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The multi-functional mechanical arm equipped on engineering vehicle can achieve different functions by installing different mechanism devices through the interface at the end of the mechanical arm. It can achieve functions like engineering construction and road rescue. Mechanical arm systems often work in complex environments, which requires good reliability and safety of the boom system. When the mechanical boom is working, the pressure of each luffing cylinder is large, and the contact force and acceleration of each boom are complex, which requires a certain degree of verification and optimization before it can be put into production. In this paper, a virtual prototype of a vehicle mounted hydraulic mechanical arm with four booms is established. Through ADAMS, the dynamic analysis of mechanical arm under multiple working conditions is carried out, the movement parameter changes and the pressure changes of each luffing cylinder are analyzed.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

In the process of truck overload and over-limit detection, it is necessary to detect the characteristics of the vehicle's size, type, and wheel number. In addition, in some vehicle vision-based load recognition systems, the vehicle load can be calculated by detecting the vibration frequency of specific parts of the vehicle or the change in the length of the suspension during the vehicle's forward process. Therefore, it is essential to quickly and accurately identify vehicle features through the camera. This paper proposes a vehicle feature recognition method based on image semantic segmentation and Python, which can identify the length, height, number of wheels and vibration frequency at specific parts of the vehicle based on the vehicle driving video captured by the roadside camera.

In new energy vehicles, the electric motor, as the main power source, is developing toward high power density. However, its heat generation problem always affects the overall performance of the motor, so an efficient motor cooling system is especially important. In desert or water-scarce areas, liquid cooling cannot meet the needs of new energy vehicle motor cooling. When glycol or other liquid coolants are low or depleted, motor heat dissipation becomes less effective. Heat pipe is a heat dissipation technology with advantages such as fast thermal response and light weight. In this paper, by improving the heat pipe arrangement and reducing the overall mass of the heat dissipation system, a heat pipe optimization design based on a drive motor heat dissipation scheme is proposed, and the overall stability of the motor working under high temperature conditions is improved.

The articulated steering system is widely used in engineering vehicles due to its high mobility and low steering radius. The design parameters have a vital impact on the selection of the steering system assemblies, such as the operation stroke, pressure, and force of the hydraulic cylinders during the steering process, which will affect the system weight. The system energy consumption is also relevant to the geometry parameters. According to the kinetic analysis of the steering system and dynamic analysis of the steering process, the kinetic model of an engineering vehicle steering system is built, and the length and pressure variation of the cylinder is calculated and validated by the field test. The influence of the factors is analyzed based on the established model. To lower the system weight, needed pressure, and force, the multi-objective particle swarm optimization method is initiated to optimize the geometry parameter of the articulated steering system.

Optimization of the steering trapezoid mechanism parameter has great significance for improving vehicular handling performance and steering safety. The mathematical model of the current trapezoid mechanism design is oversimplified; Thus, the value of the optimum parameter is often not achievable. In this paper, a design model for the trapezoidal steering mechanism is proposed taking into consideration the size and kinematic constraints. Based on combining Ackerman's principle and spatial geometric relation, a multi-body dynamics design method is used to derive a nonlinear optimization model of the split steering trapezoid mechanism. In this investigation, a hybrid genetic algorithm is developed to minimize the steering error and the corresponding optimum design parameters. The selected design parameters are the bottom angle and the steering arm length of steering trapezoid mechanisms.

As one of the core components of electric vehicle, the performance of power battery is largely determined by thermal management system. Air cooling is difficult to meet the heat dissipation requirements of high-power power batteries. Liquid cooling arrangement is complex and requires high sealing performance. Phase change materials will increase the mass of battery packs. Heat pipes have good heat conduction, temperature equalization performance and light weight, and it is an ideal cooling and heat dissipation technology with efficient cooling fins. In this paper, a thermal management system of power battery based on heat pipe and fin is proposed. The maximum temperature and wall temperature difference of power battery are reduced by heat pipe and fin heat dissipation. The influence of different fin spacing and heights on the thermal management system is studied, and then the fin spacing and height are optimized.

At present, many electric vehicles are often equipped with only a single-stage final drive. Although the single-stage speed ratio can meet the general driving requirements of electric vehicles, if the requirements of the maximum speed and the requirements for starting acceleration or climbing are met at the same time, the power demand of the drive motor is relatively large, and the efficient area of the drive motor may be far away from the operating area corresponding to daily driving. If the two-speed automatic transmission is adopted, the vehicle can meet the requirements of maximum speed, starting acceleration and climbing at the same time, reduce the power demand of the driving motor, and improve the economy under certain power performance. This is especially important for medium and large vehicles.

Automotive Front Lighting System(AFS) can receive the steering signal and the vehicular speed signal to adjust the position of headlamps automatically. AFS will provide drivers more information of front road to protect drivers safe when driving at night. AFS works when there is a steering signal input. However, drivers often need the front road's information before they turn the steering wheel when vehicles are going to go through a sharp corner, AFS will not work in such a situation. This paper studied how to optimize the working time of AFS based on GIS (Geographic Information System) and GPS(Geographic Information System) to solve the problem. This paper analyzed the process of the vehicle is about to go through a corner. Low beams and high beams were discussed respectively.

The drivers' hysteretic perception to surrounding environment will affect vehicular fuel economy, especially for the heavy-duty vehicles driving under complex conditions and long distance in mountainous areas. Unreasonable acceleration or deceleration on the slope will increase the fuel consumption. Improving the performance of the engine and the transmission system has limited energy saving potential, and most fuel-efficient driving assistant systems don't consider the road conditions. The main purpose of this research is to introduce an economic driving scheme with consideration of the prestored slope information in which the vehicle speed in mountainous slopes is reasonably planned to guide the driver's behavior for reduction of the fuel consumption. Economic driving optimization algorithm with low space dimension and fast computation speed is established to plan accurate and real-time economic driving scheme.

The Organic Rankine Cycle System (ORC) is an effective means to use the solar energy. The system adopts the solar energy on the car roof as the heat source to make the ORC work and drive the thermoelectric air-conditioner. It can improve the entering comfort on the parking condition and the vehicle energy utilization efficiency. In this research, the system comprehensively applied the principle of sunshine concentration, heat collection and photo electricity. Then considering the working condition and performance features of ORC system, the car roof was designed to have a compact structure, through which the efficiency of the solar vehicle system could be improved. Firstly, the research analyzed the heat source temperature and the heat flux impact on the output power of the ORC system. After that, the performance of heat collection was identified according to the given thermoelectric air-condition’s power requirements.

According to the resonant pavement crusher's work principle, its front frame mounted with the resonance system must meet the needs of the structural requirements. To satisfy the strength and stiffness requirement and avoid the resonance, the natural frequency of the front frame should be designed away from the crusher's working frequency. In this paper, the author builds a finite element model of the front frame and analyses its modal. According to the modal analysis results, the fourth modal frequency is close to the working frequency of the crusher. So the front frame should be optimized. In the finite element model, the front frame has been divided into a number of components of shell elements. Through optimal Latin hypercube experimental design, the author analyses the different component thickness's relationship of the frequencies of the front frame. The components with higher correlation coefficient have been chosen as the variables of optimization.