Parameter Identification Method for Battery Model Based on Bayesian Decision-Making

doi:10.3969/j.issn.1008-0198.2024.04.003

Hunan Electric Power ›› 2024, Vol. 44 ›› Issue (4): 20-26.doi: 10.3969/j.issn.1008-0198.2024.04.003

• Invited Column:New Energy Generation and Energy Storage Technology • Previous Articles Next Articles

Parameter Identification Method for Battery Model Based on Bayesian Decision-Making

ZHANG Xingwei^1,2, SHAN Zhouping³, LIU Lihong², LIU Luyu⁴, ZHOU Kun¹, LENG Zhaojin¹

1. Hunan Economic Institute Electric Power Design Co.,Ltd.,Changsha 410007,China;
2. Hunan Engineering Research Center of Large-Scale Battery Energy Storage Application Technology, Changsha 410001,China;
3. State Grid Hunan Electric Power Company Limited Economic and Technological Research Institute, Changsha 410007,China;
4. School of Electrical Engineering, Hunan University, Changsha 410082, China

Received:2024-04-01 Revised:2024-05-07 Online:2024-08-25 Published:2024-09-09

Abstract

Abstract: In order to balance the parameter estimation accuracy and computational complexity of the equivalent circuit model of battery module (BM), an identification method of BM equivalent circuit model based on Bayesian decision theory is proposed, considering the inconsistency of internal resistance, capacity and other parameters of single battery in BM, combined with the basic characteristics of series/parallel circuits. Bayesian information criterion is introduced to estimate the optimal model parameters. The simplified modeling method, the overall modeling method and the combined modeling method are used to build two parallel and two series connection of BM equivalent circuit models in MATLAB/Simulink, and the simulation and verification are carried out in the constant current discharge and dynamic stress test conditions. The results show that the combination modeling method based on third-order RC and polynomial function fitting terms of 3 has high accuracy, and the model error does not exceed 1.97% in the dynamic stress test condition, which verifies the validity and accuracy of the proposed method of identifying the model parameters of the BM model, and provides an effective method for solving the high-precision equivalent circuit model of multiple types of energy storage.

Key words: battery module, Bayesian decision theory, equivalent model, parameter identification

CLC Number:

TM910.1

ZHANG Xingwei, SHAN Zhouping, LIU Lihong, LIU Luyu, ZHOU Kun, LENG Zhaojin. Parameter Identification Method for Battery Model Based on Bayesian Decision-Making[J]. Hunan Electric Power, 2024, 44(4): 20-26.

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Parameter Identification Method for Battery Model Based on Bayesian Decision-Making

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