Research On Estimation Of SOC And SOH Of Electric Vehicle Power Battery

Electric vehicles are one of the effective ways to solve the problems of energy depletion and environmental pollution.The power battery,which accounts for one-third of the cost of the vehicle,serves as the power source of the electric vehicle and determines the range of the vehicle.However,the current power battery and its management technology are not yet mature,which greatly restricts the development of electric vehicles and related battery industry,so it is necessary to manage the power battery efficiently.Accurate condition estimation is one of the most critical parts of Battery Management System（BMS）,which not only ensures the safe use,reliable operation and lifetime of power batteries,but also provides data support for vehicle energy and balance management.Among them,the State of Charge（SOC）and State of Health（SOH）of the battery reflect the remaining charge and the aging condition of the battery,respectively.As the core of state estimation,power battery SOC and SOH estimation has been a hot and difficult research area.This article uses 18650 ternary lithium batteries commonly used in electric vehicles as research objects.Based on the fundamental analysis of battery power characteristics and the study of the main factors influencing SOC and SOH.a DP equivalent circuit model with temperature variation parameters is established and the model parameters are identified,and a SOC estimation algorithm based on adaptive sliding mode observation is designed to update the temperaturedependent parameters in the observer gain matrix in real time to achieve accurate estimation of SOC under different ambient temperatures.The effects of power battery terminal voltage,operating current,temperature,capacity decay and other factors are fully considered to design Dual Sliding Mode Observer（DSMO）for joint estimation of battery SOC and SOH.The details of the study are as follows.1)From the analysis of various energy batteries According to the basic characteristics of energy batteries A DP equivalent circuit is modeled with different temperature parameters.the calculation formula for parameter identification is derived,the battery pulse discharge experiment is designed,the OCV-SOC fitting curve is obtained,Model parameters are specified by the least squares method with forgotten factor values.The simulation results show that the model is extremely accurate and can accurately characterize the performance of the supply battery.2)An Adaptive Sliding Mode Observer（ASMO）was designed to estimate the SOC.The temperature-dependent parameters in the observer gain matrix are updated in real time,and the algorithm is simulated and verified at low,normal,and high temperatures under constant flow and Urban Dynamometer Driving Schedule（UDDS）conditions,respectively.The experimental results show that the adaptive sliding mode observer algorithm designed in this paper has less than 2% error in power battery SOC estimation under different temperature conditions,which can meet the accuracy requirement of SOC estimation for electric vehicles.3)Based on the SOC estimation study,a Dual Sliding Mode Observer（DSMO）is used for the joint estimation of battery SOC and SOH,and the effectiveness and accuracy of the DSMO is verified by simulation under UDDS conditions.4)in the battery power management system Methods for estimating SOC and SOH are examined experimentally.The results show that the estimation method designed in this paper has the advantages of fast convergence and good durability and the estimation error of SOC and SOH both satisfy the accuracy requirements.To estimate the state of the battery,power supply,electric vehicle.