Research On Control Strategy For The Plug-in Hybrid Electric Vehicle Based On Driving Cycle Identification

The rapid development of the automobile industry and the increasingly stringent emission regulations gradually subvert the concept of traditional fuel vehicles,under the environment of energy saving and environmental protection and sustainable development,new energy vehicles are gradually entering into people's lives,but due to the many defects of battery technology,plug-in hybrid electric vehicles have become the research focus of many new energy vehicles in the current and even future for its advantages of low emissions and long driving range.This paper aims at the plug-in hybrid electric vehicle,the main contents of the research include the following aspects.First of all,the driving condition data were collected on the urban road and divided into short trips.12 characteristic parameters were selected for the first time and the characteristic parameter values of all short trips were calculated.The principal component analysis is carried out on these characteristic parameters based on SPSS software,and the first 4 principal components were taken to represent the selected 12 characteristic parameters.On this basis,the short trips are divided into four categories by using the K-means clustering analysis method to represent four urban driving conditions.The representative driving conditions of each category were selected based on the "second clustering" method to construct the representative driving condition.Secondly,based on MATLAB,the learning vector quantization(LVQ)neural network was used to train the samples and to establish the driving condition recognition module,and the module was used to identify the representative driving conditions.The result shows that the module can identify driving condition accurately.Then the working mode of the plug-in hybrid electric vehicle was analyzed,and the vehicle control strategy was established based on the driving condition identification.The vehicle simulation model was built in the AVL_Cruise software based on a real vehicle,and a joint simulation was carried out with the control strategy established in the Matlab/Simulink software.The simulation results show that the established control strategy can effectively identify the driving condition and be able to switch the corresponding working mode and distribute torque reasonably.Finally,the optimization model was established in the Isight software,and some threshold values in the control strategy were optimized.The fuel consumption of the optimized vehicle was reduced by 3.5% and the fuel economy has been improved.