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The drivability plays an important role for marketability and competitiveness of passenger car in meeting some customer requirements, which directly affects the driving experience and the desire of purchasing. In this paper, a framework of objective drivability evaluation with multi-source information fusion for passenger car is proposed. At first, according to vehicle powertrain system and optimization theory, certain vehicle performances, which are closely related to objective drivability are analyzed, including vehicle longitudinal acceleration, vehicle speed, engine torque, engine speed, gear position, accelerator pedal, brake signal and voltage signal. Then, combined with the evaluation criterion of signal-to-noise ratio (SNR), mean error (ME), root mean squared error (RMSE) and signal smoothness (SS), a de-noising method is developed for the drivability evaluation information.

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.

Hydraulic retarder is one of main auxiliary braking devices of the vehicle. When the vehicle is braking, a great pressure from high-speed fluid is received by hydraulic retarder blades. It is difficult to predict rational hydraulic retarder strength, owing to the complexity of the internal flow of oil. An optimal calculation way of hydraulic retarder strength is proposed based on CFD and FEA, concluding a reasonable result. The 3-D model of hydraulic retarder is built in the general CAD software. The model of fluid passage is extracted, according to the condition when the whole flow passage is filled with oil, and imported to CFD software. The inner flow field of hydraulic retarder is analyzed and the hydraulic surface pressure distribution of the hydraulic retarder blade is obtained at the highest rotary speed of turbine wheel.

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.

Hydraulic retarder braking torque is needed to be controlled. Retarder braking torque refers to oil volume in retarder chamber, thus the key of braking torque control is oil charging & discharging control. The hydraulic oil charging & discharging system should have fast accurate response and provide enough flow rate. To achieve the requirement, the system is often complicated, many parameters need 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 on hydraulic retarder oil charging & discharging control system. The complete system is divided into some parts and every part is analyzed respectively based on mechanical theory and hydraulic theory. To build the precise model of all parts, the AMESim software is used.

Speed ratio and clamping force are two of the metal-belt CVT control targets. Conventional control strategies can not correspond to the driver's intention or provide various driving environment. A fuzzy logic ratio control algorithm and a fuzzy logic clamping force control algorithm for a metal-belt CVT are proposed. Nevertheless, high-quality fuzzy control rule base and factors of FLC are difficult to gain because repeated tests and experts' experience are needed. Therefore, genetic algorithm (GA) is introduced to optimize the fuzzy control algorithms. Using the optimized fuzzy control algorithms, Metal-belt CVT control simulations were implemented. The results show that a faster response and better robustness can be gained when compared with those of the PID control.

This paper shows a model based design method for PHEV hydraulic module. Firstly, system scheme is designed due to system function requirements and is verified preliminarily by function level modeling & simulation. Secondly, detailed models of hydraulic parts (hydraulic valves, actuators, etc…) are set up. Some part structure parameters are designed according to detailed model simulation. Thirdly, detailed hydraulic model is set up by parts model integration, the system design is further verified by completed model simulation. After prototype production, simulation result is compared with prototype test result and two results have good correlation. So this model based design method for PHEV hydraulic module is practical and has good effect on development period & cost reduction.

Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles.