Research On Energy Management Strategy Based On Charging Management For Range Extended Electric Vehicle

A range-extended electric vehicle (REEV) is an electric vehicle that includes an auxiliary power unit known as a ’range extender’ whose function is to increase the vehicle’s driving range. The amount of electric energy acquired from the grid is determined by the state of charge (SOC) of the primary battery of the REEV at a charging location. Thus, It is one of the key issues in the energy management strategy of the extended electric vehicle when the electric power is reached at a reasonable level and the electric energy is guaranteed to reach the charging facilities in different traffic conditions.. To offer a solution to this problem, In this paper, an energy management strategy based on charge management is proposed to reduce the fuel consumption of the extended range electric vehicle and reduce the discharge of pollutants. The electric energy obtained from the external power grid should be used as far as possible. To deepen the research on the electric vehicle control theory and improve the performance of the REEV, it is of great academic significance and engineering value to carry out the research on the energy management strategy of the REEV based on the charge management according to the REEV’s operation characteristics.In order to obtain the electric power of the external network when the electric vehicle arrives at the charging station, this paper establishes the travel time estimation model of electric vehicle based on charging. Based on the analysis of the charging method and the management system of the charging facilities, this paper divides the electric vehicle driving road environment into the driving condition which is influenced by the traffic signal and the surrounding vehicles, and the driving condition which is affected by the traffic signal control. When the electric vehicle is under the influence of both traffic signals and surrounding vehicles, the travel time of each section is obtained through Intelligent Transportation System (ITS), and then the time required for the entire electric vehicle to reach the charging facility is calculated by adding.The algorithm obtains the traffic signal information through the wireless communication system, and calculates the running speed curve so that the automobile can take the initiative to avoid the rapid acceleration and deceleration operation caused by the traffic light, the idle operation and improve the economy of the vehicle.This paper establishes the electric vehicle operation state recognition model.Through the analysis of the characteristics of electric vehicle operating status recognition, the sample data set is constructed by rolling time window and min-max normalization method. The genetic algorithm is used to optimize the characteristic parameters. The hybrid kernel function and support vector machine (SVM) are used to solve the problem of small sample size and high dimension of the feature parameter set.In addition, in order to solve the problem of long time training of floating search algorithm and SVM, this paper studies the calculation structure of off-line training, and proposes a method of selecting the running state characteristic parameters based on parallel computing, which solves the calculate time of running state recognition model is too long.An energy management strategy for REEV is proposed, which aims to reduce the battery SOC to the optimal area when REEV arrives at the charging facility, and determines the opening, closing time and the output power according to the current running state of the vehicle. When the vehicle runs on trunk roads, the output of the auxiliary power unit remains constant, while on expressway it operates on power tracking mode.In order to verify the proposed energy management strategy and the effectiveness of the operational status identification module, the operation status identification model based on the actual vehicle speed and the SOC model of the battery are validated. The results show that the proposed energy management strategy can accurately identify the current operating conditions of extended range electric vehicles and can effectively estimate the battery SOC.