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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.

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.

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.

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.

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 acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

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.

Lane detection is an important component in automatic pilot system and advanced driving assistance system (ADAS). The stability and precision of lane detection will directly determine precision of control and lane plan of vehicles. Traditional mechanical vision lane detection approaches in complicated environment have the deficiencies of low precision and feature semantic description disabilities. But the lane detection depending on deep learning, e.g. SCNN network, LaneNet network, ENet-SAD network have imbalance problems of splitting precision and storage usage. This paper proposes an approach of high-efficiency deep learning Segnet-LSTM semantic segmentation network. This network structure is composed with encoding network and corresponding decoding networks. First, convolution and maximum pooling. The proposal extracts texture details of five images and stores searching position of maximum pooling. Meanwhile, it will implement interpolate processing to the lost points.

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".

In winter, the temperature of the coldest month is below -20°C. Low temperature makes it difficult to start a diesel engine, combust sufficiently, which increases fuel consumption and pollutes the environment. The use of an electric power-driven auxiliary heating system increases the battery load and power consumption. Solar thermal energy has the advantages of easy access, clean and pollution-free. The coolant in the cylinder block of the diesel engine has a large contact area within the cylinder and is evenly distributed, which can be used as a heat transfer medium for the warm-up. A one-dimensional heat transfer model of the diesel engine block for the coolant warm-up is developed, and the total heat required for the warm-up is calculated by an iterative method in combination with the warm-up target.

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.

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.

In ITS, image processing technology is applied to a wide variety of areas such as visual-based intelligent vehicle navigation, visual-based traffic monitoring and visual-based traffic management. In the recognition system of the vehicle body characteristics, most of the recognition is the license plate and the car emblem, etc. This paper proposes an image recognition application method for the vertical motion of the car while driving, mainly including vertical height detection and vertical displacement velocity acceleration recognition. The edge detection model of the image object is established by using the gray image to obtain the car motion segmentation image. At the same time, an image length and actual length coordinate conversion model is established, which can calculate an arbitrary actual length of the image object. In this paper, Yuejin Shangjun X500 van was selected as the test vehicle, and the video data was captured with a camera.

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.

When driving in mountainous areas, vehicles often encounter downhill conditions. To ensure safe driving, it is necessary to control the speed of vehicles. For internal combustion engine vehicles, auxiliary brake such as engine brake can be used to alleviate the thermal load caused by the continuous braking of the friction brake. For battery electric vehicles (BEVs), regenerative braking can be used as auxiliary braking to improve brake safety. And through regenerative braking, energy can be partly converted into electrical energy and stored in accumulators (such as power batteries and supercapacitors), thus extending the mileage. However, the driver's line of sight in the mountains is limited, resulting in a certain degree of blindness in driving, so it is impossible to fully guarantee the safety and energy saving of downhill driving.

Li-ion power battery is the core component of the electric vehicle power system, and the battery temperature will increase because of the electrochemical reaction of the Li-ion battery. The heat accumulates inside of the battery, which can degrade the working performance of the power battery and shorten the battery cycle life. At present, the wind cooling technology is relatively mature. However, it cannot achieve ideal heat dissipation effect under the working conditions of the high-power or high ambient temperature. In this research, the battery thermal management is carried out by the characteristics of the working fluid’s flowing phase change heat transfer. The phase change working fluid is R-1233zd(E) which is a kind of environmentally friendly liquid with nonconductive and nonflammable. It can achieve the purpose of controlling the battery’s temperature using the characteristics of isothermal heat absorption under different gas phase rate of phase change working fluid.

It’s not easy to start the engine in winter, especially in frigid highlands, because the low temperature increases the fuel’s viscosity, decreasing the lubricating oil flow ability and the storage performance of battery. Current electrical heating method can improve the engine starting performance in low temperature condition, but this method adds an external power to the engine, leading to the engine cannot maintain an efficient energy utilization. A warming device using the solar energy is designed to conserve the energy during the daytime, and directly warm up the engine at the time when the engine turns off for a long time, especially during the night. A solar collector installed on the top of the vehicle is used to convert the solar energy to the thermal energy, which is then transferred to the heat accumulator that contain the phase-change medium which can increase the heat storage performance.