Addressing the problems of long processing time, low accuracy and poor real-time performance in wheel polygon recognition, a new recognition model is proposed. The vertical vibration data of the axle box is converted from 1D to 2D and normalised into grey degree images. Subsequently, the grey degree image is decomposed into binary images through bit plane decomposition, and an improved statistical geometric feature (ISGF) method is utilised to extract textural features from the binary image. Finally, these features are used for the training and classification process of support vector machine (SVM) to diagnose and recognise wheel polygon faults. Through dynamics simulation verification and double-wheel test bench experiments, the results show that the model organically combines the fault data with the image recognition method, effectively restrains the noise interference in vibration data, and significantly improves the recognition accuracy, reduces recognition time and demonstrates strong generalisation performance.
A four-degree-of-freedom nonlinear seat-cab suspension system was established by testing the mechanical properties of the air springs and fitting the test data with a cubic polynomial. On this basis, the whole-vehicle road sampling test technique and the bench load replication iteration technique was utilised to obtain the excitation signals of a Belgian road and general highway. Then, the vibration responses of the two road surfaces were investigated based on random vibration theory. The human vibration comfort evaluation was carried out using Chinese standards. Subsequently, the linear parameters of the suspension system were optimised and matched according to the design requirements of the suspension system using the multi-island genetic algorithm in Isight. Finally, the smoothness of the suspension system before and after optimisation was simulated and verified. The results show that the new suspension system has better vibration isolation performance and greatly improves the comfort.
Traffic anomaly detection plays a critical role in ensuring the security and reliability of modern networks, particularly in the context of software-defined networking (SDN). This paper proposes the wild geese dwarf mongoose optimisation based deep quantum neural network (WGDMO_DQNN) for traffic flow detection in SDN. The SDN is initially simulated and packet transmission is conducted. The traffic flow detection at data plane is accomplished, wherein feature extraction and traffic flow detection utilising deep quantum neural network (DQNN) are carried out. The control attack mechanism is executed in three phases: identifying, executing, and composition. If the controller is overloaded, switches are swapped to underloaded controller employing WGDMO. Moreover, WGDMO is an amalgamation of wild geese algorithm (WGA) and dwarf mongoose optimisation algorithm (DMOA). Additionally, WGDMO_DQNN achieved maximal TPR of 91.6%, TNR of 87.5%, accuracy of 93.7%, minimal switch migration cost of 0.668 and load of controller 0.437.
This paper presents a coordinated control strategy for acceleration slip regulation (ASR) and direct yaw moment control (DYC) to improve the lateral stability of a distributed in-wheel motor drive electric articulated heavy vehicle (DIMDEAHV). A non-linear TruckSim model and a linear three-degree- of-freedom (3-DOF) yaw-plane model of DIMDEAHV are generated, and their fidelity is evaluated. An algorithm is then developed to identify road conditions using the mu-lambda standard curve of the Burckhardt tyre model. Built upon the road identification algorithm, an ASR controller is designed and proved to be effective in preventing wheel slip. Finally, a coordinated control strategy for ASR and DYC is developed and validated using co-simulation under a double lane change (DLC) manoeuvre. The simulation results demonstrate that compared to the DYC alone, the ASR and DYC coordinated control can effectively prevent the wheel slip and improve the lateral stability of the DIMDEAHV travelling at high speeds on low adhesion roads.
In order to solve the rollover problem of the four-axle heavy dump truck during driving, the chassis cooperative control method was proposed combining differential braking, active front steering and active suspension control, and a 13-degree-of-freedom dynamic model was established. The weight coefficients of the three controls were set, the functional relationships between the rollover evaluation index and the weight coefficients were established respectively. With the joint simulation of MATLAB/Simulink and Trucksim, the simulation results show that, compared to the integrated control of differential braking and active suspension control, the chassis cooperative control reduces the peak roll angle by additional 1.308 deg and 2.409 deg. Compared to the integrated control of differential braking and active front steering control, the chassis cooperative control reduces the peak roll angle by additional 0.766 deg and 0.701 deg. The chassis cooperative control method effectively prevents rollover and improves the stability of the heavy truck.
Ensuring the stability of heavy-duty trucks is crucial to prevent accidents, as their high centre of gravity makes them prone to rollovers. Alongside lateral stability, roll stability is a critical control parameter, and this paper proposes a strategy that uses two subsystems to simultaneously control the vehicle's roll angle and yaw rate. The active roll subsystem employs a Fuzzy-PD controller to generate the desired torque, while the active front steering (AFS) subsystem uses a sliding mode controller to track the desired yaw rate. To test the effectiveness of the proposed strategy, simulations are carried out using a full TruckSim model as a simulation model. The controller model consists of a 2-DOF 3-axle generalised bicycle model and a 1-DOF roll model. For instance, the simulation results in the J-Turn and Sine manoeuvre illustrate that the proposed strategy enhances the vehicle's stable speed range by up to 32.8%, particularly when the two subsystems operate simultaneously.
To address the issue of poor path tracking accuracy and vehicle stability of automated articulated vehicles under emergency track changing conditions, a multi-point preview path tracking, stability decision and optimal control strategy is proposed. Considering the dual effects of vehicle speed on preview deviation and the articulated relationship between tractor and semi-trailer on the trajectory, a three-point preview driver model based on the adaptive weight of vehicle speed is designed. The phase plane method and lateral load transfer ratio are used to determine the articulated vehicle's stability. A direct yaw moment by differential braking stability control strategy for articulated vehicle based on model predictive control (MPC) is designed to reduce the yaw rate, mass centre side slip angle and lateral load transfer ratio of the tractor as well as the semi-trailer during path tracking of an articulated vehicle to improve the vehicle running stability.
In the vibration tests, three-axis accelerometers were installed on different electric buses (BYD, IKARUS). One sensor was placed on the chassis and another on the body. During the measurements, two directions of two sensors were recorded using an oscilloscope. On five routes, 10 different sections were recorded. We evaluated the root mean square (RMS) pulsation values of the vibrations in each case, as well as the maximum amplitude vibration of each section. The comparison tables show that there can be a difference of up to 40% in vibration intensity between the lowest and the highest loaded section. The comparative data show that the effect of chassis damping is to reduce the body vibration to 1/6 of the chassis load.
This manuscript presents a hybrid approach to the job shop scheduling problem (JSP) with sequence-dependent set-up and transportation times, utilising the AHO-MARR technique. The method aims to minimise both makespan and overall energy consumption in energy-efficient manufacturing environments. By integrating the Archerfish Hunting Optimiser (AHO) and the Median-Average Round Robin (MARR) scheduling algorithm, the approach considers processing time, idle time, sequence-dependent set-up time, and transportation time to improve production efficiency. AHO calculates total processing time and energy for each job, while MARR ensures practical task distribution across machines. The method effectively reduces setup time and energy usage, achieving an energy consumption of 730 kW/min. Comparative analysis shows that the AHO-MARR technique outperforms existing methods such as the Salp Swarm Algorithm (SSA), Wild Horse Optimiser (WHO), and Heap-Based Optimiser (HBO) in terms of energy efficiency and makespan reduction.
Eco-friendly vehicles are being developed to reduce carbon emissions and address global warming. The solar powered automated rapid transit ascendant network (SPARTAN) Superway project at San Jos & eacute; State University focuses on an automated transit network (ATN) powered by solar energy. ATN vehicles, with a unique cabin-below-track configuration and distinct steering system, face dynamic challenges, particularly at Y-junctions and curved guideways. This study employs multi-body dynamic simulation to analyse the behaviour of a prototype ATN vehicle under these conditions. Significant lateral forces and reaction spikes at Y-junctions were identified as risks to stability and passenger safety. A design improvement adding a rear bogie steering mechanism is proposed, effectively mitigating impact loads and stabilising lateral accelerations. Consequently, this study underscores the need for tailored dynamic analyses for structurally unique systems like ATN vehicles, ensuring safety, improving reliability, and advancing sustainable urban transit networks.
Light commercial passenger vans are commonly used in urban transportation, prioritising passenger comfort and effective handling. To ensure a smooth ride, especially over uneven road surfaces, optimising the van's suspension is essential. Key parameters like transmissibility ratio, suspension travel, and vertical acceleration must be regulated to maintain comfort. The suspension system significantly impacts vehicle dynamics and passenger experience, so selecting the right spring stiffness and dynamic frequencies is vital. This study uses a quarter-car model to determine the primary spring rates based on leaf spring deflection and shock absorber forces. The front suspension coil spring is also analysed, considering the front axle's characteristics. Optimising leaf spring rates based on Olley criteria minimises vibration isolation issues, ensuring better pitch and bounce frequencies. The positioning of front oscillation centres near the axles further enhances ride comfort and handling by improving suspension performance and minimising vibrations.
With the development of environmental protection concepts, the proportion of battery electric mining trucks is gradually increasing. However, the problems of charging mileage and battery life degradation limit the application. Meanwhile, production efficiency is also reduced due to slower charging and battery replacement speeds. The paper mainly focuses on the problem of decreased battery life and the shorter driving range of a 50-ton pure electric mining dump truck, considering the influence of the shifting strategy. A model predictive control (MPC) algorithm is proposed, which verifies that the MPC strategy can reduce energy consumption and improve battery lifetime by comparing the original rule-based control algorithm. The simulation results show that the proposed MPC strategy can reduce energy consumption by 16.92% and reduce battery life loss by 18.21% in the typical cyclic working conditions of mining roads.
