基于二值化神经网络的大规模储能电站电池容量衰退预测
作者:
作者单位:

(1.国网山东省电力公司经济技术研究院,山东 济南 250021;2.湖南大学电气与信息工程学院,湖南 长沙 410082)

通讯作者:

黄小庆(1981—),女,副教授,博士,主要从事电力系统分析、电动汽车入网研究、电力大数据方面的研究;E?mail:huangxiaoqing@hnu.edu.cn

中图分类号:

TM912

基金项目:

山东智源电力设计咨询有限公司项目(SGSDJY00ZYJS2310255)


Battery capacity degradation prediction of large‑scale energy storage power station based on binary neural network
Author:
Affiliation:

(1. Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China; 2. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

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    摘要:

    大规模储能电站的电池单体数量庞大。传统卷积神经网络在电池容量衰退预测中具备较高的预测精度,但其对计算资源需求较高,限制了其在储能电站电池管理系统中的应用。为此,提出一种基于二值化神经网络(binary neuval network,BNN)的电池容量衰退预测方法。首先,设计一个将网络权重和激活函数二值化的轻量化模型,并以电池的放电容量-电压曲线作为输入,输出关键参数的累积分布函数值。其次,通过二分法求解该参数,并将其代入双曲线方程进行容量衰退预测。最后,基于锂电池公开数据集仿真表明:在预测精度与传统神经网络模型相当的情况下,所提模型的参数量减少48.9%,预测速度提升22.37%,可降低模型复杂度和设备算力成本,为大规模储能电站电池管理提供一个更高效、更轻量的预测方法。

    Abstract:

    The number of battery cells in a large-scale energy storage power station is enormous. The conventional convolutional neural networks achieve high prediction accuracy for battery capacity degradation. However, they have high demand for computational resources, which limits their application in practical battery management systems of energy storage power stations. To solve this problem, this paper proposes a battery capacity degradation prediction method based on a binary neural network. First, a lightweight model is designed by binarizing the network weights and activation functions, using the discharge capacity-voltage curve of the battery as input to output the cumulative distribution function values of key parameters. Subsequently, these parameters are solved using the bisection method and substituted into a hyperbola equation to predict the capacity degradation curve. Finally, experiments are conducted on a public lithium-ion battery dataset. The results show that under the same prediction accuracy as traditional neural network models, the proposed model reduces the number of parameters by 48.9% and improves prediction speed by 22.37%. This study reduces model computational complexity and hardware computational cost and also provides a more efficient and lightweight prediction method for battery management in large-scale energy storage power stations.

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杨 夯,郭宜果,黄小庆,等.基于二值化神经网络的大规模储能电站电池容量衰退预测[J].电力科学与技术学报,2025,40(2):227-234.
YANG Hang, GUO Yiguo, HUANG Xiaoqing, et al. Battery capacity degradation prediction of large‑scale energy storage power station based on binary neural network[J]. Journal of Electric Power Science and Technology,2025,40(2):227-234.

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  • 在线发布日期: 2025-06-06
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