王子毅, 朱承治, 周杨林, 王红军, 慈松, 康飞宇, 陈铁义. 基于动态可重构电池网络的OCV-SOC在线估计[J]. 中国电机工程学报, 2022, 42(8): 2919-2928. DOI: 10.13334/j.0258-8013.pcsee.220314
引用本文: 王子毅, 朱承治, 周杨林, 王红军, 慈松, 康飞宇, 陈铁义. 基于动态可重构电池网络的OCV-SOC在线估计[J]. 中国电机工程学报, 2022, 42(8): 2919-2928. DOI: 10.13334/j.0258-8013.pcsee.220314
WANG Ziyi, ZHU Chengzhi, ZHOU Yanglin, WANG Hongjun, CI Song, KANG Feiyu, CHEN Tieyi. OCV-SOC Estimation Based on Dynamic Reconfigurable Battery Network[J]. Proceedings of the CSEE, 2022, 42(8): 2919-2928. DOI: 10.13334/j.0258-8013.pcsee.220314
Citation: WANG Ziyi, ZHU Chengzhi, ZHOU Yanglin, WANG Hongjun, CI Song, KANG Feiyu, CHEN Tieyi. OCV-SOC Estimation Based on Dynamic Reconfigurable Battery Network[J]. Proceedings of the CSEE, 2022, 42(8): 2919-2928. DOI: 10.13334/j.0258-8013.pcsee.220314

基于动态可重构电池网络的OCV-SOC在线估计

OCV-SOC Estimation Based on Dynamic Reconfigurable Battery Network

  • 摘要: 随着大规模储能系统的广泛发展,快速准确地估计锂离子电池的荷电状态(state of charge,SOC)对系统的安全可靠运行至关重要。然而,在传统的固定串并联电池单元/模块拓扑结构中,无法直接测量电池单元/模块的开路电压(open circuit voltage,OCV),也就无法建立OCV-SOC映射关系来准确估计SOC。对此,提出一种基于新型动态可重构电池网络的精准SOC估计方法。该方法可以在1s内测量得到OCV,然后使用梯度增强决策树估计电池单元/模块的准确SOC。实验结果表明该方法的高效率和有效性,为电池状态估计提供了一个范式结构。

     

    Abstract: With the extensive development of large-scale energy storage systems, fast and accurate estimation of lithium-ion batteries' state of charge (SOC) is critical for safe and reliable system operation. However, under the traditional fixed series-parallel battery cell/module topology, the open-circuit voltage (OCV) of a battery cell/module cannot be directly measured. Therefore, it is unlikely to establish an OCV-SOC mapping relationship for accurate SOC estimation. This paper proposed a new method for accurate SOC estimation based on a novel dynamic reconfigurable battery (DRB) network. This DRB-based SOC estimation method could directly measure the OCV in seconds, and then the accurate SOC of a battery cell/module could be estimated by using the gradient boosting decision tree (GBDT). The experimental results show the proposed method's effectiveness and efficiency, which provides a paradigm-shifting framework for battery status estimation.

     

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