Car Fuel Economy Simulation Forecast Method Based on CVT Efficiencies Measured from Bench Test

Researchers face di culties in studying the e ects of driveline e ciency on car fuel economy via bench and road tests because of long working periods, high costs, and heavy workloads. To simplify the study process and shorten test cycles, a car fuel economy simulation forecast method for combining computer simulation forecasting with bench tests is proposed. Taking a continuously variable transmission(CVT) as the research object, a transmission e ?ciency model based on a bench test is constructed. An optimal economic variogram based on the original CVT vari?ogram, the boundary conditions of vehicle performance, the road conditions and the driving behavior of the driver is generated in the Gear Shift Program(GSP)?Generation module in AVL Cruise. And on this basis a driveline simulation model that can calculate the fuel consumption based on the driveline data of a test car is built. The model is used to forecast fuel consumption and calculate real?time CVT e ciency under di erent conditions. Contrastive analyses on simulation results and real car drum test results are made. The largest error between simulation results and drum test results in driving cycles is 4.099%, which is 5.449% under constant velocity condition in driver control mode and 4.2% under constant velocity condition in automatic cruise mode. The results confirm the feasibility of the method and the good performance of the driveline simulation model in accurately forecasting fuel consumption. The method can e ciently investigate the e ects of driveline e ciency on car fuel economy. Moreover, this research provides instruc?tion for accurately forecasting fuel economy as well as references for studies on the e ects of drivelines on car fuel economy.

Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model

Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods.Quantitative precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst areas.Furthermore,coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas.This study served two main purposes.One purpose was to compare the performance of the Weather Research and Forecasting(WRF)QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System(PERSIANN-CCS)QPEs in rainfall forecasting in karst river basins.The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model.The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results.However,the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges,while the quantities forecasted by the PERSIANN-CCS QPEs were smaller.A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models.The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results.In particular,coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting,and a maximum lead time of 96 h is adequate for flood warnings and emergency responses,which is extremely important in flood simulations and forecasting.