The SOC online estimation of valve-regulated lead-acid battery based on DKF-Bi-LSTM
Accurate estimation of state of charge(SOC)of VRLA battery plays an important role in reliability and security of DC system in substation.In order to improve the accuracy of SOC estimation,a method for estimation of SOC of VRLA battery based on DKF-Bi-LSTM is proposed.Based on the double Kalman filter algorithm with secondary structure,the model estimation and state estimation are carried out respectively.Firstly,the model parameters are dynamically tracked by Kalman filter(KF)algorithm,and the battery SOC is estimated by extended Kalman filter(EKF)algorithm.Then,the online estimation result,current,voltage,temperature value are taken as the input of Bi-LSTM neural network,battery SOC real value as network output,and realize the estimation of battery SOC.Finally,compared with DKF and Bi-LSTM algorithm,The root mean square error of SOC prediction(RMSE)of DKF-Bi-LSTM algorithm is smaller,and its SOC online estimation method has higher accuracy.
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