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Considering the defect of current test method and learning from the developing experience of other methods around the world, a method adopted China Light-duty Vehicle Test Cycle (CLTC) which is suitable for the real condition of Chinese road was put forword. Through comparing the test results of 20 vehicles with big data statistics, the results obtained by this method are close to those from customers and the difference between them is around 6%. Thus, the rationality of this method is proved. Furthermore, this method can evaluate the real fuel consumption of vehicle running on Chinese roads.

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.

This paper focuses on the dynamic parameters matching and powertrain ratio optimization of an ERCB (Extended Range City Bus) for improving fuel economy. Firstly, according to the bus data and design targets of an ERCB, dynamic parameters are matched. Simulation models of each component that makes up the whole powertrain are established depending on the platform of AVL-CRUISE, including battery, motor, main reducer, wheels, etc. Dynamic performance, such as full load acceleration, climbing performance, maximum speed performance and endurance mileage, is simulated successively using AVL-CRUISE. Also the fuel economy performance under Chinese Urban Driving Cycle (CUDC) is worked out. The simulation results of each performance are analyzed. To improve the fuel economy, the transmission ratio and final ratio are optimized with Isight software.

Dynamic and economic performance play an important role in the vehicle evaluation indexes, and also are crucial parts considered during the design progress. This paper applies a method of parameters matching of powertrain to one Pure Electric City Bus(PECB), with which a four-speed transmission dynamic system is designed. Meanwhile two powertrain models adopting motors with identical peak power but different base speeds and peak torques coupled with two-speed transmission or without transmission are introduced for comparison to analyze if a smaller motor performs better in performance. The three models are based on identical type of bus. Then according to the powertrain parameters, simulation models of three bus models are established respectively. From the results of simulation, vehicle performances of the four-speed transmission powertrain, especially economic performance, are compared with the other two.

In the Plug-in hybrid electric bus, the power train parameter and control strategy significantly affect the economy and dynamic performance. Thus, the simultaneous optimization of power train parameter and control strategy is designed for the trade-off between the dynamic and economic performance. Depending on the parallel electric auxiliary control strategy in a plug-in hybrid electric bus, a vehicle dynamic simulation model is built with the software AVL Cruise. Aiming at the minimization of equivalent gas consumption and acceleration time from 0 to 50kmph, the gear ratio, final drive ratio, gear shifting strategy and control strategy are chosen as optimal variable, which significantly impact power performance and fuel economy. The driving performance and the driving range with full battery are considered as constraints. Based on the software Isight, multi-objective optimization model is built by adopting non-dominated sorting genetic optimization algorithm (NSGA-II).