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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.

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.

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 the field of Electric Vehicle (EV), what the driver is most concerned with is that whether the value of the battery's capacity is less than the failure threshold because of the degradation. And the failure threshold means instability of the battery, which is of great danger for drives and passengers. So the capacity is an important indicator to monitor the state of health (SOH) of the battery. In laboratory environment, standard performance tests can be carried out to collect a number of related data, which are available for regression prediction in practical application, such as the on-board battery pack. Firstly, we make use of the NASA battery data set to form the observed data sequence for regression prediction. And a practical method is proposed to determine the minimum embedding dimension and get the recurrence formula, with which a capacity model is built.

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.

How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements.

This paper presents a method of calculating temperature field of the piston by using a wavelet neural network (WNN) to identify the unknown boundary conditions. Because of the complexity of the heat transfer and limitations of experimental conditions of heat transfer analysis of the piston in a diesel engine, boundary conditions of the piston temperature field were usually obtained empirically, and thus the result itself was uncertain. By employing the capability of resolution analysis from a wavelet neural network, the method obtains improved boundary heat transfer coefficients with a limited number of measured temperatures. Using FEA software iteratively, results show the proposed wavelet neural network analysis method improves the prediction of unknown boundary conditions and temperature distribution consistent with the experimental data with an acceptable error.

With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial.

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.

Driver distraction detection is crucial to driving safety when autonomous vehicles are co-piloted. Recognizing drivers’ behaviors that are highly related with distraction from real-time video stream is widely acknowledged as an effective approach mainly due to its non-intrusiveness. In recently years, deep learning neural networks have been adopted to by-pass the procedure of designing features artificially, which used to be the major downside of computer-vision based approaches. However, the detection accuracy and generalization ability is still not satisfying since most deep learning models extracts only spatial information contained in images. This research develops a driver distraction model based on a two-stream, spatial and temporal, convolutional neural network (CNN).

On road simulation, both the traditional iterative method based on frequency response function (FRF) and adaptive control method based on the CARMA model are realized by using linear model to identify the target test system. However the real test system is very complicated because of various nonlinear factors. Linear models approximately describe the system only in a small range. Therefore, system simulation methods can not be used to validate the developed control algorithm and the uncertainty of test accordingly increases. As mentioned above, this paper presents a model to identify the nonlinear test system using NARMA dynamic neural network and discusses how to make the model parameters in detail. Using the test input-output series data, this network was trained by Levenberg-Marquardt method. Results of verification simulation show the validation of the nonlinear model.

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.

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.

This research is based on the Controller Area Network (CAN) bus, and briefly analyzed its communication protocol with reference to the layered model of Open System Interconnect Reference Model (OSI). Subsequently, a data acquisition system was designed and developed including a Vehicle Communication Interface (VCI) and a laptop. After the overall architecture was built, the communication mechanism of the VCI was studied. Furthermore, the lap top app was built using the layered design followed by the implementation of a scheme for data collection and experimentation involving the test driving of a real car on road. Finally, the driving style was identified by means of fuzzy reasoning and solving ambiguity based on fuzzy theory; via training the acceleration sample and forecast using the excellent learning and generalization ability of Support Vector Machine (SVM) for high-dimensional, finite samples.

In this research, the Mg2Si1-xSnx thermoelectric material is used in the exhaust temperature difference power-generating system, and the material's heat transfer characteristic and power-generating characteristic were analyzed. Firstly, steady heat transfer model from vehicle exhaust to cooling water was established. Then the impact of Sn/Si ratio to the thermoelectric characteristic parameter was analyzed. Finally, considering the influence of varying thermal conductivity to the heat transfer process along the material's heat transfer direction, when the cold end temperature of thermoelectric materials was controlled by cooling water respectively boiling at 343K and 373K, the thermoelectric conversion efficiency and power output of Mg2Si1-xSnx thermoelectric materials with different x value were evaluated based on simulation calculation.

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.

Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study.