Research On Real Time Optimal Control Strategy Of High Power Lithium Battery

In recent decades,due to increasingly serious environmental pollution problems and energy problems,making the global research on the field of new energy has become more active.As a zero pollution of the green car,electric vehicles have been vigorously developed.Since the lithium battery has been developed,because of its unique advantages,lithium battery has become the main research direction of the new energy field.Nowadays,lithium batteries are widely used in electric vehicles.At present,in order to ensure the safe driving of electric vehicles and to meet the normal needs of electric vehicles,battery management system has made a lot of research,such as the battery balance control,safety management,thermal management and energy management.In recent decades,as environmental pollution has become more and more serious and energy crisis,the global research on new energy field has become more active.As a non-pollution of green environmental protection vehicles electric vehicles have been vigorously developed.The battery management system,which is one of the components of EV,is used to monitor and manage the battery pack safely and to provide protection for the safe and normal running of electric vehicles.IN order to improve the efficiency of battery pack and satisfy the demand of EV,this thesis studies the online optimization control strategy of the lithium battery from the perspective of the practical application of BMS.The main contents are as follows:(1)Combined with the characteristics of power lithium-ion battery,summed up the advantages and disadvantages of different models of lithium batteries and finally chose the Thevenin equivalent circuit model as the basis of this thesis.Starting from the model of lithium battery pack,the relevant parameters of the battery model are identified.The shortcomings of using the off-line parameter identification are analyzed.In order to obtain more accurate model parameters,the recursive least squares method is used for the on-line identification,and then the accuracy of the model is verified.(2)The shortcomings of using single battery parameters as the basis of peak current prediction are analyzed,and the multi-parameter fusion of single cell parameters and battery parameters is proposed as the basis for estimating the peak current of the battery pack.The peak current forecasting system of the multi-parameter fusion battery is established and the decision function of the peak current is deduced.(3)The fuzzy control theory is used to analyze the decision function and the corresponding fuzzy control rules are designed.The validity of the multi-parameter fuzzy control strategy is verified by simulating the DST condition.Then the genetic algorithm is used to analyze the membership function and the fuzzy control strategy is optimized.The simulation results show that the genetic fuzzy control strategy is effective.(4)Based on the research of this paper,combined with the BMS,the software platform of PC is designed to analyze the relevant parameters of the battery Under the same experimental conditions,the optimal control strategy is compared with the cyclic simulated NEDC working condition.Finally,through the analysis of the experimental results,it is proved that the genetic fuzzy control strategy with multi-parameter fusion can effectively improve the efficiency of the battery pack under the condition of guaranteeing the consistency of the battery pack.The optimal fuzzy control strategy for multi-parameter fusion is the optimal control strategy of high power lithium battery.