| Under the guiding ideology of building a resource-saving and environment-friendlysociety, our government vigorously promotes the development of new energy vehicles. Themost influential promotion activity is The Demonstration Project of Thousands ofEnergy-saving and New Energy Vehicle in Dozens of Cities· launched in2009, in which trialoperation of energy-saving and new energy vehicles is carried out in25cities in three stages,and that play a very good leading role for the promotion of new energy vehicles.As one of the first pilot cities, Changchun takes the lead in the promotion of new energyvehicles. The fuel of the hybrid electric bus (HEB) that trial operating in Changchun iscompressed natural gas (CNG), which is a clean and cheap energy. On the basis of energysaving by hybrid electric bus, CNG hybrid electric bus can further reduce operating costsand emissions.This thesis was finished in combination with the project Technical Support and EnergySaving Technology Development for Hybrid Electric Bus of FAW·. Aiming at the problem ofhigh gas consumption that CNG hybrid electric bus running in Changchun has, a series ofstudies are carried out. On the basis of maintaining the structure of the power system andlogic threshold control strategy using in the HEB, reduce gas consumption by driving cyclerecognition and control parameters optimization.The main contents and achievements of this thesis are as follows:(1) Collect and process the actual driving cycle data. Collect the actual driving cycledata of the city bus in Changchun, and select10characteristic parameters that the mostcommonly used to reflect the characteristics of driving cycle; Divide the long driving cyclesthat collected into shorter ones, whose running time is about600s, and get50short drivingcycles; Then calculate the characteristic parameters of the50short driving cycles.(2) Do the research of driving cycle classification and recognition. First, principal component analysis is performed on the10characteristic parameters of50short drivingcycles using SPSS, and the result is using3independent principal components to representthe10interrelated characteristic parameters; Then the K-means cluster analysis is madeaccording to the scores of the3principal components, and by comparing3classes and4classes, the results show that dividing into3classes is more appropriate, and then threerepresentative driving cycles are selected; By Box-plot analysis, select5characteristicparameters that have greater impact on the classification, as inputs of driving cyclerecognizer; And then a fuzzy recognizer of driving cycles is built, whose membershipfunctions are determined according to the Box-plot; Finally, the recognition accuracy of thefuzzy recognizer is verified, and the result shows that the recognition accuracy is above95%,so the fuzzy recognizer can be used to recognize driving cycles.(3) Build the simulation platform and optimize control parameters. According to thevehicle parameters and the used threshold control strategy, Cruise vehicle model andSimulink control strategy model are built; Then, under different representative driving cycles,optimize the relevant control parameters using genetic algorithms, obtaining theoptimized-parameters library, and then do comparing simulation with original controlparameters, the result proves that the control strategy using optimal control parameters haslower gas consumption.(4) Finally, according to the researches of driving cycle recognition and controlparameters optimization, build the optimized control strategy based on driving cyclerecognition, and then do comparing simulation with original threshold control strategy. Theresults show that, under the optimized control strategy based on driving cycle recognition,the working points of the engine and the motor are more reasonable, and the efficiencies ofthe engine and the motor are significantly improved, so that the gas consumption is greatlyreduced, and the fuel economy is effectively improved. |
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