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Abstract This research develops a hierarchical control strategy to improve the stability of multi-axle electric vehicles with in-wheel motors while driving at high speed or on low adhesion-coefficient roads. The yaw rate and sideslip angle are chosen as the control parameters, and the direct yaw-moment control (DYC) method is employed to ensure the yaw stability of the vehicle. On the basis of this methodology, a hierarchical yaw stability control architecture that consists of a state reference layer, a desired moment calculation layer, a longitudinal force calculation layer, and a torque distribution layer is proposed. The ideal vehicle steering state is deduced by the state reference layer according to a linear two-degree-of-freedom (2-DOF) vehicle dynamics model.

Abstract The thermal error of ball screw is the main factor affecting the accuracy of machine tool. Establishing an accurate thermal error model of ball screw is the key to compensate the error of machine tool. The ultimate goal of the research work in this article is to develop a comprehensive modeling method that can predict the temperature rise and thermal error of ball screw. In view of the problem that the reciprocating motion of ball screw nut was ignored in the traditional thermal error model, a transient thermal-structural coupling model considering the actual working conditions was proposed. ANSYS parametric design language (APDL) was used to set the ball screw nut as the moving heat source load, and the displacement-time relationship between the ball screw nut and the ball screw was defined. The temperature and thermal deformation distribution of the ball screw under the action of the bearing and the heat source of the ball screw nut were simulated.

Abstract This article proposes a new multi-axle steering vehicle (MSV) kinematics model to improve the tire wear of multi-axle heavy commercial vehicles while ensuring driving safety. The MSV kinematics model is based on the Ackerman steering geometry, which properly distributes the tire steering angles of each axle to cause the tractor unit and the trailer unit of the multi-axle heavy commercial vehicle to steer around the same steering center. In order to compensate for the influence of the factors such as the slip angle of each tire, the adjustment parameter K is introduced to reasonably adjust the relationship between the steering wheel angle input and the tire steering angles of each axle of the trailer unit. The adjustment parameter K makes the trajectory of the trailer unit of the MSV accurately follow the trajectory of the tractor unit of the MSV without changing the trajectory of the tractor unit.

Abstract A combination of machine learning (ML) and optimal fuzzy control (OFC) is proposed for the semi-active air suspensions of heavy trucks to further improve ride comfort and road friendliness. To obtain the study aim, a vehicle dynamics model with 10 degrees-of-freedom (10-DOF) is established in the MATLAB/Simulink environment to simulate and calculate the objective functions of the root-mean-square (RMS) acceleration responses of the vertical driver’s seat and pitching cab angle and the dynamic load coefficient (DLC) on the wheel axles under various working conditions. Based on the OFC with its control rules optimized by the genetic algorithm (GA) and the data map of the random road surfaces, an ML method of the Adaptive Network-based Fuzzy Inference System (ANFIS) in MATLAB is developed and applied to control the semi-active air suspensions.

Abstract In this article, a new road load simulation technology is presented for commercial vehicle driveline. In order to assess the performance of vehicle driveline, the chassis dynamometer system is introduced on the basis of the traditional vehicle driveline test bench, which improves the accuracy of the simulation system without the need of complex modeling of commercial vehicle tire dynamics. The vertical load of the vehicle is emulated by the hydraulic loading mechanism, and the influence of the vertical load on commercial vehicle driveline is emulated when the vehicle passes the bumpy road. The evaluate control method of commercial vehicle acceleration inertia based on wheel rotational speed and vehicle dynamics model is designed.

Abstract Preview control algorithm has been widely implemented in intelligent vehicle path-tracking controllers. The key challenge of developing such control is to determine the appropriate preview distance, which plays a vital role in achieving the optimal trade-off between two competing control objectives, tracking accuracy and driving stability. Additionally, vehicle speed and road radius have a significant impact on the optimal preview distance. Thus a hierarchical vehicle path-tracking control strategy based on the adaptive two-point preview is proposed in this article. In the upper-layer module, the two-point preview driver model is constructed to obtain the target yaw rate according to the comprehensive deviation. In the lower-layer module, the neural network sliding mode controller is employed to track the yaw rate and, therefore, achieve intelligent vehicle self-tracking.

Abstract As a special distributed drive configuration, H-type distributed drive electric vehicle (H-DDEV) has better off-road ability and trafficability than traditional distributed drive electric vehicle, which attracts the interest of researchers deeply. However, due to its unique structure, how to ensure the stability of H-DDEV is still a key control problem. In this article, a hierarchical stability control algorithm based on model predictive control (HC-MPC) is proposed for H-DDEV. First, the vehicle dynamics model is established, which can reflect the dynamic characteristics of H-DDEV. Then, the HC-MPC is designed. In the upper layer, the nonlinear two-degrees-of freedom (2-DOF) model is applied in MPC, which can improve the control accuracy without increasing complexity through local linearization.

Abstract In view of the traditional constant spacing policy (CSP) can’t maximize the fuel saving rate of the truck platoon when choosing the smaller desired vehicle spacing as the control target, a new control strategy is proposed in this article. This strategy dramatically reduces the fuel consumption of the truck platoon from the start to the formation of a stable platoon, thus greatly increasing the fuel saving rate of the platoon. To prove the effectiveness of the strategy, this article carried out the longitudinal dynamics modeling of the truck and the modeling of the fuel consumption model of engine first. Longitudinal dynamics modeling establishes the dynamic equations for truck braking and nonbraking. The fuel consumption model of engine is built using a three-dimensional map. Second, the design of the controller is described. The controller calculates the desired acceleration of the following vehicle based on the speed error and the following distance error.

Abstract Aerodynamic drag for road vehicles is most often assessed based on zero yaw conditions. The rise of electric vehicles in recent years put greater demand on how the vehicles perform in real-world conditions. Specifically, the aerodynamic drag performance at non-zero yaw angles has received increased attention. Various methods to calculate wind-averaged drag have been proposed. However, there have not been any studies done for the yaw distribution in China; this is important, given its diverse geographic and climatic conditions and growing number of vehicles. This study presents a methodology using probes integrated with a production vehicle to collect representative on-road data. A survey of on-road conditions in China is presented including coastal and inland provinces, different road types, and a range of traffic conditions. Using high temporal and special resolution meteorological data, the correlation between yaw angle distribution and natural wind is derived.

Abstract Vehicular automation in the form of a connected and automated vehicle platoon is demanding as it aims to increase traffic flow and driver safety. Controlling a vehicle platoon on a curved path is challenging, and most solutions in the existing literature demonstrate platooning on a straight path or curved paths at constant speeds. This article proposes an algorithmic solution with leader-following (LF) communication topology and constant distance (CD) spacing for platooning homogeneous position-controlled vehicles (PCVs) on a curved path, with each vehicle capable of cornering at variable speeds. The lead vehicle communicates its reference position and orientation to all the follower vehicles. A follower vehicle stores this information as a virtual trail of the lead vehicle for a specific period. An algorithm uses this trail to find the follower vehicle’s reference path by solving an optimization problem.

Abstract A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side-slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration.

Abstract The present work deals with the damage life correlation of vehicle-level testing results of an axle housing for different road load conditions with the accelerated bench testing experiment results to reduce product development time. Also failure analysis is carried out to overcome the mechanical strength losses caused by the hot forming process during the manufacturing of housings. Commercial vehicle torture test tracks are built to reflect the forces similar to vehicle usage conditions from lighter to severe loadings. Strain data and calibrated force values are captured at the critical loading points in the axle for one cycle, at actual vehicle-driven speeds, to reflect the accelerated load values on five different track conditions. Damages estimation carried out based on the road loads reflects there will be no failure of axle housings till the acceptance of 120 repeats in different track combinations.

Abstract Articulated vehicles form an important part of our society for the transport of goods. Compared to rigid trucks, tractor-trailer combinations can transport huge quantities of load without increasing the axle load. The fifth wheel (FW) acts as a bridge between the tractor and trailer, which can be moved within the range to achieve rated front and rear axle loads. When the FW is moved front, it adversely affects the cab dynamics and cab suspension forces. Compared to the cab pitch and roll, yaw motion increases drastically. The current study tries to address this issue by providing reaction rod links in the rear cab suspension. In this study, a 4×2 tractor with a three-axle semitrailer is considered by keeping the FW at its frontmost position, which is the worst-case scenario for a cab. Three different cases of reaction rod arrangement and its influence on cab dynamics are studied in comparison with a model without reaction rods.

Abstract In view of the fact that the current positioning methods of railway fasteners are easily affected by illumination intensity, bright spots, and shadows, a positioning method with relative grayscale invariance is proposed. The median filter is used to remove the noise in order to reduce the adverse effects on the subsequent processing results, and the baffle seat edge features are enhanced by improved Census transform. The mean-shift clustering algorithm is used to classify the edges to weaken the interference by short lines. Finally, the Hough transform is used to quickly extract the linear feature of the baffle seat edge and achieve the exact position of the fastener with the prior knowledge. Experimental results show that the proposed method can accurately locate and have good adaptability under different illumination conditions, and the position accuracy is increased by 4.3% and 8%, respectively, in sunny and rainy days.

Abstract The aluminum alloy 7075 sheets have drawn more attention in recent years in the automotive industry for lightweighting. Hot stamping of high-strength aluminum alloy has been developed to improve the formability of the part without springback. Obtaining an adequate quench rate is a critical step of the hot stamping process and corresponds to good strength and corrosion resistance. This work looks at measuring the quench rate of 7075 at advanced aging (AA) and T6 condition via two different approaches: forced air and water with various temperatures. The results verify that water is a superior form of quenching, i.e., from 50°C/s to 550°C/s, the forced air-cooled quench rate is 2°C/s-10°C/s. Besides, mechanical properties such as yield strength, ultimate tensile strength, and uniform elongation were measured by tensile testing. As a result, a correlation between the quench rate and final mechanical properties was developed.

Abstract Compared with traditional plastics, glass fiber-reinforced plastic (GFRP) has more outstanding performance advantages, which is more and more widely used. To improve the quality of the products manufactured by the GFRP injection molding, the injection parameters are optimized in two stages. In the first stage, the range of optimization parameters including the glass fiber content and six molding parameters is selected by the Moldflow recommendation. The warpage and shrinkage of each orthogonal experiment are obtained by the Moldflow simulation. Then, a comprehensive evaluation method called GRA-TOPSIS and the range analysis method are utilized to identify the optimal level values of all optimization parameters. According to the order of influence of each parameter, the range of these parameters is adjusted for the second stage.

Abstract This article presents a semi-active vibration control suspension system using a preview Model Predictive Control (MPC) linked with a magnetorheological (MR) damper to improve vehicle stability during handling dynamics, consequently confidently achieving both maneuverability and lateral dynamic motion. The mathematical model (4DOF) described by bounce and pitch motions for sprung mass and two bounce motions for the un-sprung masses, which consists of a preview half-vehicle suspension system and MR dampers at the front and rear axles, is derived. A nonpreview case of the linear quadratic regulator (LQR), a preview case of the LQR, and a preview case of the MPC as alternative methods are applied to design the system controller in combination with a signum function method as a damper controller for both the front and rear MR dampers. The vehicle handling model based on the look-ahead distance of the road, which includes yaw and lateral motions, is linked with the driver model.

Abstract Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network.

Abstract Turboprop aircraft have the capability of reversing thrust to provide extra stopping power during landing. Reverse thrust helps save the wear and tear on the brakes and reduces the landing distance under various conditions. The article explains a methodology to predict the disking drag (reverse thrust) from the Computational Fluid Dynamics (CFD) technique using Blade Element Momentum (BEM) theory and estimation of the same from high-speed taxiing trial (HSTT) and ground roll data for a turboprop aircraft using system identification techniques. One-dimensional kinematic equation was used for modeling the aircraft dynamics, and the error between measured and estimated responses was optimized using the Output Error Optimization Method (OEOM). The estimated propeller drag was matched with CFD predictions to arrive at a relation between the propeller blade pitch angle and throttle position.

Abstract Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example.