Research On SOC And SOH Estimation Of Power Lithium Battery

Power lithium batteries have become an important power source in electric vehicles because of their excellent performance such as high energy density and long cycle life.Effective management and application of them are key technical issues in the development of electric vehicles.Battery management system(BMS),as a power battery monitoring and management system,focuses on the estimation of state of charge(SOC)and state of Health(SOH).Accurate state estimation is the guarantee of the normal operation of the power battery system and the safe and reliable operation of the vehicle,and it is also the premise of optimizing the power battery energy system,satisfying the driver to the best extent,and giving full play to the performance of the power battery.It is of great significance for the further promotion of electric vehicles.This paper first summarizes the development of new energy vehicles,analyzes the status quo and trend of power battery technology,and then expounds the current research status of SOC and SOH estimation at home and abroad.Then,the differences between ternary power lithium battery and ordinary lithium battery,as well as the differences of different packaging types of power lithium battery and their advantages and disadvantages are briefly introduced from the structure and working principle of lithium battery.This paper briefly describes the performance characteristics and current application scenarios of the current mainstream lithium iron phosphate battery and ternary lithium battery.The cylindrical 18650 power lithium battery in ternary lithium battery is selected as the research object,and the test process is designed to analyze the basic conventional electrical performance test content.Secondly,a dual-polarization equivalent circuit model was established before the state estimation of power lithium battery.Considering the influence of aging,the Capacity-OCV-SOC response surface is introduced,and the deviation compensation recursive least square algorithm is proposed to identify the resistance capacitance parameters.In the application part of state estimation algorithm,based on the combination and improvement of extended Kalman filter(EKF)and H_∞ filter(EHF),the improved AEKF/EHF combination estimation algorithm is established.Two dynamic operating conditions parameters,NEDC and UDDS,which are commonly used to simulate the actual driving of electric vehicles,and tests the combination algorithm by preset different initial error and adding bias noise.The results show that the improved combination algorithm has high estimation accuracy and robustness.Considering the influence of battery aging,a reasonable and practical strategy for estimating DC impedance of power battery is proposed based on internal resistance and capacity characterization.The available capacity is estimated based on ampere hour integration and periodic capacity calibration,and two cases are used for performance analysis.Finally,the hardware test platform is built,and the special battery detection chip is used to realize the data acquisition of voltage,current,and temperature.The test results show that the SOC state estimation of the improved combination algorithm under dynamic conditions has the expected estimation accuracy,error correction ability(convergence),and noise suppression,and the method of periodically estimating available capacity based on the SOC estimation value is feasible.