Parameter Matching And Control Strategy Research For Range-Extended Electric Vehicle

As energy shortage,environment pollution and traffic jam,the traditional automotive industry is undergoing a revolution:the vehicle is developed toward the direction of electric-drive,intelligence,interconnection in order to support people with more green and convenient travel service.However,the limited mileage is still an insurmountable barrier for pure electric vehicle under the current technical conditions.This makes the hybrid vehicle become the mainstream product of new energy vehicle at this stage,and the range-extended electric vehicle(R-EEV)as one of the most close to the pure electric vehicle has gained the attention of the major automobile manufacturers.Starting with parameters matching,a set of part-time hybrid energy management strategy based on energy prediction was designed for long distance travel of extended range electric vehicle.The main work of this paper is:1.Taking REEV’s dynamic performance as the goal to discuss the coupling relationship between the acceleration,climbing ability,the maximum speed and shift number and parameters of the drive motor,then parameters matching of the drive motor and the transmission ratio were completed according to these theories.With the consideration of demand of power and energy and the number of battery packs on the quality of the whole vehicle,parameters of power battery were matched.And the parameters of APU were matched according to the mileage indicator.2.First of all,the energy management strategy of R-EEV fell into two categories:full-time hybrid and part-time hybrid.The types and working mode of part-time hybrid strategy were emphatically introduced.Secondly,considering R-EEV the demand of long distance running,a set of part-time hybrid energy management strategy based on energy prediction was designed aimed at making the vehicle make full use of battery energy,limit the usage of APU and ensuring APU work in the minimum fuel consumption zone.Again,The MATLAB/simulink vehicle model which mainly included control strategy model,battery model,power balance model was built according to the design principle.Finally,the dynamic performance,economy and the control strategy was simulated and validated effectively.3.For the problem of different start-stop strategies affecting fuel consumption,an optimization model which includes four inputs(range S,load P,SOCAPU_start,SOCAPU_off)and single output(fuel consumption)was formulated to achieve the target of minimum fuel consumption based APU multi point working model for meeting different demand of power and energy.In the constraints of performance index of Range-Extended Electric Vehicle and battery cycle life,optimization of control parameters of APU start-stop was performed by utilizing the PSO(Particle Swarm Optimization)algorithm with diverse distances under different driving cycle to realize APU start-stop control for adaptive range.Compared with before optimization,the results show that fuel consumption is reduced by 15.87%\2.09%,42.24%\14.54%corresponding to NEDC100\200,CUDC100\200.Based on energy consumption comparisons before and after optimization,the fuel economy can be obtained through reducing APU operation time in the condition of single load and transferring operation time from peak load to medium load and low load in the condition of multiple loads.4.Based on static navigation data,energy prediction algorithm based on moving average and emergency were designed for energy management strategy based part-time hybrid;then the two prediction algorithms were tested and analyzed respectively;finally,the driver driving cycle which contained gradual change and mutation were simulated as input for prediction model,then the accuracy of prediction output were analyzed,and energy distribution of two predictive algorithms were compared based part-time hybrid energy management strategy designed by Chapter 3 under different prediction accuracy.