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To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function.

During the drivability test process, a large amount of noise generated by a series of internal and external factors of the vehicle reduces the accuracy of the drivability evaluation. To solve this problem, this paper introduces the EEMD denoising method and compares the denoising effects of the EMD denoising method and EEMD denoising method on the original signal using the entropy weight evaluation index. In addition, the optimal parameter setting is obtained by comparing the denoising results of different parameter settings in the EEMD denoising method. The results show that when the white noise is integrated 3000 times and the standard deviation of white noise is 0.1, the EEMD noise reduction method is the best, and the comprehensive score of noise reduction is 0.732 points higher than that of EMD.

The technology of active sound generation (ASG) for automobiles is one of the most effective methods to flexibly achieve the sound design that meets the expectations of different user groups, and the active sound synthesis algorithms are crucial for the implementation of ASG. In this paper, the Kaiser window function-based the harmonic synthesis algorithm of automobile sound is proposed to achieve the extraction of the order sounds of target automobile. And, the suitable fitting functions are utilized to construct the mathematical model between the engine speed information and the amplitude of the different order sound. Then, a random phase correction algorithm is proposed to ensure the coherence of the synthesized sounds. Finally, the analysis of simulation results verifies that the established method of the extraction and synthesis of order sound can meet the requirements of target sound quality.

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.

For cooperative adaptive cruise control (CACC) system, a robust following control algorithm based on fuzzy PID principle is adopted in this paper. Firstly, a nonlinear vehicle dynamics model considering the lag of driving force and acceleration constraints was established. Then, with the vehicle’s control hierarchic, the upper controller takes the relative speed between vehicles and the spacing error as inputs to output the following vehicle's target acceleration, while the lower controller takes the target acceleration as inputs and the throttle opening and brake master cylinder pressure as outputs. For the setting of target spacing, this paper additionally considers the relative speed between vehicles and the acceleration of the front vehicle. Through testing, compared with the traditional variable safety distance model, the average distance reduces by 5.43% when leading vehicle is accelerating, while increases by 2.74% in deceleration.

Regenerative braking is an effective way to increase the cruising range of vehicles. In commercial vehicles with large vehicle mass, regenerative braking can be maintained in a high-power working state for a long time theoretically because of the large braking torque and long braking time. But in fact, it is often impossible to run at full power because of battery safety problems. In this paper, a control strategy is designed to maintain the maximum power operation of regenerative braking as much as possible. The maximum charging power of the battery is obtained through the battery model, and it is set as the battery limiting parameter. The regenerative braking torque and power are obtained by using the motor model. The eddy current retarder is used to absorb the excess power that the battery can't bear, and the braking torque of the eddy current retarder is calculated. Finally, mechanical braking is used to make up the insufficient braking torque.

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.

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.

This paper addresses the brake pad particle emission during the braking process of a vehicle in motion. The frictional-constant contact between the disc brake and pads results in an increased temperature and wear of the pads. The emission of brake pad particles into the atmosphere leads to an increase in air pollution and hence becomes hazardous to the human body. In this paper, a wheel brake disc is installed in a ventilation system where the specific air flow is introduced in order to investigate the thermal performance and the emission of particles from the brake pads. A mathematical model using the fundamental parameters of the brake disc and ventilation system is established. The behavior of the heat transfer is studied using computational fluid dynamics (CFD). The particle emission rate from the pads is calculated under the assumption of uniform constant pressure distribution at the contact surface of the brake disc and pad.

When dump trucks unload dusty materials, dust particles with a diameter of 1 to 75 microns slide out of the dump body and float into the air. Dust particles naturally settle down spending a few hours, which causes air pollution. People who work in this environment daily suffer serious physical harm. To study the movement of dust particles during the unloading process, a scaled-down model is used to simulate the process of dump truck unloading gravel, and a high frame rate camera is used to record the movement trajectory of dust particles during the unloading process. In this paper, by observing the movement process of unloading dust particles by dump trucks, based on the principle of dynamics, a mathematical model describing the unloading of dust particles in the dump body and a mathematical model of the diffusion of dust particles in the air are established. Take the small gravel sampled at the construction site as an example of the experiment.

The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor.

Heat generated by friction between the brake discs and the brake pad causes the disc temperature to rise, which affects the braking performance. This flux generated from the contact surface of the vehicle brake disc not only affects the braking performance but also tends to be wasted and pollutes the environment. However, an accurate system is needed to make efficient use of this generated heat flux, which is usually wasted. Thermoelectric generators (TEGs) are solid-state gadgets utilized in the conversion of heat to electricity. Hence, the aim of this study is to convert the heat flux generated at the disc contact surface into electrical energy by employing a thermoelectric generator. In Addition, the energy harvested energy to power the battery, which in turn charges the temperature monitoring systems. Thermoelectric generators were positioned at different geometrical points of the brake discs to achieve optimal efficiency and energy storage possibilities.

Based on the technology of the order tracking and extraction, a software for the modulation of sound quality of automotive order sound is developed in this paper. The order analysis, the amplitude modulation of order sound in specific speed range and the calculation of the functional expression of the order sound amplitude are integrated in this software. Furthermore, a side-valve interpolation correction algorithm (SV-ICA) is proposed to solve the problem that the smooth transition of the order sound curve at endpoints cannot be guaranteed, which would result in the appearance of abnormal noise in the modulated sounds. The application of built software in constructing the automobile sounds with the sound quality of sportiness is demonstrated.

With the increasingly serious global environmental and energy problems, as well as the increasing number of vehicles, pure electric vehicles with its advantages of environmental protection, low noise and renewable energy, become an effective way to alleviate environmental pollution and energy crisis. Due to the current pure electric vehicle power battery technology is not perfect, the range of pure electric vehicle has a great limit. Through the braking energy recovery, the energy can be reused, the energy utilization rate can be improved, and the battery life of pure electric vehicles can be improved. In this paper, a pure electric vehicle is taken as the analysis object, and the whole vehicle analysis model is built. Through the comparative analysis, based on the driver's braking intention and vehicle running state, the braking energy recovery control strategy of double fuzzy control is proposed.

In order to study the influence of low-viscosity oil on automobile pollutant emissions reduction, three different 0W20 oil samples were prepared with oil 5W30 as the base oil. Parameters such as the oil viscosity, ash, and element content were tested at different stages, speeds, and accelerations of the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results showed the effects of low-viscosity oil on exhaust emissions reduction were mainly concentrated in the low-speed and extra high-speed segment. At the low-speed segment, especially in the starting stage, carbon monoxide (CO), total hydrocarbon (THC), and non-methane hydrocarbon (NMHC) emissions can be reduced. The use of low ash oil can reduce nitrogen oxides (NOx) emissions; the methane (CH4) emissions can be reduced by increasing the Zinc (Zn) content in engine oil moderately.

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.

The development of intelligent transportation improves road efficiency, reduces automobile energy consumption, and improves driving safety. The core of intelligent transportation is the two-way information interaction between vehicles and the road environment. At present, road environmental information can flow to the vehicle, while the vehicle’s information rarely flows to the outside world. The electronic throttle and electronic braking systems of some vehicles use sensors to get the state of the accelerator and brake pedal, which can be transmitted to the outside environment through technologies such as the Internet of Vehicles. But the Internet of Vehicles technology has not been widely used, and it relies on signal sources, which is a passive way of information acquisition. In this paper, an active identification method is proposed to get the vehicle pedal on-off state as well as the driver’s operation behavior through existing traffic facilities.

When driving in mountainous areas, vehicles often encounter long downhill sections. Due to the large mass of bus and the drum brake with poor heat dissipation effect, it is easy for bus to produce braking thermal decay in long downhill section, which makes the vehicle out of control and causes safety accidents. The braking methods of parallel hybrid electric bus include drum braking, engine braking and regenerative braking, whose torque models are established in this paper. The coasting test in Trucksim is used to verify the correctness of the engine braking torque model. Based on coupling braking torque curve with vehicle speed in different gradient, the stable speed is determined and the shift strategy is proposed. The temperature rise model of brake drum is established to analyze the temperature change of brake drum during long downhill. Then, according to the ramp data of G22 freeway, the above models are simulated.

Road traffic accidents resulting from alcohol-impaired driving are increasing globally despite several measures, currently in place, to curb the trend. For this reason, recent research aims at integrating alcohol early-detection systems and driving simulator experiments to identify intoxicated drivers. However, driving simulator experiments on drunk driving have focused mostly on male participants than female drivers whose characteristics have scarcely been explored. Hence in this paper, vehicle dynamic control inputs on steering, braking, and acceleration performance of 75 licensed female drivers with an upshot of alcohol at four different blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) were investigated. The participants completed simulated driving in a fixed-based simulator experiment coupled with real-time ecological scenarios to extract discrete responses.