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With the development of highway transportation and automobile industry technology, highway truck overload phenomenon occurs frequently, which poses a danger to road safety and personnel life safety. So it is very important to identify the overload phenomenon. Traditionally, static detection is adopted for overload identification, which has low efficiency. Aiming at this phenomenon, a dynamic overload identification method is proposed. Firstly, the coupled road excitation model of vehicle speed and speed bump is established, and then the 4-DOF vehicle model of half car is established. At the same time, considering that the double input vibration of the front and rear wheels will be coupled when vehicle passes through the speed bump, the model is decoupled. Then, the vertical trajectory of the body in the front axle position is obtained by Carsim software simulation.

This paper proposed a model predictive control(MPC) based car-following control strategy for electric vehicles considering comfort, in order to improve the comfort of the car-following control system of electric vehicles. The MPC algorithm is improved in the following three aspects to improve the comfort: Firstly, a five-state longitudinal car-following model is adopted, so that the MPC algorithm can optimize the acceleration and acceleration change rate of the ego vehicle. Secondly, for the weight coefficients of the output vector and the input vector of the objective function, the fixed weight coefficients are changed into variable weight coefficients by the way of Nash equilibrium game, so that the control system can improve the weight of the parameters used to control the comfort under suitable driving conditions.

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.

The sweeping vehicle has made a great contribution to the cleaning of urban roads. The traditional electric sweeping vehicle uses the main and auxiliary motors to drive the driving system and the operating system respectively. However, because the sweeper is in a low-speed working condition for a long time, and the drive motor must meet the demand for high power, there exist problems of low motor utilization and high cost. Aiming at this phenomenon, a dual-motor power coupling system based on planetary gears is proposed. First, analyze the driving mode of the dual-motor coupling power system according to the actual working scheme of the sweeper, and match the parameters of the motor based on this. Second, on the premise of meeting the power requirements, analyze and divide the working range of each drive mode based on the principle of minimum energy consumption, and then obtain the best drive mode switching control and speed and torque distribution strategy.

With the rapid development of the logistics and transportation industry, heavy-duty trucks play an increasingly important role in social life. However, due to the characteristics of large cargo loads, high center of mass and relatively narrow wheelbase, the driving stability of heavy trucks are poor, and it is easy to cause rollover accidents under high-speed driving conditions, large angle steering and emergency obstacle avoidance. To improve the roll stability of heavy trucks, it is necessary to design an active anti-rollover control system, through the analysis of the yaw rate and the load transfer rate of the vehicle, driving states can be estimated during the driving process. Under the intervention of the control system, the lateral transfer rate of heavy trucks can be reduced to correct the driving posture of the vehicle body and reduce the possibility of rollover accidents.

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.

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 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.

Heavy commercial vehicle drivers may frequently shift gears when they are running on long and downhill roads in mountainous area. In order to improve driving safety and fuel economy, it is necessary to predict the slope of the road ahead in real time and correct the driver's shift strategy in time. At present, the road slope estimation is mainly based on the real-time estimation of the road slope at the current position of the vehicle based on the vehicle driving information obtained by the sensors, but the road slope of the road section that the vehicle is about to reach has not been predicted. In this paper, based on the road slope information of the road section that the driver has driven through, combined with Geographic Information System (GIS) information and road design standards, the slope of the road section ahead is predicted.

The tank truck has a wide range of application. When the liquid in the tank is not fully loaded, the lateral movement of the liquid in the tank will shift the center of gravity of the tank truck and make the vehicle less safe. It is easy to roll over when the tank truck is turning. This study combines the vehicle dynamic characteristics and geographic information, which gives the driver safe speed and safe braking distance tips before turning, to reduce the traffic accidents caused by driver's misjudgment. The dynamic model of the tank truck is established, through collecting the real-time information of the vehicle, the vehicle load and braking torque are calculated by the relevant dynamic model. The system needs to measure the deviation of the center of gravity in the tank truck movement process, and the deviation of the center of gravity has a great influence on the safety speed.

Lane changing manoeuvers is an essential rudiment in vehicle driving and has a significant impact on the characteristics of traffic flow. In the case of traditional cars, the driver operates the vehicle to complete the lane change whilst for autonomous vehicles, completing the lane change requires planning the lane change trajectory and controlling the vehicle speed during the lane change. Unreasonable lane change trajectory and vehicle speed may cause the vehicle to lose stability, threaten driving safety, increase energy consumption and waste energy. This paper considers the safety and economy of the lane changing process, and proposes a new lane changing method for vehicles.

With the increasingly serious problems of environmental pollution and energy shortage, electric vehicles have a promising future. As a core component of electric vehicles, the drive motor is developing towards high power density of which remains temperature rise problems, which affects the performance, efficiency and service life of the drive motor. Liquid cooling has high energy consumption and poor reliability. The heat-pipe has excellent heat conduction and temperature uniformity capabilities. Therefore, this paper proposes a heat pipe-based drive motor heat dissipation system to make the heat-pipe act on the inside of the motor to reach a specified range of driving conditions. The drive motor can better dissipate heat through the heat-pipe. Firstly, analysis of the internal heat generation mechanism of the motor, heat transfer characteristics of the heat-pipe and the heat-pipe layout plan was established.

The non-contact overload recognition method refers to the method of detecting the vibration state of the vehicle through visual recognition without touching the vehicle, and then calculating the vehicle load in combination with the vehicle dynamics model to determine whether the passing vehicle is overloaded. Due to the convenience of detection, low cost of infrastructure and informatization, this method has great advantages in the field of overload identification. However, the model used in this recognition method is the single mass vibration model at present, which will have a large error due to the interaction between the front and rear suspension, and the position of the center of mass needs to be acquired in the recognition process, which is difficult in the actual identification process. In this paper, a vehicle vibration model containing two modes of vibration is proposed, and uses Sobol algorithm to analyze the parameter sensitivity of the model.

There are a large number of curves and slopes in the mountainous areas. Unreasonable acceleration and deceleration in these areas will increase the burden of the brake system and the fuel consumption of the vehicle. The main purpose of this paper is to introduce a speed planning and promotion system for commercial vehicles in mountainous areas. The wind, slope, curve, engine brake, and rolling resistances are analyzed to establish the thermal model of the brake system. Based on the thermal model, the safe speed of the brake system is acquired. The maximum safe speed on the turning section is generated by the vehicle dynamic model. And the economic speed is calculated according to the fuel consumption model. The planning speed is provided based on these models. This system can guide the driver to handle the vehicle speed more reasonably.

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.

Regenerative braking is an effective technology to extend the driving range of electrified vehicles by recovering kinetic energy from braking. This paper focuses on the design of the regenerative braking control strategy for a commercial vehicle which requires significantly larger braking power than passenger cars. To maximize the energy recovery while ensuring the braking efficiency of the vehicle and its braking safety, this paper proposed a fuzzy logic strategy for regenerative braking control, and a feasibility study was conducted for an electric van. The work includes in three steps. Firstly, state variables that significantly affect regenerative braking performance, i.e., vehicle speed, battery State-of-Charge (SOC), and braking intensity, are identified based on mathematical modelling of the vehicle system dynamics in braking maneuver.

Braking energy recovery is an important method for Battery Electric Vehicle (BEV) to save energy and increase driving range. The vehicle braking system performs regenerative braking control based on driver operations. Different braking operations have a significant impact on energy recovery efficiency. This paper proposes a method for planning the braking process of a BEV based on the Intelligent Vehicle Infrastructure Cooperative System (IVICS). By actively planning the braking process, the braking energy recovery efficiency is improved. Vehicles need to decelerate and brake before entering a curve. The IVICS is used to obtain information about the curve section ahead of the vehicle's driving route. Then calculating the reference speed of the curve, and obtaining the vehicle's braking target in advance, so as to actively plan the vehicle braking process.

With the shortage of energy and the continuous development of automotive technology, electric vehicles are gradually gaining popularity. The energy of electric vehicles mainly comes from the power grid, and its large-scale use is inseparable from the support of the power system. However, electric vehicles consume power quickly, have short driving ranges, and frequently charge, and there are plenty of problems such as disorder and randomness in charging, which is not conducive to rational planning of the power grid. Optimizing the charging problem of electric vehicles can not only save power resources but also bring huge economic benefits to operators of charging stations. In this paper, the CAN bus communication protocol, combined with GPS positioning, LabVIEW monitoring, GPRS transmitting and other technical means, can realize the information exchange of the "vehicle-charging device-distribution network".

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.