Research On Firefly Optimization Algorithm For The SOC And SOH Co-estimation Of Lithium-ion Batteries

With the increasingly prominent global energy and environmental ecological problems,the automobile industry is being driven to continuously reform towards new energy.The performance level of power battery directly affects the performance of the whole pure electric vehicle.State of Charge(SOC)and State of Health(SOH),as two key state quantities that must be accurately evaluated by the Battery Management System(BMS),are necessary prerequisites for effective realization of functions such as balance control and fault diagnosis.Therefore,this paper takes ternary lithium ion battery as the research object,and carries out the research on collaborative estimation of lithium battery SOC and SOH based on firefly optimization algorithm.The main work of this research is as follows:(1)Aiming at the uncertainty factor of aging of power lithium-ion battery,in this study,a novel dynamic migration modeling method is proposed to accurately characterize the dynamic characteristics of the battery.After establishing the initial Thevenin model,the functional relationship between each parameter and the battery SOC is obtained by off-line parameter identification.The migration factor is added to the function relationship to adjust each parameter.The migration factor forms the migration matrix,and the particle filter algorithm is used to continuously correct and update the migration matrix to achieve dynamic adjustment of battery parameters.(2)In this study,a “particle filtering-Kalman filtering” two-layer filter co-estimation system is constructed to achieve the progressive inter cyclic correction of SOC and SOH.The first layer particle filter realizes SOC estimation,and takes the estimated value as the input of the second layer filter to realize the progressive estimation of SOH.Then,take the estimated value of SOH at the current time obtained from the second layer Kalman filter as the input of the next iteration cycle,further correct the estimated value of SOC at the next time,and form a closed loop until the end of the iteration.(3)In this study,a synergetic prediction method for SOC and SOH of power lithium-ion battery based on firefly optimization algorithm is proposed.SOC particles simulate the behavior of firefly individuals in the nature to gradually approach the brightest individual through mutual attraction of fluorescence brightness,so as to achieve the goal that the particles approach the optimal value,that is,the state particles further approach the real value.On this basis,the chaotic mapping algorithm is added,and the variables are linearly mapped into chaotic variables through chaotic mapping,and then the search process is optimized according to the ergodicity and randomness of chaos.So as to achieve high-precision collaborative estimation of SOC and SOH.(4)Based on different battery aging conditions,in this study,an experimental verification system for SOC and SOH co-estimation of power lithium-ion batteries is designed.The coestimation is experimentally verified under three complex test conditions,and the model and algorithm were respectively verified under the conditions of battery health,mild battery aging and severe battery aging,and compared with the estimation results under the traditional algorithm,a detailed analysis of the estimation results is carried out to verify the effectiveness of the proposed model and method in this study.The experimental verification results under the battery health state and the battery aging state show that the proposed dynamic battery migration model and chaotic firefly particle filter method can effectively improve the accuracy and estimation stability of the SOC and SOH collaborative estimation of power lithium ion batteries,providing a theoretical basis for the effective management and safe application of power lithium ion batteries.