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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.

Vehicle exhaust waste-heat recovery with thermoelectric power generators can improve energy efficiency, as well as vehicle fuel economy. In the conventional structure, the hot-end of thermoelectric module is directly connected with the outer wall of the exhaust pipe, while the cold-end is connected with the water pipe’s outer wall of the vehicle engine cooling cycle. However, the variety of vehicle engine operating conditions leads to the instability of the hot-end temperature, which will reduce the generating efficiency of the thermoelectric modules and also shorten its service life. This research is on the basis of constructing a heat transfer oil circulation, and to study the action principles and implementation methods of it.