A novel model for estimation of the discharge ohm internal resistance of lithium‐ion battery combining the temperature and different state‐of‐charge segments. (17th September 2022)

• Record Type: Journal Article
• Authors: Chen, Lin; Wu, Shuxiao; Feng, Zhe; Liu, Bohao; Ding, Yunhui; Pan, Haihong
• Is Part Of: International journal of energy research. Volume 46:Number 15(2022)
• Page Start: 24465
• Page End: 24475
• Publication Date: 2022-09-17
• Subjects: cubic polynomial -- discharge ohm internal resistance -- exponential function -- SoC segment; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.8762
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Assessment of discharge ohm internal resistance (DOR) is a critical step for the battery thermal management system. However, the influence of various variables on DOR makes it challenging to develop a reliable and dependable model. Therefore, an approach for estimating DOR that relies on single‐factor (temperature) is developed by employing cubic polynomial and exponential fitting functions. First, the SoC range is divided into two segments: 0.3‐0.6 and 0.6‐0.9 according to the mathematical relationship between DOR and SoC. To emphasize temperature as the sole element to determine DOR and to simplify the coupling impact of many variables in each SOC segment. Additionally, the established models of two segments are used to estimate DOR at 5°C, 25°C, 35°C, 45°C, and 55°C, and to predict the DOR at 15°C. Finally, the model in SoC = 0.6‐0.9 is used for SoC = 1. The DOR estimation error is less than 3 mΩ. When the fitting models at the discharge rate of 1 C are transferred to predict the DOR at 0.75 C and 1.25 C, the maximum error is only 4.39 mΩ. The results show that the proposed method for estimating DOR has a high degree of generalizability and accuracy, making it adaptable to dynamic settings with varying temperatures.