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Estimation of Battery Health based on Improved Unscented Kalman Filtering Algorithm

Volume 15, Number 5, May 2019, pp. 1482-1490
DOI: 10.23940/ijpe.19.05.p25.14821490

Haiying Wang, Yu Wang, Zhilong Yu, and Ran Li

School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on December 18, 2018; Revised on January 16, 2019; Accepted on February 12, 2019)

Abstract:

In connection with the life aging problem of valve-regulated lead-acid batteries (VRLA), to ensure that batteries have a good performance and long life, we have considered VRLA as the backup power that works in a complex environment for a long time. The noise signal is analyzed, a VRLA health estimation model of the double adaptive Kalman algorithm is built, and a method of estimating battery health based on the improved unscented Kalman filter is put forward by using the battery Thevenin equivalent circuit model. The test results show that the average relative error of the VRLA health state estimated by the improved UKF algorithm is 3.1% and it can estimate the VRLA health state effectively.

 

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