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Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data.

The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

A vehicle’s NVH performance has a significant impact on the user experience of the driver and passengers. About one-third of the vehicle complaints are related to NVH performance. As the core component of the vehicle, the drivetrain’s NVH characteristics have a significant impact on vehicle comfort. How to reliably and stably reproduce the specific condition of the whole vehicle through the test method, and obtain the highly consistent objective data for analyzing and improving the NVH characteristics of the drivetrain is of great significance in engineering. For this purpose, China Automotive Technology Research Center Co., Ltd. (CATARC) designed and built a new type drivetrain NVH test facility, which consists of five dynamometers, and can carry horizontal/vertical, front/rear drive or four-wheel drive structures including powertrain, transmission, and rear axle, or even a whole vehicle.

In this paper, the dynamic stress of the front subframe of a passenger car was obtained using modal stress recovery method to estimate the fatigue life. A finite element model of the subframe was created and its accuracy was checked by modal test in a free hanging state. Furthermore, the whole vehicle rigid-flexible coupling model of the passenger car was built up while taking into account the flexibility of the subframe. Meanwhile, the road test data was used to verify the validity of the dynamic model. On this basis, the modal displacement time histories of the subframe were calculated by a dynamic simulation on virtual proving ground consisting of Belgian blocks, cobblestone road and washboard road. By combining the modal displacement time histories with modal stress tensors getting from normal mode analysis, the dynamic stress time histories of the subframe were obtained through modal stress recovery method.

With the prevalent trend of the pure electric vehicle, vehicle interior noise has been reduced significantly. However, other noises become prominent in the cabin. Especially, the BSR noise generated by friction between parts and the clearance between components become the elements of complaints directly affect the quality of vehicles. Currently, the BSR noises are subjectively evaluated by experts, and the noise samples are simply labeled as ‘qualified’ or ‘unqualified’. Therefore, it is necessary to develop an evaluation model to assess the BSR noise objectively. In this paper, we study the vehicle rattle noise intensively. Several types of rattle noise were recorded in a semi-anechoic room. The recorded signals were then processed in the LMS test lab. to extract the single impact segments. A pool of simulated signals with different impact rates (number of impacts per second) and various loudness was synthesized for analyzation.

In order to improve the handling and stability of a light bus at high speed, a virtual model was established in Adams-Car and its anti-roll bar and bushing parameters were virtually optimized. The tyre mechanical characteristics were firstly tested by using a plate-type tyre tester and the Magic Formula parameters of the tyre were obtained. Then the virtual bus model's handling performance were studied by the simulation of central steering test and steady static circular test. An optimal matching method was put forward. By using genetic algorithm to conduct optimization, the optimised parameters were obtained. After that the anti-roll bar and bushing samples were respectively manufactured. At last, the comparative trials were performed in an automotive proving ground, and the subjective evaluation of the light bus's handling and stability was taken by three specialized assessors.

The basic theory of statistical energy analysis (SEA) is introduced, a commercial heavy duty truck cab is divided into 35 subsystems applying SEA method, and a three dimensional SEA model of the commercial heavy duty truck cab is created. Three basic parameters including modal density, damping loss factor and coupling loss factor are calculated with analytical and experimental methods. The modal density of the regular wall plate of the cab is calculated with traditional formula. The damping loss factors of the regular and complicated plates are obtained using analytical method and steady energy stream method. Meanwhile, the coupling loss factors of structure-structure, structure-sound cavity, and cavity-cavity are also calculated. Four kinds of excitations are in the SEA model, including sound radiation excitation of engine, engine mount vibration excitation, road excitation and wind excitation.

In this paper, a Magneto-Rheological (MR) fluid semi-active suspension system was tested on a commercial vehicle, a domestic light bus, to determine the performance improvements compared to passive suspensions. MR fluid is a material that responds to an applied magnetic field with a significant change in its rheological behavior. When the magnetic field is applied, the properties of such a fluid can change from a free-flowing, low viscosity fluid to a near solid, and this change in properties takes place in a few milliseconds and is fully reversible. A quarter suspension test rig was built out to test the nonlinear performance of MR damper. Based on a large number of experimental data, a phenomenological model of MR damper based on the Bouc-Wen hysteresis model was adopted to predict both the force-displacement behavior and the complex nonlinear force-velocity response.

This paper presented one calculation method of the contact load, which is the load acted on the spring at the moment when the second-level stiffness of the spring just begins to work. In the proposed method, the contact load calculation mainly based on the dynamic load of the unsprung mass and the road grades and the commonly driving speed were also considered. A semiempirical formula of the contact load was put forward. Then the contact load of the commercial bus's rear suspension was respectively calculated by using the proposed formula and traditional methods(geometric mean method and average load method) to compare each other and to verify the new method. Later, the spring samples were respectively manufactured based on the calculation results. At last, the validation tests were respectively performed in an automotive proving ground.

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.

In this paper, a shock absorber physical model is developed. Firstly, a rebound valve model which is based on its structure parameters is built through using the large deflection theory. The von Karman equations are introduced to discover the physical relationships between the load and the deflection of valve discs. An analytical solution of the von Karman equations is then deducted via perturbation method. Secondly, the flow equations and the pressure equations of the shock absorber operating are investigated. The relationship between fluid flow rate and pressure drop of rebound valve is analyzed based on the analytical solution of valve discs deflection. Thirdly, an inter-iterative process of flow rate and pressure drop is employed in order to adequately consider the influence of fluid flow on damping force. Finally, the physical model is validated by comparing the experimental data with the simulation output.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

In this paper, a computer model of a multi-purpose vehicle (MPV) is built to study vehicle ride comfort by multi-body system dynamic theory. Virtual test rigs are developed to perform natural body frequency tests and random road input tests on the complete vehicle multi-body dynamic model. By comparing simulation results with field test results, the accuracy of the model is validated and the feasibility of virtual test rigs is established.