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Blade Electric Vehicle (BEV) with a light body plays an important role in saving the energy and reducing the exhaust emission. However, reducing the body weight need to meet the heterogeneous attributes such as structural, safety and NVH (Noise, Vibration and Harshness) performance. With the rapid development of finite element (FE) analysis technology, simulation analysis is widely used for researching the complex engineering design problem. Multidisciplinary Design Optimization (MDO) of a BEV body is a challenging but meaningful task in the automotive lightweight. In present research, the MDO is introduced to optimize a BEV Body-in-White (BIW).

In the Rankine cycle, the pressure differential generated by the phase change of the working fluid produces turbine output power, which enables the recovery of waste heat from the internal combustion engine. The heat transfer ability of the evaporator is the key factor that determines the quality of turbine's mechanical work. In this paper, the performance of the evaporator with two-phase zone and preheated zone is studied. After obtaining the thermal characteristics of diesel engine exhaust from the experimental data, the mathematical model of the evaporator is built according to the specific working conditions of ORC and geometrical parameters of the evaporator. Three typical engine operating conditions are used to estimate the heat transfer characteristics of the evaporator. The result shows that, in the evaporator, the heat transfer coefficient of the Rankine working fluid is much greater than the exhaust side of the engine.

This work analyzes the transmission efficiency of vehicle driveline including the gearbox, universal transmission and differential. Based on the structure of transmission, mathematic models are built to analyze transmission's characteristics. However, an experiment reveals the limitation of this method. Then, the paper statistically analyzes the experimental data and mainly analyzes the influencing factors. Then Neural Network is used to build the efficiency model. A method called “filling data and gradually extrapolating” is used when building neural network model. Finally, the neural network model is used in the simulation of fuel consumption. The conclusion is Neural Network model can imitate the transmission efficiency of vehicle driveline efficiently, but its internal structure is not clear so other modeling methods are needed to be found.

In order to obtain the comprehensive evaluation of thermal management system for the retarder, the complete driveline thermal management model is built. The characteristic parameters for the thermal management system are determined and the hydromechanical characteristics for the retarder are fixed by the rig test. On the basis of the same whole vehicle driving cycle, comparing to the traditional mechanical-drive system, the independent-drive system makes the working temperature of the heat source more stable. Meanwhile the parasitic power caused by the radiator fan is decreased markedly on the condition that the heat reject requirement of the heat source is satisfied.

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.

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.

In heavy truck driveline system, the components often include clutch, transmission, transfer case, drive shaft, etc. A fluid torque converter could be equipped in front of the transmission in order to improve the starting performance. Meanwhile, a hydraulic retarder could be introduced for auxiliary braking so as to adapt the truck to the brake on long downgrade in mountainous regions. Thus, the driveline heat load would have a notable increase. Both the fluid torque converter and the hydraulic retarder would produce a large quantity of heat, and a special cooling system is needed for adjusting the transmission fluid temperature with which the gains are potentially very large [1]. The heat load for driveline is often calculated based on empirical formula. For the heavy truck, however, if the heat value is underestimated, driveline components would suffer from overheated damage.

Hydraulic retarder has been widely applied on military vehicles and heavy commercial vehicles because of it could provide great brake torque and has lasting working time [1]. In order to reduce driver's frequent actions in braking process and prevent hydraulic retarder system from overheating, it is need to apply constant braking torque control, this control target has a strict requirement to hydraulic control system design. Many parameters often require repeated test to determine, which increases the R&D cost and extends the research cycle. This paper tries to find a time-efficient research method of hydraulic retarder control system through studying on a heavy military vehicle hydraulic retarder system. Hydraulic retarder model is set up through test data. The hydraulic control system is built based on AMESim. Controller model is set up based on PID control. The whole vehicle brake model is built based on MATLAB/Simulink.

Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact.