Battery SOC Estimation Based On GNL Model Adaptive Kalman Filter

With the aggravation of the energy crisis and the increasingly serious urban air pollution,the development of electric vehicles has been highly valued in China,and the performance of power batteries as the core components of electric vehicles has been put forward with higher and higher requirements.At the same time,the number of decommissioned power batteries is increasing exponentially year by year,therefore,it is urgent to realize the large-scale cascade utilization of decommissioned power batteries.Consequently,accurate and reliable real-time online estimation of state of charge(SOC)is the key technology to ensure safe and effective operation of electric vehicles and realize battery cascade utilization.This paper chose the lithium iron phosphate battery as the research object based on the current situation of the development of power battery,internal resistance and voltage of battery three characteristic parameters are analyzed on basis of the principle of battery capacity.Then through contrasting different equivalent circuit models,GNL equivalent circuit model is chosen because of considering the factors of self-discharge of the aging batteries thus reflecting features of batteries more accurately.According to the data of intermittent constant current exile experiment,the relevant parameters of GNL circuit equivalent model are identified,which provide the model grounding for the estimation of battery charge state.This paper introduces Kalman filter and its derivation algorithms in detail,discretizes the corresponding state space equation by using the matrix quadratic method,and proposes an adaptive unscented Kalman filter algorithm combining the the idea of unscented Kalman filter algorithm with double extended Kalman filter algorithm to estimate the state of charge iteratively in real time.Based on GNL circuit equivalent model,unscented Kalman filter algorithm is used to estimate SOC,and extended Kalman filter algorithm is used to estimate model parameters,so as to realize adaptive estimation of SOC.Under the conditions of intermittent constant current exile experiment and variable current exile experiment,the adaptive unscented Kalman filter algorithm and the GNL circuit equivalent model are compared and verified with the aging batteries as the experimental object.The results show that GNL equivalent circuit model can accurately simulate the working process of the battery compared with the second-order RC equivalent model,so as to meet the actual simulation requirements accurately.C ompared with the double-extended kalman algorithm,the adaptive unscented Kalman filter algorithm is less affected by the initial value error,saving computation time and owing stronger anti-fluctuation ability,so it can meet the actual demand of real-time online estimation of SOC.Based on the ECVTS test system which is self-developed hardware and the corresponding BMS function,Visual C#programming language is used to compile the adaptive unscented Kalman filter algorithm to estimate SOC into the online monitoring interface to realize the online monitoring of power battery SOC.