Research On State Of Energy Estimation Of Lithium-ion Battery Pack Considering Cell Inconsistency

With the increasing energy shortage and environmental pollution worldwide,the research and development and promotion of electric vehicles have received more and more attention.Lithium-ion battery is an important energy source for electric vehicles.The accurate estimation of its State of Energy(SOE)is of great significance for vehicle mileage prediction and battery safety management.It is also one of the core difficulties.Limited by the voltage and capacity of the single lithium battery,in actual use,hundreds or thousands of single batteries must be connected in series and parallel to provide power for the electric vehicle.However,due to differences in innate manufacturing processes and differences in acquired environments and driving habits,there is always inconsistency among the cells in the battery pack,making it difficult to estimate the State of Energy of the module,and even cause serious safety problems.Based on this,this paper studies the SOE estimation method of lithium-ion battery series modules considering inconsistency of monomers,mainly from the following aspects:1.The basic composition,working principle and basic characteristics of lithium-ion batteries are introduced,comparatively analyzed the advantages and disadvantages of several common battery models,and from the initial capacity,internal resistance and OCV-SOE curve and other aspects,the inconsistency of single cell parameters was analyzed.It is determined that the first-order RC model is used to describe the dynamic characteristics of the single cell,and the recursive least squares(RLS)and adaptive volume Kalman filter(ACKF)are used to jointly estimate the key parameters of the representative single cell and the SOE.2.A SOE estimation method for series-connected battery packs based on representative cells is studied,so that when the battery pack is close to the full charge state,the estimated SOE value of the battery pack is close to the SOE estimate value of the strongest single cell,and when the battery pack is close to full discharge state,the estimated SOE value of the battery pack is close to the estimated SOE value of the weakest single cell,and the SOE value of the battery pack transitions smoothly in the middle range.The representative cells are selected based on the terminal voltages of the single cells in the battery pack at the initial time,and an adaptive weight update strategy based on the changes in the terminal voltage of the battery is designed.3.A new European Driving Cycle(NEDC)simulation test was conducted on a seriesconnected lithium-ion battery pack containing 6 cells at different temperatures(0 ℃,25 ℃,and 50 ℃)to verify the performance of equivalent circuit model parameter identification and SOE estimation algorithm.The results show that the battery pack SOE estimation method designed in this paper can achieve the expected results.Compared with other methods such as AWS-RLS-EKF,AWS-RLS-UKF,AWS-RLS-CKF,it has better accuracy and robustness.