Control Parameters Optimization And Predictive Control Strategy Research For Range-Extended Electric Vehicle

Energy and environmental problems are increasingly serious now.The rapid development of new energy automobile industry has been widely expected.However,in the absence of a breakthrough in battery technology,the full implementation of pure electric vehicle is a little overhastiness.This makes hybrid electric vehicle(HEV)a sought-after destination of current stage in various countries.The REEV(Range-Extended Electric Vehicle)is widely regarded as the ideal model for the transition from traditional fuel vehicles to pure electric vehicles.In his paper,the platform based Matlab/Simulink softwareis was used.The research work on the REEV was carried out with the matching of vehicle parameters as the entry point based on the rules-based engine multi-operator control strategy.(1)According to the structural characteristics of REEV,the different working modes were analyzed.The parameters matching of REEV was completed on the basis of the performance indexes and design requirements of REEV.Then the numerical models of the key power components were established to prepare the research of the follow-up control strategy.(2)The REEV multi-operation point control strategy was established based on the engine's characteristic curve and fuel consumption numerical model.Then the genetic algorithm was used to optimize the system control parameters.The REEV can further explore its fuel economy under the framework of regulatory control.The dynamic control strategy of variable parameter was developed by using cluster analysis algorithm and LVQ neural network algorithm to recognize the roadway type,so as to realize dynamic switching control parameters through on-line real-time recognizing the roadway type.(3)In order to solve the randomness problem of the battery SOC at the end of driving distance,the multi-objective range adaptive control strategy was developed,and the control parameters were introduced.By limiting the SOC,the SOC of the battery always fluctuates near SOCref,and was closed to the target SOC value at the end of the range.The energy of the battery and the engine was properly distributed through the equivalent factor K.Further,to study the effect of road slope on fuel economy and emission of vehicle.Observing the degree of influence of the road gradient on the equivalent coefficient,a multi-objective range adaptive control strategy was developed considering the road gradient.(4)Reasonable and sufficient use of external power source is the key to improve the fuel economy and emissions of the vehicle.The external environment information of vehicles was obtained and the intelligent system of"vehicle-road-network" was built based on the vehicle-mounted navigation system,TIS system and smart power grid system.In the view of the charging characteristics of REEV,the charging facilities were taken into consideration on the way.The optimal charging path of REEV was obtained by Dijkstra algorithm,then a segment range self-adaptive control strategy was developed according to the results of predictive path planning.