摘要：The state of charge (SOC) is a critical state quantity that must be determined in real-time for a battery energy storage system (BESS). It is a prerequisite for the operation of a BESS. However, obtaining the precise value of SOC is challenging due to it being a hidden state quantity. Existing neural network models commonly employ an end-to-end prediction paradigm for SOC estimation, which fails to fully exploit the rich information present in the time-series battery data. Unlike most studies available in the literature, we propose a novel SOC prediction method named CLDMM. This method is the first to apply contrastive learning techniques from the image field to the SOC prediction of lithium batteries. The method utilizes data augmentation, a multi-scale encoder, and multi-layer perceptrons to learn latent representations and mix these with raw data proportionally for downstream predictive tasks. The performance of the proposed method is evaluated using the Panasonic NCR18650PF dataset, and ablation study were conducted. Experimental results show that CLDMM outperforms baseline methods, achieving an average mean absolute error (MAE) of 0.64% and an average maximum error (MAX) of 2.66%.

关键字：SOC; Lithium-ion battery; Contrastive learning

ISSN号：2213-1388

卷、期、页：卷: 71

发表日期：2024-11-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

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：SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS

参与作者：Xiong, Yifeng,何霆,朱文龙,Liao, Yongxin,Chen, Zhilong

第一作者：徐泉,牛迎春

论文类型：期刊论文

论文概要：Xiong, Yifeng,何霆,朱文龙,Liao, Yongxin,徐泉,牛迎春,Chen, Zhilong，The state of charge predication of lithium-ion battery energy storage system using contrastive learning，SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS，2024，卷: 71

论文题目：The state of charge predication of lithium-ion battery energy storage system using contrastive learning