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In contemporary trajectory planning research, it is common to rely on point-mass model for trajectory planning. However, this often leads to the generation of trajectories that do not adhere to the vehicle dynamics, thereby increasing the complexity of trajectory tracking control. This paper proposes a local trajectory planning algorithm that combines sampling and sequential quadratic optimization, considering the vehicle dynamics model. Initially, the vehicle trajectory is characterized by utilizing vehicle dynamic control variables, including the front wheel angle and the longitudinal speed. Next, a cluster of sampling points for the anticipated point corresponding to the current vehicle position is obtained through a sampling algorithm based on the vehicle's current state. Then, the trajectory planning problem between these two points is modeled as a sequential quadratic optimization problem.

The design of suspension affects the vehicle dynamics such as ride comfort and handling stability. Nonlinear characteristics and friction are important characteristics of suspension system, and the influence on vehicle dynamic performance cannot be ignored. Based on the seven-degree-of-freedom vehicle vibration nonlinear model with friction, the vibration response process of the vehicle and the influence of suspension friction on vehicle ride comfort and suspension action process were studied. The results show that friction will significantly affects the simulation of ride comfort and coincide with the function of the shock absorber. The suspension shock absorbers of vehicles were optimized with and without suspension friction. The results showed that the suspension tended to choose softer shock absorbers when there was friction. However, both of the two optimizations are able to improve the ride comfort of vehicles, and the simulation results were similar.

This paper investigates a hierarchical optimization procedure for the optimum synthesis of a double-axle steering mechanism by considering the dynamic load of a vehicle which is seldom discussed in the previous literature. Firstly, a multi-body model of double-axle steering is presented by characterizing the detailed leaf spring effect. Accordingly, the influences of dynamic load including the motion interference of steering linkage resulted from the elastic deformation of leaf spring, and the effects of wheel slip angle and the position discrepancy of wheel speed rotation centers are explored systematically. And then, a hierarchical optimization method based on target cascading methodology is proposed to classify the design variables of double-axle steering mechanism into four levels. At last, a double-axle steering mechanism of a heavy-duty truck is utilized to demonstrate the validity of this method.

The optimization of vehicle suspension kinematic/compliance characteristics is of significant importance in the chassis development. Practical suspension system contains many uncertainties which may result from poorly known or variable parameters or from uncertain inputs. However, in most suspension optimization processes these uncertainties are not accounted for. This study explores the use of Chebyshev polynomials to model complex nonlinear suspension systems with interval uncertainties. In the suspension model, several kinematic and compliance characteristics are considered as objectives to be optimized. Suspension bushing characteristics are considered as design variables as well as uncertain parameters. A high-order response surface model using the zeros of Chebyshev polynomials as sampling points is established to approximate the suspension kinematic/compliance model.

To study the torque distribution of track and tire in the wheel-track hybrid drive vehicle driving along the shoreline, an analysis model of wheel-track hybrid drive vehicle was established by using multi-body dynamics (MBD), discrete element (DEM), and shoreline pavement construction methods. The vehicle speed, acceleration, torque, vertical load, sinkage, slip, and other indicators when the vehicle passes the shoal at different wheel speed of rotation are analyzed. The relationships between wheel speed of rotation and slip, sinkage and slip, and vertical load and driving moment were studied, and the laws that the sinkage of tires and tracks is positively related to their slippage and the driving moment of wheels and tracks is positively related to their vertical load were obtained.

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.

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.

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.

Electric vehicles driven by in-wheel-motor have the advantages of compact structure and high transmission efficiency, which is one of the most ideal energy-saving, environmentally friendly, and safe driving forms in the future. However, the addition of the in-wheel-motor significantly increases the unsprung mass of the vehicle, resulting in a decrease in the mass ratio of the vehicle body to the wheel, which will deteriorate the ride comfort and safety of the vehicle. To improve the vibration performance of in-wheel-motor driven vehicles, a semi-active inerter-spring-damper (ISD) suspension with in-wheel-motor (IWM) dynamic vibration absorber (DVA) of the electric wheel is proposed in this paper. Firstly, a structure of in-wheel-motor DVA is proposed, which converts the motor into a dynamic vibration absorber of the wheel to suppress the vibration of the unsprung mass.

The air brake system has been widely used since its great superiority over many other kinds of brake systems, but the capacity and the stability of air brake system are determined by many factors which are always uncertain and difficult to be evaluated accurately. So it is necessary to improve the robustness of this kind of brake system. In this paper, a physical model of air brake control system is made by a multi-domain physical modeling software-AMESim and the robust design for air brake system is carried out. Firstly, the key design parameters that will greatly affect on the delay time and pressure that leads to the shaking problem are obtained by using the method of design of experiment (DOE). Then, the regress of the response surface based on results of DOE and the robust design using the tolerance design are carried out. The value for those key parameters that can lead to the best performance and robustness of the air brake system are finally determined.

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.

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.

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.

The ride comfort of the commercial vehicle is mainly affected by several vibration isolation systems such as the primary suspension system, engine mounting system and the cab mounting system. A rigid-flexible coupling model for the truck was built and analyzed in multi-body environment (ADAMS). The method applying the excitation on the wheels center and the engine mountings in time domain was presented. The variables' effects on the ride performance were studied by design of experiment (DOE). The optimal design was obtained by the co-simulation of the ADAMS/View, iSIGHT and Matlab. It was found that the vertical root mean square (RMS) acceleration and frequency-weighted RMS acceleration on the seat track were reduced about 17% and 11% respectively at different speeds relative to baseline according to ISO 2631-1.

Steering returnability is an important index for evaluating vehicle handling performance. A systematic method is presented in this paper to reduce the high yaw rate residue and the steering response time for a light duty truck in the steering return test. The vehicle multibody model is established in ADAMS, which takes into consideration of the frictional loss torque and hydraulically assisted steering property in the steering mechanism, since the friction, which exists in steering column, spherical joint, steering universal joint, and steering gear, plays an important role in vehicle returnability performance. The accuracy of the vehicle model is validated by road test and the key parameters are determined by executing the sensitivity analysis, which shows the effect of each design parameter upon returnability performance.

Elastomeric bushings are widely used in the passenger cars to make the cars have an ideal vehicle Noise, Vibration and Harshness (NVH) performance. However, elastomeric bushings also influence on the vehicle handling, ride and the durability performance of each component in the vehicle suspension system. It is relatively easy and cost effective to change the compliance of the bushing components compared with other method because they are made of elastomeric materials. The design of an elastomeric bushing is really a big challenge. One of the main difficulties comes from the different target compliance is wanted according to the handling, ride and durability demand at each different orientation (indicated by X Y Z) of the bushing. In this paper the following procedure was used for optimization of suspension elastomeric bushing compliance. Firstly, a detailed multi-body model was built including the nonlinear bushing effects and lower control arm flexibility.

Many vehicles have been equipped with air springs as elastic elements to get better performance in comfort, but absorbers may not work in an optimal state due to the variation of suspension stiffness. While the function of semi-active suspension is to enable the absorber damping to be adjusted according to different road roughness levels and to coordinate between comfort and handling. To solve the problem of matching the damping coefficient of variable stiffness suspensions represented by air springs, this paper proposed a method for calculating the optimal damping ratio of a semi-active suspension system in real-time with sprung mass acceleration and dynamic tire load to establish the objective function and suspension dynamic deflection as the constraint to reflect the unification of comfort and handling.

The road adhesion coefficient has a great impact on the performance of vehicle tires, which in turn affects vehicle safety and stability. A low coefficient of adhesion can significantly reduce the tire's traction limit. Therefore, the measurement of the coefficient is much helpful for automated vehicle control and stability control. Considering that the road adhesion coefficient is an inherent parameter of the road and it cannot be known directly from the information of the on-vehicle sensors. The novelty of this paper is to construct a road adhesion coefficient observer which considers the noise of sensors and measures the unknown state variable by the trained neural network. A Butterworth filter and Adaptive Neural Fuzzy Interference System (ANFIS) are combined to provide the lateral and longitudinal velocity which cannot be measured by regular sensors.

Suspension kinematics and compliance strongly influence the handling performance of the vehicle. The kinematics and compliance characteristics are determined by the suspension geometry and stiffness of suspension bodies and elastic components. However, it is usually inefficient to model all the joints, bushings, and linkage deformation in a full vehicle model. By transforming the complex modeling problem into a data-driven problem tends to be a good solution. In this research, the neural-network-based suspension kinematics and compliance model is built and implemented into a 17 DOF full vehicle model, which is a hybrid model with state variables expressed in the global coordinate system and vehicle coordinate system. The original kinematics and compliance characteristics are derived from multibody dynamics simulation of the suspension system level.

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.