Work

Joint Adaptive Assessment of the State of Charge of Lithium Batteries at Varying Temperatures

Year: 2025

Type: article

Abstract: The fixed battery model parameters result in poor real-time state of charge (SOC) estimation, and the model-based estimation method of lithium battery SOC ignores the consequences of various working conditions and temperatures with the battery, resulting in low estimation accuracy. Based on multi-new information theories, this work proposes a joint evaluation method for lithium battery state of charge using adaptive extended Kalman filtering (AEKF) and variable forgetting factor recursive least squares (VFFRLS). Through testing at various temperatures and working conditions and a comparison with the conventional joint method, the efficacy of the algorithm presented in this study is confirmed. The findings demonstrate that the maximum root mean square error is kept at 1.57% and that the joint VFFRLS-AEKF technique suggested in this paper can effectively predict the lithium battery SOC. In contrast, the algorithm in this paper takes an average of less than 151 s to converge to within the 2% error range of the true value under various working conditions when the initial SOC value is set incorrectly. It also has good robustness and adaptability to adjust well to complex working conditions, which enhances the ability to predict energy consumption and the battery’s efficiency.

Source: Batteries

DOI: 10.3390/batteries11040130

Authors: Xuejuan Zhao, Zhigang Zhang, Xinyang Liu, Yuanxiao Cai

Institutions: Ningxia University, Xinhua College of Ningxia University, Changsha University of Science and Technology, Ningxia Vocational Technical University of Industry and Commerce

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,258