State of Charge Estimation for Lithium Battery Based on Fractional Order Square Root Cubature Kalman Filter and Adaptive Multi-innovation Unscented Kalman Filter

Authors

  • Ying Wei Faculty of Engineering, Anhui Sanlian University, China

DOI:

https://doi.org/10.7546/CRABS.2024.04.02

Keywords:

SOC estimation, fractional order model, square root cubature, Kalman filter, adaptive multi-innovation unscented Kalman filter

Abstract

Accurate state of charge (SOC) estimation of batteries is of great significance for electric vehicles. A SOC estimation method based on a fractional order square root cubature Kalman filter (FOSRCKF) and an adaptive multi-innovation unscented Kalman filter (AMIUKF) is proposed. The battery is modelled using fractional order calculus theory and the model parameters are identified by adaptive genetic algorithm. The FOSRCKF estimates the battery SOC, while the AMIUKF online updates the internal resistance in the model, and there exchanges information between two filters. The experimental results under the Urban Dynamometer Driving Schedule (UDDS) and the US06 Highway Driving Schedule show that the proposed method has lower SOC estimation error and lower terminal prediction error compared with the traditional SRCKF method based on integer order models, which demonstrates the effectiveness, accuracy and robustness of the proposed method.

Author Biography

Ying Wei, Faculty of Engineering, Anhui Sanlian University, China

Mailing Address:
Faculty of Engineering,
Anhui Sanlian University,
Hefei 230601, China

E-mail: weiying8ths@sohu.com

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Published

26-04-2024

How to Cite

[1]
Y. Wei, “State of Charge Estimation for Lithium Battery Based on Fractional Order Square Root Cubature Kalman Filter and Adaptive Multi-innovation Unscented Kalman Filter”, C. R. Acad. Bulg. Sci. , vol. 77, no. 4, pp. 485–495, Apr. 2024.

Issue

Section

Mathematics