State Of Chaege Estimation Of Lithium Battery Based On Adaptive Kalman Filter

In recent years,with the increasing improvement of people’s living standards,the sales of fuel cars have increased rapidly,which not only brings convenience to people,but also aggravates the emission of greenhouse gases and causes the shortage of fossil energy.In this context,each vehicle enterprise vigorously promotes the research and development of electric vehicles,and electric vehicles will become the mainstream means of transportation in the next few decades.Battery Management System(BMS)is the core part of electric vehicle power System,which is very important for the safe,stable and efficient operation of the vehicle.Estimation of State of Charge(SOC)is one of the key technologies of BMS for battery energy management and control.Accurate estimation of lithium battery SOC has important research value for improving the range and safety performance of electric vehicles.This paper takes a single ternary lithium battery as the research object and estimates the SOC of lithium battery.The main research contents of this paper are as follows:Firstly,the outline structure,working principle and several important parameters of lithium battery are introduced,and the design scheme of lithium battery test system is briefly described.Based on the test system,an experimental platform was built to test the available capacity,temperature characteristics and discharge rate characteristics of ternary lithium battery,and the test results were analyzed.Then,the second-order RC equivalent circuit model was selected as the equivalent model of lithium battery.Pulse discharge experiments at different temperatures were designed to identify the model parameters,and the OCV-SOC curve was fitted.The simulation model of lithium battery was built in the simulation environment.Then,considering that the SOC estimation of lithium battery will be affected by noise,kalman filter algorithm is adopted to estimate the SOC of lithium battery to reduce the influence of noise on the estimated value,and different noises exist in different discharge conditions of lithium battery.Adaptive Extened Kalman Filter(AEKF)was used to estimate the SOC of lithium battery.In the simulation environment,through four different discharge conditions verification algorithm.The results show that AEKF algorithm has high estimation accuracy.Finally,the hardware circuit of the lithium battery test system is designed.STM32-F405RGT6 is used as the main control chip,and the battery is discharged by the electronic load module.The four-switch Buck-Boost circuit is used to charge the battery.The SOC estimation experimental platform was built by the designed hardware,and the algorithm was written into the single chip microcomputer to run.The estimation performance of AEKF algorithm in the actual application environment was verified by USING DST discharge conditions.The experimental results verified that AEKF algorithm has high estimation accuracy and stability,and the maximum estimation error is less than 4%,which meets the actual requirements.