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In summer, when vehicle parks in direct sunlight, the closed cabin temperature would rise sharply, which affects the occupants step-in-car comfort Solar powered vehicle parking ventilation system adopts the solar energy to drive the original ventilator. Thus, the cabin temperature could be dramatically decreased and the riding comfort could be also improved. This research analyzed the modified crew cabin thermal transfer model. Then the performance of the solar powered ventilation system is analyzed and optimized combined with the power supply characteristics of the photovoltaic element. The storage and reuse of the solar power is achieved on condition that the cabin temperature could be steadily controlled. The research shows that, the internal temperature is mainly affected by the solar radiation intensity and the environment temperature.

Driving comfort is one of the most important indexes for automobile comfort. Driving posture comfort is closely related to the drivers' joint angles and joint torques. In present research, a new method is proposed to identify the most comfortable driving posture based on studying the relation between drivers' joint angles and joint torques. In order to truly reflect a driving situation, the accurate human driving model of 50 percent of the size of Chinese male is established according to the human body database of RAMSIS firstly. Biomechanical model based on accurate human driving model is also developed to analyze and obtain dynamic equations of human driving model by employing Kane method. The joint torque-angle curves of drivers' upper and lower limbs during holding wheel or pedal operation can be obtained through dynamic simulation in the MATLAB. Through curve-fitting analysis, the minimum joint torque of a driver' limb and the optimal joint angel can be found.

Typical vehicle speed deceleration occurs at the freeway exit due to the driving direction change. Well conducting the driver to control the velocity could enhance the vehicle maneuverability and give drivers more response time when running into potential dangerous conditions. The freeway exit speed limit sign (ESLS) is an effect way to remind the driver to slow down the vehicle. The ESLS visibility is significant to guarantee the driving safety. This research focuses on the color variable ESLS system, which is placed at the same location with the traditional speed limit sign. With this system, the driver could receive the updated speed limit recommendation in advance and without distraction produced by eyes contract change over the dashboard and the front sight. First, the mathematical model of the drivetrain and the engine brake is built for typical motor vehicles. The vehicle braking characteristics with various initial speeds in the deceleration area are studied.

With the continuous increasing requirements of commercial vehicle weight and speed on highway transportation, conventional friction brake is difficult to meet the braking performance. To ensure the driving safety of the vehicle in the hilly region, the eddy current retarder (ECR) has been widely used due to its fast response, lower prices and convenient installation. ECR brakes the vehicle through the electromagnetic force generated by the current, and converted vehicle mechanical energy into heat through magnetic field. Air cooling structure is often used in the traditional ECR and cooling performance is limited, which causes low braking torque, thermal recession, and low reliability and so on. The water jacket has been equipped outside the eddy current region in this study, and the electric ECR is cooled through the water circulating in the circuit, which prolongs its working time.

In this paper, we propose a method of dynamics simulation and analysis based on superelement modeling to increase the efficiency of dynamics simulation for vehicle body structure. Using this method, a certain multi-purpose vehicle (MPV) body structure was divided into several subsystems, and the modal parameters and frequency response functions of which were obtained through superelement condensation, residual structure solution, and superelement data restoration. The study shows that compared to the traditional modeling method, the computational time for vehicle body modal analysis can be reduced by 6.9% without reducing accuracy; for the purpose of structural optimization, the computational time can be reduced by 87.7% for frequency response analyses of optimizations; consistency between simulation and testing can be achieved on peak frequency points and general trends for the vibration frequency responses of interior front row floors under accelerating conditions.

Energy has the worldwide concern since the World War. Recently, the energy harvesting technology has got more attraction in different fields and applications. Hence, in a world where energy becomes rare and expensive, even the small quantities are worth to be harvested where it can be exploited in different applications. Vehicle suspension is one of the vibration power dissipation sources in which the undesired vibration is dissipated into heat waste. Accordingly, the principal motivation of this study is exploitation the conflict between the potentially harvested power and vehicle dynamics in automotive suspension system induced by road irregularity. Therefore, in terms of RMS conflict diagrams, the conflict between the potential power and vehicle dynamics are sufficiently and comprehensively defined considering a vehicle speed of 20 m/s.

The basic principle of wavelet transform is presented and the method of wavelet theory is used in vibration signal analysis of vehicle in this paper. The vibration signals which generated in the locations such as cab floor, engine, transmission, band spring and frame under the usual work condition are measured by the vibration test system. The vibration signals are decomposed with the principle of wavelet decomposition at level six, and eigenvectors of signal energy are gained. According to the correlation coefficient of eigenvectors of signal energy distribution, two signals correlativity is determined. It could be an effective method that identificate the main vibration source.

With the development of the Internet for vehicles, the Car-sharing has been developed rapidly in recent years. This paper focuses on the network programming and distribution for Car-sharing, which helps to clarify the characteristics and basic law of Car-sharing network development, as well as the main approaches to construct it. Firstly, by analyzing the effect factors and expanding ways of Car-sharing network, characteristics of the development of Car-sharing industry and its network, as well as main Car-sharing users and services, the influence factors of Car-sharing demand and the main demand points in a city are summarized. Secondly, in order to better evaluate the network programming and distribution for Car-sharing, this paper proposes an optimization decision method of the car-sharing network planning by evaluating the possible alternatives in a same scale. The assessment index of Car-sharing network planning is constructed.

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.

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.

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.

As an auxiliary braking device of heavy-duty vehicle, eddy current retarder can reduce the brake failure due to the high temperature of the main brake. Nevertheless, the eddy current retarder will generate high temperature locally during the working process of it, leading to the decline of the brake power. The study on the heating characteristics of eddy current retarder is advantageous to the layout and parameter design of the liquid cooling channel of the retarder body and prolong the effective time of the auxiliary brake. In this research, a new kind of integrated eddy current retarder has been established. The thermal-magnetic coupling characteristics are studied and the laws of variation in torque output of auxiliary brake affected by the body temperature of retarder are analyzed. The boundary conditions are provided for the construction of the cooling channel. Firstly, the distribution of magnetic field and the characteristics of eddy current are simulated.

For the thin ice on the road in winter, the traditional road deicing vehicle relies on mechanical and chemical methods for melting ice, which is inclined to damage the pavement and has insidious influence on environment. The thermal deicing vehicle has been adopted in recent years. Although the deicing method is available, the deicing efficiency is unacceptable while the energy consumption is huge. The study adopts the new idea of “bottom-to-top” for melting the intersection area between the road surface and the bottom ice layer by the microwave heating firstly and then cleaning them out using high pres. vapor cutting so as to save the cost of energy and enhance the traffic safety. First of all, the mathematical model of the melting process of the intersection of the pavement and the ice layer was established according to the microwave heating characteristics.

Eddy current retarder (ECR) shares a large market of auxiliary brakes in China, but shortcomings of the short continuous braking time and the high additional energy consumption are also obvious. The propose of combined braking partakes the braking torque of ECR. However, the existed serial-parallel braking strategy could hardly balance well the relationship between the braking stability and the energy recovery efficiency. This research puts forward an energy management strategy of combined braking system which aims to maximize energy recovery while ensure the brake stability. The motor speed, the braking request and the state of charge (SoC) of the storage module are analyzed synthetically to calculate the reasonable braking torque distribution proportion. And the recovered energy is priority for using in the braking unit to reduce the additional energy consumption in this strategy.

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.

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.

For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

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.