Research On State Of Charge And State Of Health Estimation For Vehicle Lithium-ion Battery

The problems of energy exhaustion and environmental pollution become increasingly more serious,therefore the development of electric vehicles has become one of the ways to solve the problems.As the energy storage unit of the electric vehicles,power battery accounts for the cost of electric vehicles 1/3 to 1/2.The technology development of power battery still can't fully meet the needs of electric vehicles,therefore it becomes the key bottleneck restricting the development of electric vehicles.The special technology research of power battery was included in the development of strategic emerging industries of the state in "13th Five-Year Plan".Power battery as the energy source of electric vehicles,the accurate assessment of state of charge(SOC)and state of health(SOH)is the precondition to ensure the safe and efficient operation of the battery.In the actual application,the temperature,aging,working conditions and other factors make the accurate state estimation difficult.In this paper,lithium-ion battery as the research object,and the estimation of SOC and SOH for the lithium-ion battery were studied.First,the performance and advantages and disadvantages of power battery are summarized.Based on the battery testing platform,the power battery testing plan is designed.According to the experimental data of the power battery,analyzes the temperature characteristic,the characteristic of different discharge ratio,hysteresis characteristic and aging properties.The precise battery model is the prerequisite for accurate state estimation.Based on the comparative analysis of the existing battery model,the second-order RC equivalent circuit model was established.In view of the inherent hysteresis characteristics of the power battery,the parameters of the model are identified for the charging and discharging directions respectively.And according to the different characteristics of LiFeP04 batteries and ternary batteries,choose the different open-circuit voltage function.The accuracy of the model is verified by experiments,and analyzes the causes of the error and the influence of aging on the battery model.Aiming at the shortcomings of the existing SOC estimation method,this paper adopts the adaptive strong tracking unscented Kalman filter(ASTUKF)algorithm to estimate SOC.This method can overcome the shortcomings of extended Kalman filter(EKF)algorithm and the unscented Kalman filter(UKF)algorithm,which is sensitive to the accuracy of the model and the initial value of the noise covariance.Compared to the traditional strong tracking algorithm,it doesn't need calculate the Jacobian matrix,and reduces the amount of computation.Finally,the accuracy of the three algorithms for estimating the SOC is compared by the different operating conditions of the LiFeP04 batteries and ternary batteries.The results show that the ASTUKF algorithm improves the accuracy of the state estimation,the state mutation tracking ability,and robustness is good.After analyzing the definition and influencing factors of the battery SOH,the capacity of the battery is taken as the health status.After comparing the battery capacity declining models,the power function model is used to describe the battery capacity decline.We uses the EKF algorithm and UKF algorithm to update the parameters of the model respectively,then estimate the capacity of the lithium-ion battery.Finally,the results show that the UKF algorithm can estimate the battery capacity more accurately.