A Study Of Extended-Range Electric Vehicle’s Powertrain Parameter Matching And Optimization

As the environment problem become more and more serious, and the powersources more and more insufficient, the necessity of developing an adaptive andsubstitutional type of vehicle becomes more and more important. And theRange-extended Electric Vehicle now could be a ideal candidate here, thisconfiguration could fill the gap between the conventional internal-combustion enginevehicle and the electric vehicle. And this is the compromise we must make under thepresent technology circumstances. What mainly discussed in this paper is the first stepin the “V” type develop schedule: set the powertrain parts parameters in the light ofdynamic performance demand.The main details in this paper is as follows:1、Search and read the available literatures to analyse the present technologylevel, including analyse the alike vehicle’s configuration and powertrain parameters,analyse the control strategy applied in the vehicles. So that we can establish thesimulation model base on this.2、Establish the vehicle’s simulation model, which means modify the originalseries electric vehicle simulation model and change it into the Range-extendedelectric vehicle model, and meanwhile, change the control strategy to fit the model,including modify the engine’s operation point and set them into three certain fixedpoints and the make the range-extender to be available to be employed to drive themotor directly instead of transmit the power via the battery.3、According to the dynamic performance demands, Set the powertrain parameterby applying the formulas. The parameters is including the output power and theoutput torque of the motor, the range extender engine’s max output power, thegenerator’s generating power, and the battery pack energy volume. Then input the setted parameters into the simulation ADVISOR model to verify the correctness.4、Optimize the outcomes of the last step by using the GA algorithm, theconstrained function is minimize the total product cost and minimize the total mass ofpowertrain, then input the outcomes into the simulation model. Verify the correctnessin ADVISOR, too.In the parameter matching process, there are two sets of outcome results, one ofthem is the outcomes determined by the dynamic demanding, the other is theoptimized outcomes, by comparing the two sets of outcomes, we can conclude thatalthough the parameters setted for catering the dynamic demanding could indeed meetthe dynamic demanding, but this parameters make the powertrain have a large size,cost a lot and have a poor fuel economy performance. But after the optimization, thepowertrain parameters has been substantially downsized, consequently reduce theproduction cost and the system mass.