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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.

Preventing skidding is essential for studying the safety of driving in curves. However, the adhesion of the vehicle during the driving process on the wet and slippery road will be significantly reduced, resulting in lateral slippage due to the low adhesion coefficient of the road surface, the speed exceeding the turning critical, and the turning radius being too small when passing through the corner. Therefore, to reduce the incidence of traffic accidents of passenger cars driving in curves on rainy and snowy days and achieve the purpose of planning safe driving speed, this paper proposes a curve active safety system based on a deep learning algorithm and vehicle dynamics model. First,we a convolutional neural network (CNN) model is constructed to extract and judge the characteristics of snow and ice adhesion on roads.

With the improvement of sensor accuracy, sensor data plays an increasingly important role in intelligent vehicle motion control. Good use of sensor data can improve the control of vehicles. However, data-based end-to-end control has the disadvantages of poorly interpreted control models and high time costs; model-based control methods often have difficulties designing high-fidelity vehicle controllers because of model errors and uncertainties in building vehicle dynamics models. In the face of high-speed steering conditions, vehicle control is difficult to ensure stability and safety. Therefore, this paper proposes a hybrid model and data-driven control method. Based on the vehicle state data and road information data provided by vehicle sensors, the method constructs a deep neural network based on LSTM and Attention, which is used as a compensator to solve the performance degradation of the LQR controller due to modeling errors.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

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.

The sweeper vehicle plays a very key role in maintaining the urban environment. If the sweeper vehicle can accurately and efficiently identify and classify the ground garbage in the working process, it can greatly improve the working efficiency of the sweeper vehicle and reduce the consumption of manpower. Although the deep learning algorithm based on DUC and PSPNet has high accuracy, the recognition speed is low. ENet is a lightweight network, which greatly improves efficiency, but significantly sacrifices accuracy. This paper presents an improved real-time detection lightweight network based on PSPNet, which takes into account the operation speed and accuracy. The network takes PSPNet as the backbone network, and increases the stride in the convolution process, to reduce the size of the feature map and reduce the amount of calculation.

With the rapid development of new energy vehicles, the echelon utilization of retired power battery has become an important factor to promote the healthy development of this industry, while the Remaining Useful Life (RUL), as the key reference factor for the echelon utilization of retired power battery, has attracted the attention and research of many scholars in recent years. At present, most prediction methods are based on off-line data, which cannot process real-time data in time, so it is difficult to realize online prediction of RUL. In order to realize the real-time online monitoring and high-precision calculation of lithium-ion battery RUL, this paper proposes a lithium-ion battery RUL prediction method based on data-driven and multi-model fusion. The one-dimensional Convolutional Neural Network (1D_CNN) is used for fast online feature extraction of one-dimensional battery capacity time series data to mine potential hidden information.

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.

As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model.

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables.

With the rapidly developing automotive industry and stricter environmental protection laws and regulations, lightweight materials, advanced manufacturing processes and structural optimization methods are widely used in body design. Therefore, in order to evaluate and improve the pedestrian protection during a collision, this paper presents an impact simulation modeling and structural optimization method for a sport utility vehicle engine hood made of carbon fiber reinforced plastic (CFRP). Head injury criterion (HIC) was used to evaluate the performance of the hood in this regard. The inner panel and the outer panel of CFRP hood were discretized by shell elements in LS_DYNA. The Mat54-55 card was used to define the mechanical properties of the CFRP hood. In order to reduce the computational costs, just the parts contacted with the hood were modeled. The simulations were done in the prescribed 30 impact points.

The thermal management system is essential for the safe and long-term operation of the power battery. The temperature difference between the individual cells exceeds the acceleration of the battery performance, which leads to battery out of use and affects the performance of the vehicle. Compared with the low heat transfer coefficient of the air-cooling system, the complex structure of the liquid-cooling system and the large quality of phase change material system, the heat pipe has high thermal conductivity, strong isothermal performance and light weight, it’s an efficient cooling element that can be used for thermal management. In this study, the flat plate heat pipe(FPHP) is used to manage the temperature of the battery, through experiments, the optimized placement of the flat heat pipe is obtained.

How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements.

With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial.

Magnetorheological fluid (MRF) is used as the transmission medium of the hydraulic retarder. The rheological properties are regulated by changing the magnetic field to achieve accurate control of the retarder's braking torque. Under the action of the external magnetic field, the flow structure and performance of the MRF retarder will be changed in a short time. The apparent viscosity coefficient increases by several orders of magnitude, the fluidity deteriorates and the heat generated by the brake cannot be transferred through the liquid circulation, which will affect the braking torque of the retarder. Changing the surface area of the stator also has an influence on the braking torque of the retarder and the wall heat dissipation. In this study, the relationship between the braking torque of the MRF retarder and the stator surface area of the retarder was analyzed.

Driver distraction detection is crucial to driving safety when autonomous vehicles are co-piloted. Recognizing drivers’ behaviors that are highly related with distraction from real-time video stream is widely acknowledged as an effective approach mainly due to its non-intrusiveness. In recently years, deep learning neural networks have been adopted to by-pass the procedure of designing features artificially, which used to be the major downside of computer-vision based approaches. However, the detection accuracy and generalization ability is still not satisfying since most deep learning models extracts only spatial information contained in images. This research develops a driver distraction model based on a two-stream, spatial and temporal, convolutional neural network (CNN).

In order to avoid the braking recession on heavy commercial vehicles caused by the long-distance continuous braking of the main brake, the hydraulic retarder is widely used as an important brake auxiliary device in various heavy commercial vehicles to improve the vehicle safety. However, the hydraulic retarder not only has the advantages of large braking torque and good stability, but also has the disadvantages of poor retarding ability at low rotating speed, braking lag and difficulty in accurately controlling the braking torque. This paper introduces a new type of hydraulic retarder. The new retarder replaces the oil in the retarder with magnetorheological fluid and applies a magnetic field in the retarder arrangement space, so that slows down the vehicle by using the rheological properties of the magnetorheological fluid under the magnetic field.

The high gravity center of the concrete mixer truck reduces the truck’s stability while steering. The rolling stirring tank makes the stability even worse than the regular engineering vehicle due to the dynamic variation of the centroid position. Most of the researches on the rollover stability of concrete mixer trucks focus on the rollover model establishment and dynamic simulation module. The change of concrete centroid is ignored when the safety cornering speed is calculated. This paper proposes a pre-warning method for the cornering speed of concrete mixer trucks based on centroid dynamic simulation. In the method, the mixing tank stirring model and the vehicle driving dynamic model are established on the Fluent and TruckSim simulation platforms, respectively. The theoretical speed threshold obtained by simulation is used as the evaluation index of the warning speed in the curve. Firstly, the dynamic simulation of the stirring tank model is carried out by Fluent.

Electronic Stability Control (ESC) is an important measure to proactively guarantee vehicle safety. In this paper, the method of four-wheel hub-motor torque control is compared with the traditional single-wheel hydraulic brake control in ESC system. The control strategy adopts the hierarchical structure. In upper controller, the stability of the vehicle is identified by threshold method, the additional yaw moment control uses a way to get the moment including feedforward and feedback parts based on the linear quadratic regulator (LQR). The medium controller is tire slip rate control, in order to get the optimal target slip rate from the upper additional yaw moment, a method of quadratic programming to optimize the longitudinal force is proposed for each wheel. The inputs of tire state for the magic tire model is introduced so as to calculate the target slip rate from the target longitudinal force.

In these years, the advantages of using phase change material (PCM) in the thermal management of electric power battery has been wide spread. Because of the thermal conductivity of most phase change material (eg.wax) is low, many researchers choose to add high conductivity materials (such as black lead). However, the solid-liquid change material has large mass, poor flow-ability and corrosively. Therefore, it still stays on experiential stage. In this paper, the Thermal characteristics of power battery firstly be invested and the requirements of thermal management system also be discussed. Then a new PCM thermal management has been designed which uses pure water as liquid phase change material, adopts PCM with a reflux device for thermal management.