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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

Aiming at the abnormal vibration of driver seat of a passenger car in idle condition, vibration acceleration of engine, frame and seat rail was tested to identify vibration sources. Order tracking and spectrogram analysis indicated that the second order self-excitation of engine was the main cause. To solve the problem, semi-active controlled hydraulic engine mount with air spring of which characteristics could shift between a high dynamic stiffness and a low one was applied. Then the structure and principle of the mount with variable characteristics was introduced and control mode was analyzed. Dynamic characteristics were obtained by bench test. With sample mount applied, vibration of seat rail was tested again in multiple vehicle and engine working conditions. Dates showed that abnormal vibration in idle condition was extremely reduced and the mount could also meet the requirement of engine to dynamic stiffness in driving conditions.

As one of the most important auto-body moving parts, door hinge is the key point of door design and its accessories arrangement, also the premise of the door kinematic analysis. We proposed an effective layout procedure for door hinge and developed an intelligent system on CATIA CAA platform to execute it. One toolbar and five function modules are constructed - Axis Arrangement, Section, Parting Line, Kinematic, Hinge Database. This system integrated geometrical algorithms, automatically calculate the minimum clearances between doors, fender and hinges on sections to judge if the layout is feasible. As the sizes of the clearances are set to 0s, the feasible layout regions and extreme start/end points are shown in parts window, which help the engineer to check the parting line and design a new one. Our system successfully implemented the functions of five modules for the layout of door hinge axis and parting line based on a door hinge database.

In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

Conventional viscous shock absorbers, in parallel with suspension springs, passively dissipate the excitation energy from road irregularity into heat waste, to reduce the transferred vibration which causes the discomfort of passengers. Energy-harvesting shock absorbers, which have the potential of conversion of kinetic energy into electric power, have been proposed as semi-active suspension to achieve better balance between the energy consumption and suspension performance. Because of the high energy density of the rotary shock absorber, a rotational energy-harvesting shock absorber with mechanical motion rectifier (MMR) is used in this paper. This paper presents the assessment of vehicle dynamic performance with the proposed energy-harvesting shock absorber in braking process. Moreover, a PI controller is proposed to attenuate the negative effect due to the pitch motion.

Polyvinyl butyral (PVB) film and SentryGlas® Plus (SGP) film have been widely used in automotive windshield and architecture curtain serving as protective interlayer materials. Viscoelasticity is the unique property of such film materials, which can contribute to improving impact resistance and energy absorbing characteristics of laminated glass. In this study, the uniaxial tensile creep and stress relaxation tests are conducted to investigate the viscoelasticity of PVB and SGP films used in laminated glass. Firstly, tensile creep and stress relaxation tests of PVB film (0.76mm) and SGP film with three thickness (0.89mm, 1.14mm and 1.52mm) are conducted using Instron universal testing machine to obtain creep and stress relaxation curves. Afterwards, both viscoelastic models (Burgers model, Maxwell-Weichert model) and empirical equations (Findley power law, Kohlrausch equation) are applied to simulate the creep and stress relaxation results.

With the increasingly complex traffic environment, the vehicle AEB system needs to go through a large number of testing processes, in order to drive more safely on the road. For speeding up the development process of AEB and solve the problems of long cycle, high cost and low efficiency in AEB testing, in this paper, a millimeter wave radar turntable is built, and a high-precision detection algorithm of turntable encoder is designed, at the same time, a test method of vehicle AEB based on the detection data of radar turntable encoder is designed. The verification results show that methods described in this paper can be used to develop the vehicle AEB test algorithm efficiently.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

Owing to the small size of engines and high injection pressures, it is difficult to avoid the fuel spray impingement on the combustion cylinder wall and piston head in Direct Injection Spark Ignition (DISI) engine, which is a possible source of hydrocarbons and soot emission. As a result, the droplets size and distribution are significantly important to evaluate the atomization and predict the impingement behaviors, such as stick, spread or splash. However, the microscopic behaviors of droplets are seldom reported due to the high density of small droplets, especially under high pressure conditions. In order to solve this problem, a “spray slicer” was designed to cut the spray before impingement as a sheet one to observe the droplets clearly. The experiment was performed in a constant volume chamber under non-evaporation condition, and a mini-sac injector with single hole was used.

Due to the rapid development of road infrastructure and vehicle population in China, the fuel consumption and emission of on-road vehicles tested in China World Transient Vehicle Cycle (C-WTVC) cannot indicate the real driving results. But the test results in China Heavy-duty Commercial Vehicle Test Cycle-Coach (CHTC-C) based on the road driving conditions in China are closer to the actual driving data. In this paper, the model for predicting the performance of heavy-duty vehicles is established and validated. The fuel consumption and NOx emission of a Euro VI heavy-duty coach under C-WTVC and CHTC-C tests are calculated by employing the developed model. Furthermore, the fuel consumption of the test coach is optimized and its sensitivity to the driving factors is analyzed.

A computational fluid dynamics (CFD) guided design optimization was conducted for the piston bowl geometry for a heavy-duty diesel engine. The optimization goal was to minimize engine-out NOx emissions without sacrificing engine peak power and thermal efficiency. The CFD model was validated with experiments and the combustion system optimization was conducted under three selected operating conditions representing low speed, maximum torque, and rated power. A hundred piston bowl shapes were generated, of which 32 shapes with 3 spray angles for each shape were numerically analyzed and one optimized design of piston bowl geometry with spray angle was selected. On average, the optimized combustion system decreased nitrogen oxide (NOx) emissions by 17% and soot emissions by 41% without compromising maximum engine power and fuel economy.

Lean-burn combustion is an effective method for increasing the thermal efficiency of gasoline engines fueled with stoichiometric fuel-air mixture, but leads to an unacceptable level of high cyclic variability before reaching ultra-low nitrogen oxide (NOx) emissions emitted from conventional gasoline engines. Multi-point micro-flame ignited (MFI) hybrid combustion was proposed to overcome this problem, and can be can be grouped into double-peak type, ramp type and trapezoid type with very low frequency of appearance. This research investigates the micro-flame ignition stages of double-peak type and ramp type MFI combustion captured by high speed photography. The results show that large flame is formed by the fast propagation of multi-point flame occurring in the central zone of the cylinder in the double-peak type. However, the multiple flame sites occur around the cylinder, and then gradually propagate and form a large flame accelerated by the independent small flame in the ramp type.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

The performance of electric vehicles could be enhanced by more flexible drivetrain configurations combined with advanced control methods. Based on four wheel independent driving and front and rear axle modular steering configuration, which was proposed by our research group last year, the problem of slippery under close-to-limit conditions are further discussed and simulated. A new torque vectoring method based on obtainable parameters and variables in real driving situations is introduced to reduce the sideslip when turning on low friction surfaces or with high speed. This method is developed from a comprehensive index, which reflects the stability and maneuverability, by adding additional torques when stability could not be compensated enough by basic torque vectoring. Besides, an improvement of adding a simu-Torsen differential mechanism is made to the model of the vehicle, which enables another control method with the same purpose as before.

The vehicle pull problem is very important to driving safety. Major factors that may cause the pull problem related to tire include variations of geometric dimension (e.g. RPK) and stiffness (e.g. cornering stiffness, aligning stiffness), plysteer and conicity. In previous research, the influencing mechanism of these factors was well studied. But in fact, vehicle pull problem caused by tire is probabilistic. When we assemble four tires onto the car, there could be 384 different assembly arrangements. If there are significant differences among these four tires, there will also be significant differences in the influence of different tire assembly schemes on vehicle pull, which has not been systematically discussed in previous studies. If we want to evaluate the pull performance of all these arrangements by vehicle test, it will be a time consuming process which will take almost 24 working days, along with a high test expense.

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

This paper conducts an investigation on the operating cycle of Bus No. 306, which is equipped with wireless charging system, in Changsha, Hunan Province, China. The wireless charging system and electric buses are manufactured by ZTE Corporation (Zhongxing Telecommunication Equipment Corporation) and BYD Company Limited, respectively. In this paper, the operating cycle is quantified and modeled based on experimental data. The real-time bus route and SOC (state of charge) during daytime operation are recorded with the help of GPS (global position system) and BMS (battery management system). The wireless charging process is tested with a power analyzer and its charging efficiency is compared with a plug-in system. Besides, the radiation level while charging is also taken into consideration. Currently, the buses are designed to operate in daytime and get charged at night.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.