Double Extended Kalman Filter Algorithm Based on Weighted Multi-Innovation and Weighted Maximum Correlation Entropy Criterion for Co-Estimation of Battery SOC and Capacity

Mingdong Lei; Bin Wu; Wenyao Yang; Peng Li; Jianhua Xu; Yajie Yang

doi:10.1021/acsomega.3c00918

Double Extended Kalman Filter Algorithm Based on Weighted Multi-Innovation and Weighted Maximum Correlation Entropy Criterion for Co-Estimation of Battery SOC and Capacity

ACS Omega. 2023 Apr 21;8(17):15564-15585. doi: 10.1021/acsomega.3c00918. eCollection 2023 May 2.

Authors

Mingdong Lei^{1

2

3}, Bin Wu¹, Wenyao Yang^{2

3}, Peng Li², Jianhua Xu^{2

4}, Yajie Yang^{2

4}

Affiliations

¹ School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
² School of Electronic Information and Electrical Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China.
³ Chongqing Engineering Research Center of New Energy Storage Devices and Applications, Chongqing University of Arts and Sciences, Chongqing 402160, China.
⁴ School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, No. 4, Chengdu 610054, China.

Abstract

Most of the traditional extended Kalman filter algorithms for the co-estimation of SOC and capacity of lithium-ion batteries are designed based on the minimum mean square error (MMSE) criterion, which may show superior performance in Gaussian noise scenes. However, due to the complexity of the battery operating environment, it is likely to face non-Gaussian noise (especially outlier noise), at which time the performance of the traditional extended Kalman filter algorithms will be seriously weakened. To solve the above problems, this paper first proposes a double extended Kalman filter algorithm based on weighted multi-innovation and weighted maximum correlation entropy (WMI-WMCC-DEKF) for the co-estimation of battery SOC and capacity. In this paper, the performance of the target algorithm is verified and compared by generating different types of noise from three noise models: weak Gaussian mixture noise, strong Gaussian mixture noise, and outlier noise. The maximum absolute error value (MAE) and root mean square error value (RMSE) of the WMI-WMCC-DEKF algorithm can achieve the highest performance improvement of 69.3 and 84.2% (SOC), 61.3, and 94.2% (capacity), respectively. The experimental results fully prove that the target algorithm has excellent performance against three kinds of noises.