Robustness enhanced capacity estimation method for lithium-ion batteries based on multi-voltage-interval incremental capacity peaks

Frontiers in Energy Research

Energy Research

Binxiang Xu¹  Linfeng Zheng¹  Xianli Guo¹  Jing Xu² 

[1] International Energy College, Jinan University, Zhuhai, China;Institute of Rail Transportation, Jinan University, Zhuhai, China;Research Center of Grid Energy Storage and Battery Application, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China;

关键词: battery capacity estimation;    incremental capacity peak;    multiple voltage intervals;    battery management system (BMS);    back-propagation neural network (BPNN);   

DOI  :  10.3389/fenrg.2023.1207194

 received in 2023-04-17, accepted in 2023-05-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

Accurate battery capacity estimation can contribute to safe and reliable operations of lithium-ion battery systems. The incremental capacity (IC) based techniques provide promising estimates of battery capacity. However, curve smoothing algorithms are usually required in the IC-based methods, which introduce additional errors and are computationally burdensome. To address this issue, this work proposes a novel approach using multi-voltage-interval IC peaks combined with a back-propagation neural network (BPNN) for battery capacity estimation. Multiple voltage intervals covering relatively narrow and wide values are applied for computing IC curves to enhance the estimation robustness. In particular, there is no need to employ smoothing algorithms. A BPNN is then applied to approximate the correlation between multi-voltage-interval IC peak and capacity. Besides, a five-point moving window technique is proposed to capture multi-voltage-interval IC peaks online effectively. Experimental results show capacity estimates with the majority of relative errors of ±1% and the maximum error of 2%.

【 授权许可】

Unknown   
Copyright © 2023 Guo, Xu, Zheng and Xu.