An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

• Published in: Ionics Vol. 30; no. 2; pp. 991 - 1006
• Main Authors: Guan, Zhe, Yang, Fa Zhi, Yu, Tao hua, An, Aimin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024; Springer Nature B.V
• Subjects: Adaptive control; Algorithms; Chemistry; Chemistry and Materials Science; Condensed Matter Physics; Control methods; Dynamic characteristics; Electric charge; Electrochemistry; Energy Storage; Equivalent circuits; Errors; Innovations; Kalman filters; Lithium batteries; Management systems; Optical and Electronic Materials; Parameter identification; Power management; Renewable and Green Energy; Robust control; State estimation; State of charge; Lithium-ion battery; Battery state; Multi-innovation; Dual Kalman online identification; Adaptive robust Kalman filtering
• ISSN: 0947-7047; 1862-0760
• DOI: 10.1007/s11581-023-05314-2

In response to the inaccurate estimation of State of Charge (SOC) in current power battery management systems, and considering that SOC may be subject to offset constraints from State of Health (SOH) when estimated separately. A method combining adaptive robust Kalman filtering with multiple innovation theories and online identification of dual Kalman filtering parameters is proposed. This control method is based on adaptive robust Kalman control. It corrects the estimated values using multiple innovation values and Kalman gains at different times. Dual Kalman filtering is used for online parameter identification and joint estimation of battery health status, which increases the amount of error information and provides an optimized method for accurate estimation of SOC and SOH. To verify the rationality of the algorithm, a second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is carried out under different operating conditions. The experimental results show that the average error of SOC under three operating conditions: UDDS, FUDS, and US06 is 0.56%, 0.31%, and 1.23%, respectively. The estimated error of SOH after stabilization is less than 1.73%. The estimation error is the lowest among the five estimation algorithms. The proposed algorithm has been verified to have good accuracy and convergence. The multi-innovation adaptive robust dual Kalman filtering algorithm provides a theoretical basis for accurate state estimation and widespread application of lithium batteries.