基于电池簇放电电量的电池堆不一致性在线监测方法

doi:10.11930/j.issn.1004-9649.202211089

中国电力 ›› 2023, Vol. 56 ›› Issue (7): 207-215,227.DOI: 10.11930/j.issn.1004-9649.202211089

基于电池簇放电电量的电池堆不一致性在线监测方法

张媛, 夏向阳, 岳家辉, 刘代飞, 王明琦

长沙理工大学电气与信息工程学院，湖南长沙 410114

收稿日期:2022-11-24 修回日期:2023-06-07 发布日期:2023-07-28
作者简介:张媛(1999-),女,硕士研究生,从事储能安全与数值分析技术研究,E-mail:2577399887@qq.com;夏向阳(1968-),男,通信作者,博士,从事储能系统安全运行与智能控制研究,E-mail:xia_xy2022@163.com;岳家辉(1994-),男,博士研究生,从事储能电站安全与数值分析研究,E-mail:243952600@qq.com
基金资助:
国家自然科学基金资助项目（柔性直流输电交流侧故障下换流器多桥臂主动应对的能量调控机理及穿越控制研究，51977014）。

Online Monitoring Method of Battery Stack Inconsistency Based on Discharge Quantity of Battery Clusters

ZHANG Yuan, XIA Xiangyang, YUE Jiahui, LIU Daifei, WANG Mingqi

School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410014, China

Received:2022-11-24 Revised:2023-06-07 Published:2023-07-28
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Energy Regulation Mechanism and Fault Ride-through Control of Multi-armed Converter of AC Side Fault in MMC-HVDC System, No.51977014).

摘要/Abstract

摘要： 针对电池在生产过程中难以做到完全一致且在不同运行工况下电池老化程度不一致的情况，为避免大规模储能电站中电池“短板效应”的发生，提出一种基于电池簇放电电量的电池堆不一致性在线监测方法。将储能电站中的放电电量作为特征参数，定量分析电池堆与电池簇放电电量的对应关系，并对二者进行线性拟合，求导得到其变化速率，以实现电池堆一致性的在线监测；将变化速率记录为时序信号，并作为BP神经网络输入来实现短时预测，以达到预测储能电站安全运行的目的。最后，通过对湖南某储能电站实际运行数据进行分析，计算出其预测平均绝对误差为0.406%、均方误差为0.002%、均方根误差为0.489%，均小于0.5%，验证了所提方法的可行性与有效性。

关键词: 不一致性, 锂离子电池簇, 锂离子电池堆, 放电电量, 状态估计

Abstract: Batteries cannot remain completely consistent during production and the degree of aging is inconsistent in different operating conditions. Therefore, to avoid the “barrel effect” in large-scale energy storage power stations, this paper proposes an online monitoring method of battery stack inconsistency based on the discharge quantity of battery clusters. With the discharge quantity in the energy storage station as a characteristic parameter, the corresponding relationship between the battery stack and the discharge quantity of battery clusters is analyzed quantitatively. Meanwhile, linear fitting is conducted on the two variables to obtain their change rate k and realize the online monitoring of battery stack consistency. The change rate k is recorded as a timing signal and serves as a BP neural network input to achieve short-time prediction and accurately estimate the safe operation of the energy storage power station. Finally, by analyzing the actual operation data of an energy storage power station in Hunan, it is calculated that the MAE is 0.406%; the MSE is 0.002%; the RMSE is 0.489%. All of the values are less than 0.5%, verifying the feasibility and effectiveness of the proposed method.

Key words: inconsistency, lithium-ion battery cluster, lithium-ion battery stack, discharge quantity, state estimation

张媛, 夏向阳, 岳家辉, 刘代飞, 王明琦. 基于电池簇放电电量的电池堆不一致性在线监测方法[J]. 中国电力, 2023, 56(7): 207-215,227.

ZHANG Yuan, XIA Xiangyang, YUE Jiahui, LIU Daifei, WANG Mingqi. Online Monitoring Method of Battery Stack Inconsistency Based on Discharge Quantity of Battery Clusters[J]. Electric Power, 2023, 56(7): 207-215,227.

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https://www.electricpower.com.cn/CN/Y2023/V56/I7/207

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基于电池簇放电电量的电池堆不一致性在线监测方法

Online Monitoring Method of Battery Stack Inconsistency Based on Discharge Quantity of Battery Clusters

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基于电池簇放电电量的电池堆不一致性在线监测方法

Online Monitoring Method of Battery Stack Inconsistency Based on Discharge Quantity of Battery Clusters

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