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The main purpose of this research is to investigate the optimal design of pipeline diameter in an air brake system in order to reduce the response time for driving safety using DOE (Design of Experiment) method. To achieve this purpose, this paper presents the development and validation of a computer-aided analytical dynamic model of a pneumatic brake system in commercial vehicles. The brake system includes the subsystems for brake pedal, treadle valve, quick release valve, load sensing proportional valve and brake chamber, and the simulation models for individual components of the brake system are established within the multi-domain physical modeling software- AMESim based on the logic structure. An experimental test bench was set up by connecting each component with the nylon pipelines based on the actual layout of the 4×2 commercial vehicle air brake system.

NVH quality is one of the most important criteria by which people judge the design of a vehicle. The Powertrain Mounting System (PMS), which can reduce the vibration from engine to vehicle cab as well as the inside noise, has attained significant attention. Much research has been done on the isolation method for three- and four-point mounting. But the six-point mounting system, which is usually equipped in commercial vehicle, is seldom studied and should be paid more attention. In this paper, the support rod installed on the upside of the transmission case is considered as a flexible body. Thus a rigid-flexible coupling model of PMS is established and the necessity of the established model is analyzed by comparing the simulation results of the new model and those of the conventional model.

In this paper, a new computational method is provided to identify the uncertain parameters of Load Sensing Proportional Valve (LSPV) in a heavy truck brake system by using the polynomial chaos theory. The simulation model of LSPV is built in the software AMESim depending on structure of the valve, and the estimation process is implemented relying on the experimental measurements by pneumatic bench test. With the polynomial chaos expansion carried out by collocation method, the output observation function of the nonlinear pneumatic model can be transformed into a linear and time-invariant form, and the general recursive functions based on Newton method can therefore be reformulated to fit for the computer programming and calculation. To improve the estimation accuracy, the Newton method is modified with reference to Simulated Annealing algorithm by introducing the Metropolis Principle to control the fluctuation during the estimation process and escape from the local minima.

To solve the dynamic response problem that contains uncertain parameters needs, the stochastic differential equations needs to be calculated. Interval analysis has been widely used to solve engineering problems which contain many uncertain parameters usually. But the numerical solution method for stochastic differential equations based on the interval analysis method was seldom investigated. In this study a new numerical interval method for the stochastic differential equations based on the Euler's method is presented, which can be used to solve the linear system effectively and efficiently. The probabilistic and interval dynamics analysis of a two-degree-of-freedom bike car model with uncertain parameters are presented.

This paper describes a simplified model to identify sprung mass using golden section method, the model treats the unsprung mass vertical acceleration as input and the sprung mass vertical acceleration as output, which can avoid the nonlinear influence of trye. Unsprung mass can be also calculated by axle load and the identified sprung mass. This study carries out road test on the vehicle ride comfort and takes a scheme that the group of 20 km/h is used to identify sprung mass and the group of 80 km/h is used to verify the identification result. The similarity of the results from the simulation and experiments performed are, for the sprung mass, 98.59%. A conclusion can be drawn that the simple method to measure the sprung mass in the suspension systems in used vehicles, such as the vehicle shown here, is useful, simple and has sufficient precision.

There are many uncertain parameters in shock absorbers, which are induced by the manufacturing error, the wear of components and the aging of materials in real vehicle environment. These uncertainties often cause some deterioration of vehicle performance. To optimize the ride characteristic of a vehicle when the shock absorber includes uncertain parameters, the robust design method is used. In this paper, a Twin Tube shock absorber fluid system model has established on the multi-domain modeling environment. This model not only includes the commonly used parameters of the shock absorber but also takes into account the structure parameters of various valves in the shock absorber, which is more detailed and accurate than those models in the past literature. The robust design of the shock absorber parameters is successfully approached using the co-simulation technique, and the ride comfort performance of the vehicle is also improved.

As there are a variety of uncertainty contained in dynamic systems, this paper presents a method to identify the uncertain parameters of Load Sensing Proportional Valve in a heavy truck brake system. This method is derived from polynomial chaos theory and uses the maximum likelihood approach to estimate the most likely value of uncertain parameters, such as equivalent bearing area diameter of the diaphragm, preload of return spring and so on. The maximum likelihood estimates are obtained through minimizing the cost function derived from the prior probability for the measurement noise. Direct stochastic collocation has been shown to be more efficient than Galerkin approach in the simulation of systems with large number of uncertain parameters. The simulation model of Load Sensing Proportional Valve is built in software AMESim based on logic structure of the valve. The uncertain parameters are estimated through the simulation results which are treated as measurements.

This paper deals with the robust design of the Load Sensing Proportional Valve (LSPV). To find out the parameters which have main effect on the performance of the LSPV, the DOE based on orthogonal experiment is carried out utilizing the LSPV model built in AMESim environment. In order to save computation expense, the RSM technique is used to approximate the optimal objectives and constraints. Then a robust design methodology using multi-objective evolutionary algorithm (MOEA) is performed and a set of non-dominated solutions are therefore obtained. With specified assessments, feasible solutions can therefore be selected from the entire field of the Pareto optimal solutions. The validation is made by Monte Carlo Simulation Technique in terms of the robustness of the feasible solutions.

As is known to all, there are some contradictions between the handling and ride performance during the design process of vehicles. Sometimes owing to serious collisions of each criterion in the high-dimensional solution space, the common method to deal with the contradiction is to transform into a single target according to weights of each objective, which may not obtain a desired result. A multi-criteria approach is therefore adopted to optimize both properties and the result of a multi-criteria design is not a unique one but a series of balanced solutions. This paper is focused on the robust design of a simplified vehicle model in terms of not only ride comfort but also handling and stability using a multi-objective evolutionary algorithm (MOEA) method. Using the proposed method, the conflicting performance requirements can be better traded off. One of the most important indexes to characterize the vertical ride comfort is the acceleration of the sprung mass.

The vehicle ride comfort behavior is closely associated with the vibration isolation system such as the primary suspension system, the engine mounting system, the cab suspension system and the seat suspension system. Air spring is widely used in the cab suspension system for its low vibration transmissibility, variable spring rate and inexpensive automatic leveling. The mathematical model of the air spring is presented. The amplitude and frequency dependency of the air spring's stiffness characteristic is highlighted. The air spring dynamic model is validated by comparing the results of the experiment and the simulation. The co-simulation method of ADAMS and AMESim is applied to integrate the air spring mathematical model into the cab multi-body dynamic model. The simulation and ride comfort test results under random excitation are compared.

The structure of a classic self-energizing synchronizer is presented, and a simulation model is developed for analyzing the synchronizer performance. The self-energizing synchronizer has a disk spring and several energizing teeth on the sleeve for increasing the shift force. Besides, the asymmetric arrangement of chamfer teeth is applied to increase the torque for rotating ring and shift gears smoothly. The parameterized model of the typical synchronizer is developed with ADAMS for studying the synchronizer performance. In order to truly reflect the reality, the teeth of the claw plate are connected to the gear ring through bushing force alone, and the stiffness coefficient are obtained through the analysis of finite element model. Based on the dynamic model, the behavior of synchronizer with asymmetric arrangement of chamfer teeth, and the energizing effect of stiffness of the disk spring are studied. The simulation results can be used to design the synchronizer.

This paper presents the dynamic and analysis models of a typical synchronizer, which are mainly used to analyze the sensitivity of parameters of synchronizer on the gear shift performance. Because there are so many parameters namely coefficient of friction, cone angle, mean radius, blocker angle, etc, affected the synchronizer performance, which the times of experience will increase in a geometrical ratio if tested them one by one, it is almost impossible to evaluate all parameters by experience alone. Due to virtual prototype technology, the synchronizer parameterized virtual models can be built for the synchronizer studies. The parameterized virtual models of the typical synchronizers are developed with ADAMS. Then the gear shift process is simulated and analyzed for the given input parameters respectively. The models also predict the shift time and the peak of the shift force, and the model is validated by the good correlation between simulation results and test data.

Vehicle systems often operate with some degree of uncertainty. This study applies the Chebyshev interval method to model vehicle dynamic systems operating in the presence of interval parameters. A full vehicle model is used as the numerical model and the methodology is illustrated on the steering wheel angle pulse input test. In the numerical simulation, suspension stiffness coefficients and suspension damping coefficients are chosen as interval parameters and lateral acceleration and yaw rate are chosen to capture vehicle dynamic characteristics. System responses in time domain are validated against Monte Carlo simulations and against the scanning approach. Results indicate that the Chebyshev interval method is more efficient than Monte Carlo simulations. The results of scanning method are similar to the ones obtained with the Chebyshev interval method.

Hydraulic Engine Mount (HEM) is widely used in vehicle Powertrain Mounting System (PMS) for vibration isolation. The dynamic performances of an HEM are strongly frequency dependent. A Five-Parameters Fractional Derivative model is used to describe the dynamic properties of an HEM. A 1/4 car model is applied to evaluate the effect of frequency-dependent dynamic stiffness which using measured data of a typical hydraulic engine mount. The excitations from engine and road are considered in the simulation. The generalized- α method is presented to solve the vehicle model with five-parameter fractional derivative model.

Vehicle Thermal Management System (VTMS) is a cross-cutting technology that directly or indirectly affects engine performance, fuel economy, safety and reliability, driver/passenger comfort, emissions. This paper presents a novel methodology to investigate VTMS based on Modelica language. A detailed VTMS platform including engine cooling system, lubrication system, powertrain system, intake and exhaust system, HVAC system is built, which can predict the steady and transient operating conditions. Comparisons made between the measured and calculated results show good correlation and approve the forecast capability for VTMS. Through the platform a sensitivity analysis is presented for basic design variables and provides the foundation for the design and matching of VTMS. Modelica simulation language, which can be efficiently used to investigate multi-domain problems, was used to model and simulate VTMS.

Due to the elimination of the mechanical connection between the steering column and steering gear in the Steer-by-Wire (SBW) system, the road-feeling simulation is mainly supplied by the road-feeling motor which loads a drag torque on the steering wheel rather than the actual torque transmitted from the road. To obtain more realistic steering wheel torque, a novel feedback torque of the road-feeling motor fusion estimation method based on the Kalman filter is presented in this paper. Firstly, the model-based estimation method is utilized to estimate the aligning torque between tires and ground which is converted into the rack force through the steering system. Then the estimated rack force is used as the observed data for the Kalman Filter of the sensor-based method and the Kalman Filter-based fusion estimation method is resulted, through which the more realistic feedback torque of the road-feeling motor can be obtained.