Research On SOC Estimation Of Lithium Ion Battery Based On RTS-IEKPF Algorithm

In recent years,electric vehicles have developed rapidly due to environmental crisis.Power lithium-ion battery packs are an important part of electric vehicles,to ensure efficient and safe operation of battery packs,it is necessary to establish a battery management system(BMS)for battery management.The group is effectively managed.The state of charge(SOC)is a typical representation of the operating state of the battery.The accuracy of the estimation algorithm plays a decisive role in improving the performance of the BMS system.In this paper,we will focus on the following aspects of the estimation accuracy of lithium-ion battery SOC.Firstly,the current situation and development trend of lithium-ion batteries for electric vehicles is analyzed.The structure,working principle and related battery technical parameters of lithium-ion batteries are studied.Considering the residual capacity of batteries is affected by factors such as discharge rate,temperature,number of cycles and self-discharge.Using SOC calculation formula with coefficient correction to correct the battery capacity.The common model of the battery is compared and analyzed.The battery mixed noise model based on Thevenin equivalent model was used to reduce the adverse effect of drift current on battery SOC estimation.The battery was subjected to pulse charge and discharge test to realize parameter identification in battery model.The battery equivalent model was verified under constant current discharge and HPPC cycle test conditions.The simulation results were well fitted with the experimental data,which proved the accuracy of the model and identification results.Secondly,a comparative analysis of common SOC estimation methods for lithium-ion batteries is carried out.Considering the transient large-current charge and discharge,the nonlinearity of the battery during operation is increased.Using Iterated Extended Kalman Filter(IEKF)to estimate the battery SOC will create a large linearization error.In order to reduce the error and improve the estimation accuracy of SOC,based on the battery mixed noise model,this paper proposes the use of Rauch-Tung-Streibel(RTS)optimal smoothing algorithm combined with IEKF algorithm to generate the proposed distribution of particle filtering,and an iterative extended Kalman particle filtering algorithm combining RTS optimal smoothing is obtained,the SOC of the lithium battery is estimated by this method.Finally,by constructing the SOC estimation platform,the experimental results show that compared with the IEKF and IEKPF algorithms,the RTS-IEKPF algorithm effectively improves the accuracy of SOC estimation.Finally,the paper also built a test platform for SOC estimation of lithium-ion battery,designed the hardware part and software part related to SOC estimation in the test platform,and verified the function of the designed system through experimental verification.The experimental results show that the system platform involved in this paper can meet the requirements of data acquisition accuracy related to battery SOC estimation,and the RTS-IEKPF algorithm has better accuracy in SOC estimation.The RTS-IEKPF algorithm studied in this paper has high precision in estimating battery SOC,fully meets the requirements of SOC for electric vehicles,it will accelerate the industrialization and practical process of electric vehicles,has a high promotion and application value.