Modeling Methods Of Time-variant Driving Cycles For Passenger Car Between Origin-Destination

Energy conservation and environmental protection is becoming increasingly importantin the life of automobile. In the aspect of energy conservation, route choice is more and moreimportant to energy consumption. In the same time, driving cycles is a bridge of thetransportation system behavior and fuel consumption of automobile. Transportation systemis a complex nonlinear system consisting of people, vehicles and road which is a stochastic,dynamic and adaptive time-varying system. As for its essence, driving cycles is a microcosmof the traffic network dynamics, and when it is impacted by various external factors (such asthe city road network and traffic signal control), the performance is remarkable in time andspace. Vehicle fuel consumption was influenced obviously by the differences. Hence studyon the time-varying model of the passenger car is of great significance to reduce vehicle fuelconsumption.This thesis relied on National Key Project "973" of China The research of predictableenergy control theory impacted by Traffic environment for EV. Time-varying driving cyclesrefers to different time periods in the same paths and the same periods in different paths.Given the current status of passenger cars in China, we selected passenger car as testvehicle and did enough experiments in Changchun three routes between Origin-Destinationwith VBOX2、Fuel-consumption Meter、DEWETRON and established database. On thebasis of the Markov properties, the paper systematically proposes the method based onMarkov for designing driving cycles and constructs the time-varying model of differentroutes between Origin-Destination. Rationality of Markov method was demonstrated and iscompared with combinatorial optimization method. The main contents are as follows:Firstly, on the basis of Markov theory and element meshing, the two-dimensiondistribution map of velocity and acceleration is divided into grid to define the state. After this,it lays the foundation for obtaining the state transition probability matrix.Secondly, according to statistics and learning rule of the state transition, the paperconcludes the state transition probability matrix and analyses the spectrum of matrices. Thestationary distribution of the state transition probability matrix has been calculated based onthe features of stationary distribution of Markov chain through power method and spectraldecomposition. The relationship of the state transition matrix and the distribution of velocityand acceleration is revealed in the paper.Thirdly, passenger car test in Changchun three routes between Origin-Destination hasbeen conducted. According to Monte Carlo simulation, new sequence of states is obtainedand candidate driving cycles have been obtained. After sifting from the candidates, the representative time-varying model driving cycles are obtained.Finally, the rationality of Markov chain method is verified in two ways. The first is thecomplete induction of state transition matrix; The second is consistency between designeddriving cycles and referenced driving cycles. By contrasting combinatorial optimization andMarkov chain method, the result shows that Markov chain method is concise and efficient.