摘要：Accurately estimating the state of charge (SOC) is crucial for energy storage battery management systems as it ensures battery performance and extends lifespan. However, existing deep learning-based methods often overlook the dynamic process information during battery charging and discharging, which compromises the accuracy of SOC estimation. To address this limitation, this paper proposes a novel SOC estimation method. First, we employ differential processing on the collected voltage, current, and temperature data to capture dynamic feature changes. Next, all features are normalized to ensure they are on the same scale. Finally, the processed data is divided into sliding windows and input into the TCN-BiLSTM-Attention Net (TBANet) model for SOC estimation. The results show that compared with traditional deep learning based SOC estimation methods, adding incremental features to TBANet improves the estimation accuracy by 15.8%. The average absolute error and root mean square error of the experimental results are 0.72% and 0.91%, respectively. In addition, this approach adopts transfer learning methods to verify the strong adaptability of the proposed method on different datasets, which highlights the robustness of TBANet and its potential for wide applicability in real-world scenarios.

关键字：energy storage battery; state of charge; process information; transfer learning; multiple incremental features

ISSN号：0013-4651

卷、期、页：卷171期7

发表日期：2024-07-01

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：JOURNAL OF THE ELECTROCHEMICAL SOCIETY

参与作者：Chen, Zhilong,何霆,Mao, Yingzhe,朱文龙,Xiong, Yifeng,Wang, Shen,Zeng, Jianhua

第一作者：徐泉,牛迎春

论文类型：期刊论文

论文概要：Chen, Zhilong,何霆,Mao, Yingzhe,朱文龙,Xiong, Yifeng,Wang, Shen,Zeng, Jianhua,徐泉,牛迎春，State of Charge Estimation Method of Energy Storage Battery Based on Multiple Incremental Features，JOURNAL OF THE ELECTROCHEMICAL SOCIETY，2024，卷171期7

论文题目：State of Charge Estimation Method of Energy Storage Battery Based on Multiple Incremental Features