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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 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.

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.

To provide a greater weight capacity, the tandem axle which is a group of two or more axles situated close together has been used on most heavy truck. In general, the reaction moments during braking cause a change in load distribution among both axles of the tandem suspension. Since load transfer among axles of a tandem suspension can lead to premature wheel lockup, tandem-axle geometry and the brake force distribution among individual axles of a tandem suspension have a pronounced effect on braking efficiency. The braking efficiency has directly influence on the vehicle brake distance and vehicle travelling direction stability in any road condition, so how to improve the braking efficiency is researched in this paper. The load transfer among individual axles is not only determined by vehicle deceleration but also by the actual brake force of each axle for tandem axle suspension, which increases the difficulty of braking efficiency improving.

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.

Interval inverse problems can be defined as problems to estimate input through given output, where the input and output are interval numbers. Many problems in engineering can be formulated as inverse problems like vehicle suspension design. Interval metrics, instead of deterministic metrics, are used for the suspension design of a vehicle vibration model with five degrees of freedom. The vibration properties of a vehicle vibration model are described by reasonable intervals and the suspension interval parameters are to be solved. A new interval inverse analysis method, which is a combination of Chebyshev inclusion function and optimization algorithm such as multi-island genetic algorithm, is presented and used for the suspension design of a vehicle vibration model with six conflicting objective functions. The interval design of suspension using such an interval inverse analysis method is shown and validated, and some useful conclusions are reached.

When the drill arm reaches the specified position, the rubber top disk of the propelling beam is pressed against the rock surface by the hydraulic cylinder force and the rock drill starts drilling. Because of the reaction force and the deformation of the drill arm, the propelling beam will be offset from its target position and vibrate, which will affect the drilling accuracy. To analyze the vibration of the propelling beam, the rigid-flexible coupled model is established. The minimum displacement offset of the propelling beam from the initial position is used as the optimization function and the parameters of the rubber top disk are used as optimization variables. The amplitude of the propelling beam at a steady state is used as the constraint. From the simulation results, the rigid-flexible coupled model can describe the vibration of the propelling beam better than the rigid model, especially during the rock drill working stage.

Unsuitable shift control strategies may increase the vehicle jerk and clutch wear. In order to improve the shift quality of electric vehicles (EVs) equipped with dual clutch transmission, this paper proposes an optimal shift control strategy based on linear quadratic regulator, in which weighting matrices are selected by using genetic algorithm (GA). The dynamics of the shift process of the dual clutch transmission is analyzed to establish the dynamic model of the driving system. In addition to the vehicle jerk, the friction work of clutch is also considered as one of the performance criteria and a new linear quadratic objective function is formulated. The optimal weighting matrices for obtaining a globally optimal solution are selected benefit from the global search capacity of genetic algorithm. The optimal target trajectories of the torque of the two clutches and motor are obtained by simulating the linear quadratic regulator (LQR).

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.

This paper presents a torque distribution strategy for four-wheel independent drive electric vehicles (4WIDEVs) to achieve both handling stability and energy efficiency. The strategy is based on the dynamic adjustment of two optimization objectives. Firstly, a 2DOF vehicle model is employed to define the stability control objective for Direct Yaw moment Control (DYC). The upper-layer controller, designed using Linear Quadratic Regulator (LQR), is responsible for tracking the target yaw rate and target sideslip angle. Secondly, the lower-layer torque distribution strategy is established by optimizing the tire load rate and motor energy consumption for dynamic adjustment. To regulate the weights of the optimization targets, stability and energy efficiency allocation coefficient is introduced. Simulation results of double lane change and split μ road conditions are used to demonstrate the effectiveness of the proposed DYC controller.