基于多循环特征的储能电池SOH估计模型

doi:10.19799/j.cnki.2095-4239.2023.0485

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (11): 3488-3498.doi: 10.19799/j.cnki.2095-4239.2023.0485

基于多循环特征的储能电池SOH估计模型

张宣梁¹(), 何霆¹(), 朱文龙¹, 王屾², 曾建华², 徐泉³, 牛迎春³

^1.华侨大学计算机科学与技术学院，福建厦门 361021
^2.中海储能科技（北京）有限公司，北京 102308
^3.重质油国家重点实验室，中国石油大学（北京），北京 102299

收稿日期:2023-07-17 修回日期:2023-08-02 出版日期:2023-11-05 发布日期:2023-11-16
通讯作者: 何霆 E-mail:zhangxuanliang1999@163.com;xuantinghe@hit.edu.cn
作者简介:张宣梁（1999—），男，硕士研究生，研究方向为数据驱动的电池状态估计方法，E-mail：zhangxuanliang1999@163.com；
基金资助:
国家自然科学基金国际（地区）合作与交流项目(52211530034)

A SOH estimation model for energy storage batteries based on multiple cycle features

Xuanliang ZHANG¹(), Ting HE¹(), Wenlong ZHU¹, Shen WANG², Jianhua ZENG², Quan XU³, Yingchun NIU³

^1.School of Computer Science and Technology, Huaqiao University, Xiamen 361021, Fujian, China
^2.ZhongHai Energy Storage Technology (Beijing) Co. Ltd. , Beijing 102308, China
^3.State Key Laboratory of Heavy Oil, China University of Petroleum, Beijing 102299, China

Received:2023-07-17 Revised:2023-08-02 Online:2023-11-05 Published:2023-11-16
Contact: Ting HE E-mail:zhangxuanliang1999@163.com;xuantinghe@hit.edu.cn

摘要/Abstract

摘要：

准确估计电化学储能电池的SOH(state of health)对于确保电池的安全可靠工作至关重要。数据驱动方法在SOH估计领域得到广泛应用，但现有方法忽略了电池循环过程中多个连续循环之间的时序健康信息和特征挖掘，以及这些特征与SOH值之间的关系。为解决这些问题，本研究提出了一种名为MCNet(multi-cycle net)的新型SOH估计模型。该模型不需要手动提取健康特征，只需输入电池充电阶段的电流和电压，即可自动挖掘单次循环中与SOH估计相关的特征，并提取多个连续循环之间的相关特征，进而融合上述特征进行SOH估计。首先，为了构造模型的多循环张量输入数据并提升模型的收敛速度，将每个循环内充电阶段长度不同的采样数据进行长度对齐、最大-最小归一化以及拼接多个连续历史循环数据的预处理；其次，将上述预处理后的张量数据作为模型的输入，建立MCNet模型对公开电池数据集进行预测，平均绝对误差都在1%以内、均方根误差都在1.5%以内；最后，将本文所提出的模型与其他常用的序列预测模型进行比较，并与仅使用单次循环下的电流、电压数据作为输入进行了对比实验，结果表明，本文提出的模型具有较高的SOH估计精度，并且使用多个循环的电流、电压数据作为输入可以提升估计精度。

关键词: 电化学储能电池, 健康状态, 多循环特征, Transformer, 门控循环单元

Abstract:

Accurate estimation of the state of health (SOH) of electrochemical energy storage batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have been widely used for SOH estimations. However, existing methods overlook the temporal health information and feature extraction between multiple consecutive cycles of battery operation and the relationship between these features and the SOH value. This study proposes a novel SOH estimation model called multi-cycle net (MCNet) to address these issues. This model does not require manual extraction of health features; it only takes current and voltage measurements during the charging phase of the battery as input. It automatically extracts features relevant to the SOH estimation within each cycle, extracts relevant features between multiple consecutive cycles, and then combines them for SOH estimation. First, to construct the multicycle tensor input data and improve the convergence speed of the model, the sampled data from the charging phase of each cycle with varying lengths were preprocessed through length alignment, maximum-minimum normalization, and concatenation of multiple consecutive historical cycle data. Second, the preprocessed tensor data were used as input to build the MCNet model for predicting the SOH using a publicly available battery dataset. The average-absolute- and root-mean-square errors were within 1% and 1.4%, respectively. Finally, the proposed model was compared with other commonly used sequence prediction models, and a comparative experiment was conducted using only single-cycle current and voltage data as inputs. The results demonstrate that the proposed model achieves higher accuracy in SOH estimation, and using multiple cycles' current and voltage data as input improves the estimation accuracy.

Key words: electrochemical energy storage batteries, state of health, multi-cycle features, transformer, GRU

中图分类号:

TM 911

张宣梁, 何霆, 朱文龙, 王屾, 曾建华, 徐泉, 牛迎春. 基于多循环特征的储能电池SOH估计模型[J]. 储能科学与技术, 2023, 12(11): 3488-3498.

Xuanliang ZHANG, Ting HE, Wenlong ZHU, Shen WANG, Jianhua ZENG, Quan XU, Yingchun NIU. A SOH estimation model for energy storage batteries based on multiple cycle features[J]. Energy Storage Science and Technology, 2023, 12(11): 3488-3498.

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实验组	训练集	测试集
组1	CS2_36、CS2_37、CS2_38	CS2_35
组2	CS2_35、CS2_37、CS2_38	CS2_36
组3	CS2_35、CS2_36、CS2_38	CS2_37
组4	CS2_35、CS2_36、CS2_37	CS2_38

实验组	方法	MAE	RMSE
组1	单循环	0.65%	1.14%
组1	10循环	0.55%	1.01%
组2	单循环	0.72%	1.00%
组2	10循环	0.65%	1.04%
组3	单循环	0.85%	1.26%
组3	10循环	0.60%	0.86%
组4	单循环	1.02%	1.56%
组4	10循环	0.89%	1.36%

模型	组1		组2		组3		组4
模型	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
BiLSTM	1.71%	0.91%	1.49%	1.06%	1.09%	0.78%	1.46%	0.95%
LSTM	1.59%	0.93%	1.33%	0.92%	1.09%	0.81%	1.54%	0.98%
BiGRU	1.72%	1.02%	1.16%	0.77%	1.14%	0.79%	1.37%	1.02%
GRU	1.66%	0.97%	1.32%	0.95%	1.35%	1.13%	1.81%	1.15%
MCNet	1.01%	0.55%	1.04%	0.65%	0.86%	0.60%	1.36%	0.89%

电池	10循环		1循环
电池	RMSE	MAE	RMSE	MAE
电池2	1.39%	0.73%	1.47%	0.72%
电池4	0.30%	0.22%	0.35%	0.27%
电池5	0.48%	0.45%	0.59%	0.54%
电池6	0.47%	0.25%	0.46%	0.28%

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基于多循环特征的储能电池SOH估计模型

A SOH estimation model for energy storage batteries based on multiple cycle features

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