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In the racing world, speed is everything, and the Formula Student cars are no different. As one of the key means to improve the speed of the car, lightweight plays an important role in the racing world. The weight reduction of unsprung metal parts can not only improve the driving speed, but also effectively optimize the dynamic of the car, so the lightweight design of unsprung parts has attracted much attention. In the traditional Formula Student racing car, the hub and spoke are two independent parts, they are fixed by four hub bolts or a central locking nut, the material of these fasteners is usually steel, so it brings a lot of weight burden. In order to achieve unsprung lightweight, a new type of wheel part design of Formula Student racing car is proposed in this paper. The hub and spoke are designed as integrated aluminum alloy parts, effectively eliminating the mass of hub bolts or central locking nuts.

Gear vibration and noise evaluation approaches are commonly proposed with a variety of way in a transmission system while complicated and obscured, it is difficult to provide a feasible basis for the gear development of E-drive system.

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.

In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem.

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.

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.

Dangerous driving behavior will cause serious traffic accidents, which will not only threaten life and property but also cause traffic congestion and reduce road capacity. Speed detection is an important detection method to identify whether a driver is driving dangerously. Traditional speed detection methods need additional sensors, which will increase the cost of speed measurement. This paper proposes a vehicle speed estimation algorithm based on the imaginary projection plane (IPP). The IPP will be established according to the height, field angle, and vertical tilt angle of the camera and will be used to establish the mapping relationship between the world coordinates and image coordinates of the vehicle. By combining YOLOv4 and DeepSORT, the vehicle license plate is detected and tracked, and the center point of the vehicle license plate is taken as the feature point of vehicle speed estimation. The vehicle speed is estimated according to the IPP.

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.

According to the requirements of high transmission ratio and high load torque of high-speed electric drive axle planetary gear system, the design and analysis of fine-pitch planetary gear system with small modulus, small pressure angle and high full tooth height of are carried out. In order to improve the bearing capacity of gear and reduce gear meshing noise, the tooth profile modification parameters of gear system are optimized. In this paper, the tooth modification methods are analyzed and the gear train parameters are determined. The influence degree of different tooth modification methods on the transmission performance of the gear train is determined by orthogonal experiment method. The transmission error is reduced, the stress fluctuation is improved, and the gear meshing performance is greatly improved by adopting the appropriate modification scheme, which proves the effectiveness of the tooth modification scheme.

With the development of economy, the road cleaning faces great challenges because the road area keeps increasing and the road types tend to be diversified. Cleaning vehicle is widely used in road surface cleaning, but it is more and more difficult to meet the demand of road surface cleaning only through using a single road surface cleaning method. If the way of manual adjustment of cleaning parameters is adopted, the driver is required to have rich experience. At present, there is an urgent need for a cleaning vehicle that can autonomously adjust cleaning parameters according to the road surface. This study is based on road recognition technology. After the pavement category is reflected by the visual sensor feedback information and the pavement adhesion coefficient, the parameters of the cleaning vehicle are adjusted by the controller to adapt to different roads.

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.

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle.

In summer, the relatively low temperature water condenses in the evaporator when the vehicle air-conditioning (AC) is running. At present, the vehicle AC condensate water without well utilization is directly wasted. The condenser’s thermal transfer performance has a great influence on the AC performance, and to increase the convective heat transfer coefficient (CHTC) is the key to its design. In this paper, a method of using atomized condensate water (CW) to enhance the condenser’s thermal transfer performance is proposed, which can make the most of the CW's cold energy. It achieves the reuse of CW and increases the condenser’s CHTC. First, the CW flow calculation model in the evaporator and the calculation model of the condenser enhanced thermal transfer using atomized CW are both set up. The influence of the evaporation degree of atomized CW particles in the air on the enhancement effect is comprehensively considered.

The road feature has an important influence on the safe speed of the unmanned vehicle and the safe space between two vehicles. Real-time access to the features of the road ahead of time and timely adjustment of engine torque are significant to unmanned driving. Most of the researches nowadays make full use of vehicle sensor technology and environment perception technology. Vehicle sensor is widely used to collect the features of the road. While in this paper, a new type of road feature extraction is proposed based on vehicle speed change. Under the premise of less sensor installed, vehicle speed-time data series is collected. The pavement parameters can be estimated with vehicle speed. Based on the vehicle dynamics, this paper studies the relationship between vehicle speed and rolling resistance. Different road features have different influences on road friction resistance.

The closed cabin temperature is anticipated to be cooled down when it is a bit hot inside the driving car. The traditional air-condition lowers the cabin temperature by frequently switching the status of the compressor, which increases the engine’s parasitic power and shortens the compressor’s service-life. The semiconductor auxiliary cooling system with the properties of no moving parts, high control precision and quick response has the potential to assist the on-board air-condition in modulating the cabin temperature with relative small ranges. Little temperature differences between the cabin and the outside environment means that the system energy consumption to ensure the occupant comfort is relatively low and the inefficiency could be made up by the renewable energy source.

In vehicles with urea-SCR system, normal operation of the urea-SCR system and engine will be influenced if there are deposits appearing on exhaust pipe wall. In this paper, a commercial vehicle is employed to study the influence factors of deposits through the vehicle road test. The results show that, urea injection rate, temperature and flow field have impacts on the formation of deposits. When decreasing the urea injection rate of calibration status by 20%, the deposit yield would reduce by 32%. If the ambient temperature decreased from 36 °C to 26 °C, the deposit yield would increase by 95%. After optimizing the exhaust pipe downstream of the urea injector by removing the step surface, only a few flow marks of urea droplets are observed on the pipe wall, and no lumps of deposits existing.

In order to overcome hysteresis and dead zone problems caused by friction for the proportional solenoid valve, and improve rapidity and stability of the pneumatic system on hydraulic retarder, a closed-loop control strategy based on valve coil current was proposed. The high-frequency low-amplitude dither signal was introduced into the proportional solenoid valve. With the proper dither signal, the stick-slip motion of the valve core was transformed into a steady one, and its dynamic performance was improved. Consequently, response time of retarder was reduced during gear changing. The proportional valve coil current was measured as a feedback for a closed-loop control strategy. Combining with the closed-loop strategy, the PI control algorithm was adopted to make sure that valve current was in accordance with the target value. Pulse Width Modulation (PWM) signal was used for the driving of proportional solenoid valve.

Several factors including internal factors which are related to the structure and components of transmission and external factors which are related to the running condition influence transmission efficiency (TE) collectively. Selected one manual transmission as the research object, this paper mainly analyzes factors including gears and bearings power loss through theoretical calculation and the external factors, such as gears, temperature and torque. Firstly, with a methodology, the overall efficiency of the manual transmission is calculated based on factors. Then, this paper discusses efficiency through external factor. This transmission is experimented on transmission test bench. On the bench, the driving motor (DM) simulates the power input of engine and the load motor (LM) simulates the whole resistance of vehicle. The mechanical transmission is operating in different speeds, torques and work temperature, thus the corresponding data are obtained.

Glass lifter is a key part of automobile door system. Guide rail is the carrier of glass lifter, and it bears various load cases when glass lifer works. Mass, stiffness and natural frequencies are the factors that influence the performance of glass lifter. In order to design a lighter and reasonable glass lifter, topology optimization methods are studied in this paper. In a rope-wheel glass lifter, design domain is determined by the mechanical structure and working conditions. Firstly, the single target continuum structure topology optimization mathematic models of guide rail are built in this paper, and analysis of multi-stiffness topology optimization are carried out accordingly in which volume fraction is set as 0.4, 0.5 and 0.6. These models are based on SIMP (Solid Isotropic Material with Penalization) theory.

As a key component of airstream system equipped in the road sweeper, the structure of the suction nozzle determines its internal flow field distribution, which affects the dust-sucking efficiency to a great degree. This research is aiming to determine a better suction nozzle structure. Starting with an analysis of the one used in a certain type of road sweeper, the initial model of the suction nozzle is established, and the internal flow field is simulated with typical computational fluid dynamics (CFD) software named FLUENT. Based on the simulation results, the dust-sucking capability of the initial structure is evaluated from the aspects of pressure and velocity distribution. Furthermore, in order to explore the influence of different structural parameters on the flow field distribution within the suction nozzle, models with different cavity heights and shoulder angles are established, and Univariate Method is utilized to analyze the contrast models.