative low frequency. In order to explain the interior work dynamic mechanism of shock absorbers, a schematic Micro-process Dynamic Model with 10 steps particularly divided extension and compression stroke in more detail, and dynamic differential equations for each step were presented and discussed. Furthermore, numerical simulation for the inner impacts interaction between piston and damping fluid of hydraulic shock absorber was realized by ADINA software, by the establishment of a gas-liquid two-phase finite element model.

Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs.

Function and performance test of automated vehicles in the closed field is a necessary way to verify their safety, intelligence and comfort. The design and number of test scenarios will influence if the automated vehicles can be tested and evaluated effectively and fast. Based on the interrelationship among the vehicle, driver’s (or control system) driving strategy and road, we use the permutation and combination method to compare the relative position and movement relations between an automated vehicle (vehicle under test) and the surrounding vehicles to generate a total possible test scenarios group.

In order to improve the comprehensive protection for children with variable shapes and sizes, this paper conducted studies on the impact injury for obese children based on a 10-YO finite element model. Some specific geometrics on the body surface were firstly acquired by the combination of pediatric anthropometric database and generator of body (GEBOD). A Radial Basis Function (RBF) based mesh morphing technique was then used to modify the original standard size FE model using the obtained geometrics. The morphed FE model was validated based on the experimental data of frontal sled test and chest-abdomen impact test. The effects of obesity on injury performances were analyzed through simplified high-speed and low-speed crash simulations.

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.

In order to improve the performance of electro-hydraulic composite braking system of hybrid electric vehicle (HEV), a new type of plug-in HEV with dual motor was taken as the research object. The model of motor loss was built to achieve maximum motor efficiency, and the hydraulic braking system model, which can dynamically control pressure, was built. Based on the optimization of a motor’s continuously variable transmission (CVT) joint efficiency, the real-time optimal allocation strategy based on threshold method and cooperative control strategy of the electro-hydraulic composite braking system were brought out to recover most of the regenerative energy under the premise of ensuring safety. The model was built to verify the performance by AMESim-Simulink.

To reduce the computational time of the iterations in robust design, meta-models are frequently utilized to approximate time-consuming computer aided engineering models. However, the bias of meta-model uncertainty largely affects the robustness of the prediction results, this uncertainty need to be addressed before design optimization. In this paper, an efficient uncertainty quantification method considering both model and parameter uncertainties is proposed. Firstly, the uncertainty of parameters are characterized by statistical distributions. The Bayesian inference is then performed to improve the predictive capabilities of the surrogate models, meanwhile, the model uncertainty can also be quantified in the form of variance. Monte Carlo sampling is finally utilized to quantify the compound uncertainties of model and parameter. Furthermore, the proposed uncertainty quantification method is used for robust design.

Developing pure electric powertrains have become an important way to reduce reliance on crude oil in recent years. This paper concerns energy management of dual motor-driven electric vehicles. In order to obtain a predictive energy management strategy with good performance in computation and energy efficiency, we propose a hybrid algorithm that combines model predictive control (MPC) and convex programming to minimize electrical energy use in real time control. First, few changes are occurred in original component models in order to convert the original optimal control problem into convex programming problem. Then convex optimization algorithm is used in the prediction horizon to optimize torque allocation between two electric motors with different size. To verify the effectiveness of the hybrid algorithm, a real city driving cycle is simulated. Furthermore, different predictive horizons are performed to illustrate the robustness and time efficiency of the proposed method.

Automotive powertrain mounting system (PMS) plays a key role in the vibration isolation and the comfort improvement in vehicle. So far, most of powertrain is modeled as a rigid body in 6 Degrees of Freedom (DOF) in research. Few comprehensive and overall optimization are considered which addresses the excitation of the powertrain, the vibration and noise inside the body and the transmission path of vibration together. In this paper, a 13-DOF model including automotive powertrain mounting system and the full vehicle is developed in order to achieve comprehensive and overall optimization for PMS. The minimum of vertical vibration at seat track and the noise at driver ear on the right side, the maximum of system's vibration isolation rate and the energy decoupling rate, the reasonable allocation of system natural frequencies are considered as the optimization targets. Genetic algorithm is used to solve the multi-objective optimization problem.

The performances of heavy-duty natural gas engines have been limited by combustion temperature and NOx emissions for a long time. Recently, water injection technology has been widely considered as a technical solution in reducing fuel consumption and emissions simultaneously in both gasoline and diesel engines. This paper focuses on the impacts of intake manifold water injection on characteristics of combustion and emissions in a natural gas heavy-duty engine through numerical methods. A computational model was setup and validated with experimental data of pressure traces in a CFD software coupled with detailed chemical kinetics. The simulation was mainly carried out in low-speed and full-load conditions, and knock level was also measured and calculated by maximum amplitude of pressure oscillations (MAPO).

Several surrogate models such as response surface model and radial basis function and Kriging models are developed to speed the optimization design of vehicle body and improve the vehicle crashworthiness. The error analysis is used to investigate the accuracy of different surrogate models. Furthermore, the Kriging model is used to fit the model of B-pillar acceleration and foot well intrusion. The response surface model is used to fit the model of the entire vehicle mass. These models are further used to calculate the acceleration response in B-pillar, foot well intrusion and vehicle mass instead of the finite element model in the optimization design of vehicle crashworthiness. A multi-objective optimization problem is formulated in order to improve vehicle safety performance and keep its light weight. The particle swarm method is used to solve the proposed multi-objective optimization problem.

Compression molded SMC composed of chopped carbon fiber and resin polymer which balances the mechanical performance and manufacturing cost presents a promising solution for vehicle lightweight strategy. However, the performance of the SMC molded parts highly depends on the compression molding process and local microstructure, which greatly increases the cost for the part level performance testing and elongates the design cycle. ICME (Integrated Computational Material Engineering) approaches are thus necessary tools to reduce the number of experiments required during part design and speed up the deployment of the SMC materials. As the fundamental stage of the ICME workflow, commercial software packages for SMC compression molding exist yet remain not fully validated especially for chopped fiber systems. In the present study, SMC plaques are prepared through compression molding process.

The formation of fuel film in the combustion cylinder affects the mixing process of the air and the fuel, and the process of the combustion propagation in engines. Some models of film evaporation have been developed to predict the evaporation behavior of the film, but rarely experimental results have been produced, especially when the temperature is high. In this study, the evaporation behavior of the film of different species of oil and their blends at different temperature are observed. The 45 μL films of isooctane, 1-propanol, 1-butanol, 1-pentanol, and their blends were placed on a quartz glass substrate in the closed temperature-controlled chamber. The shape change of the film during evaporation was monitored by a high-speed camera through the window of the chamber. First, the binary blends film of isooctane and one of the other three oils were evaporated at 30 °C, 50 °C, 70 °C and 90 °C.

Road load data is an essential input to evaluate vehicle durability and strength performances. Typically, load case of pothole impact constitutes the major part in the development of structural durability. Meanwhile, misuse conditions like driving over a curb are also indispensable scenarios to complement impact strength of vehicle structures. This paper presents a methodology of establishing Multi-body Dynamics (MBD) full vehicle model in Adams/Car to acquire the road load data for use in durability and strength analysis. Furthermore, load level between durability and misuse conditions of the same Impact road was also investigated to explore the impact due to different driving maneuvers.

Surrogate model based design optimization has been widely adopted in automotive industry. Hybrid surrogate model with multiple component surrogates is considered to be a better choice when simulating highly non-linear responses in vehicle crashworthiness analysis. Currently, the number of component surrogates has to be decided before-hand when constructing of a hybrid surrogate model. This paper conducts a comparative study on the performances of three popular hybrid modeling methods including heuristic computation strategy, and two kinds of optimal weighted surrogates. The effects of samples size on the number of individual surrogates that should be included into the final hybrid surrogate models for crashworthiness responses are investigated. Different hybrid modeling techniques and multiple validation criteria are evaluated. Some observations and conclusions on the selection of component surrogates in hybrid surrogate modeling are given in the end.

As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

Due to the design of lightweight, high speed driveline system, the coupled bending and torsional vibration and rotordynamics must be considered to predict vibratory responses more realistically. In the current analysis, a lumped parameter model of the propeller shaft is developed with Timoshenko beam elements, which includes the effect of rotary inertia and shear deformation. The propeller shaft model is then coupled with a hypoid gear pair representation using the component mode synthesis approach. In the proposed formulation, the gyroscopic effect of both the gear and propeller shaft is considered. The simulation results show that the interaction between gear gyroscopic effect and propeller shaft bending flexibility has considerable influence on the gear dynamic mesh responses around bending resonances, whereas the torsional modes still dominate in the overall frequency spectrum.

Brake squeal is an instability issue with many parameters. This study attempts to assess the effect of thermal load on brake squeal behavior through finite element computation. The research can be divided into two parts. The first step is to analyze the thermal conditions of a brake assembly based on ANSYS Fluent. Modeling of transient temperature and thermal-structural analysis are then used in coupled thermal-mechanical analysis using complex eigenvalue methods in ANSYS Mechanical to determine the deformation and the stress established in both the disk and the pad. Thus, the influence of thermal load may be observed when using finite element methods for prediction of brake squeal propensity. A detailed finite element model of a commercial brake disc was developed and verified by experimental modal analysis and structure free-free modal analysis.