An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles. [Dataset]

Abstract

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring. This output contains supplementary files associated with the original journal article.

Citation

ZHU, C., WANG, S., YU, C., ZHOU, H., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles. [Dataset]. Journal of energy storage [online], 88, article number 111552. Available from: https://www.sciencedirect.com/science/article/pii/S2352152X2401137X?via%3Dihub#s0100

• Acceptance Date: Mar 30, 2024
• Online Publication Date: Apr 4, 2024
• Publication Date: May 30, 2024
• Deposit Date: Apr 15, 2024
• Publicly Available Date: Apr 5, 2025
• Publisher: Elsevier
• DOI: https://doi.org/10.1016/j.est.2024.111552
• Keywords: Lithium-ion battery; SOC; Cauchy robust correction; Sage Husa extended Kalman filtering; Dual polarization model
• Public URL: https://rgu-repository.worktribe.com/output/2303090
• Related Public URLs: https://rgu-repository.worktribe.com/output/2299385 (Journal article)
• Type of Data: Supplementary xlsx. files.
• Collection Date: Apr 27, 2022
• Collection Method: To verify the reliability of the Dual Polarization model and the superiority of the CRC-SHEKF algorithm, the FFRLS algorithm is used to identify the model parameters. Compare the estimated voltage with the experimental voltage to verify the reliability of the model. The resulting parameters will be passed to the CRC-SHEKF algorithm to estimate the lithium battery SOC. When estimating SOC, the voltage and current under three dynamic working conditions, HPPC, DST, and BBDST, will be used as inputs. The ampere-hour integration method is a classic algorithm for estimating SOC. The calculation formula is the definition of SOC, which estimates SOC by accumulating the amount of charge and discharge during the battery charging and discharging process. This method only records the incoming and outgoing battery power from the outside, which is a theoretical value, and ignores the changes in the internal state of the battery. This article takes the SOC theoretical calculation value of the ampere-hour integration method as the true value and compares it with the estimated values of other algorithms. The output SOC estimated value is compared with the actual SOC value to verify the accuracy of the CRC-SHEKF algorithm. In this paper, mean absolute error (MAE), root mean square error (RMSE), maximum error (MAX), and computing time are selected as evaluation indexes.