Research On Joint Estimation Algorithm Of SOC And SOH For Lead-Acid Batteries

Lead-acid batteries have the advantages of high safety,low cost,and stable operation,and occupies the leading position in the market of low-speed electric vehicles.State of charge(SOC)and state of health(SOH)of batteries are the key indicators of battery management system monitoring,and their accurate estimation is an important premise to ensure the normal charging and discharging of batteries and ensure the safe and efficient operation of low-speed electric vehicles.At present,the joint estimation accuracy of SOC and SOH for battery management systems applied to low-speed electric vehicles is not high,and the phenomenon of overcharge and over discharge of batteries still exists.Therefore,in order to improve the accuracy of SOC and SOH estimation of lead-acid battery and extend the service life of battery,thesis takes VRLA battery as the research object to study the SOC and SOH estimation method of battery.To accurately obtain the battery voltage,current,temperature,and other parameters,the battery monitoring system software and hardware were designed to meet the requirements of experimental testing.After analyzing the complexity and accuracy of the existing equivalent circuit models,the second-order RC equivalent circuit model of lead-acid batteries is selected.In order to improve the accuracy of parameter identification,the variable forgetting factor recursive least square method(VFFRLS)was proposed to identify the unknown resistance and capacitance parameters online.The validity of the model is verified by using the experimental data in MATLAB software.Aiming at the problem that the estimation error of SOC of lead-acid batteries based on unscented Kalman filter(UKF)algorithm is large by ignoring the change of system noise,an adaptive unscented Kalman filter(AUKF)algorithm combining UKF algorithm and improved Sage-Husa filter algorithm is proposed for SOC estimation.The improved Sage-Husa filtering algorithm is used to adjust the system noise adaptively and update the noise covariance.In addition,the multi-information identification method is introduced to improve the AUKF algorithm,and the single information vector is extended to the multi-information matrix to realize the state variable estimation update.Finally,the multi-information adaptive unscented Kalman filter(MI-AUKF)algorithm is brought up.The experimental data of pulse discharge and UDDS cycle condition are used to verify the effectiveness of the algorithm.The results show that compared with the UKF and AUKF algorithms,the MI-AUKF algorithm can estimate the SOC of battery with higher accuracy.The extended Kalman filter(EKF)algorithm is used to estimate the maximum available capacity of the battery,and then the SOH estimate is obtained.Aiming at the problem that the accuracy of maximum available capacity affects the estimation accuracy of SOC and SOH of battery,a joint estimation algorithm based on MI-AUKF+EKF is proposed to estimate the SOC and maximum available capacity of lead-acid batteries simultaneously.In MATLAB software,pulse discharge experiment data and UDDS working condition experiment data are used to verify the SOC estimation results of the joint estimation algorithm.Compared with the MI-AUKF algorithm,the joint estimation algorithm has a smaller SOC error and can also estimate SOH more accurately.The results show that the combined estimation algorithm based on MI-AUKF+EKF is practical to estimate the SOC and SOH of lead-acid batteries.