基于注意力改进BiGRU的锂离子电池健康状态估计

doi:10.19799/j.cnki.2095-4239.2021.0099

摘要/Abstract

摘要：

锂离子电池的健康状态(state of health， SOH)是电池管理系统的核心问题，对其精确的评估能够保障电池的安全可靠运行。然而在实际应用中，容量较难直接测得，导致SOH估算困难。为了获得准确的SOH，本文提出一种基于注意力改进双向门控循环单元(BiGRU)的锂离子电池SOH估计方法。首先提取电池充放电曲线中的电压、电流与阻抗等参数，通过自编码器(auto encoder, AE)对其降维，提取特征量并减少数据间的冗余性。其次，引入注意力机制(attention mechanism， AM)对输入变量分配权重，突出对SOH估计起到关键作用的特征量。最后，利用BiGRU学习输入变量与容量之间的映射关系，捕获容量衰减下的长期依赖性。在不同充电倍率的电池数据集上的结果表明，该方法对不同类型电池的SOH皆可以实现高精度估计，均方根误差在1.1%以下。

关键词: 锂离子电池, 健康状态, 自编码器, 注意力机制, 双向门控循环神经网络

Abstract:

Lithium-ion batteries' state of health (SOH) is the central concern of a battery management system. An accurate evaluation of SOH can ensure that batteries operate safely and reliably. However, in practice, it is difficult to measure capacity, which makes SOH estimation difficult directly. To obtain accurate SOH, this paper proposes an attentional improved bidirectional gated recurrent unit (BiGRU)-based SOH estimation method for lithium-ion batteries. Firstly, parameters such as voltage, current, and impedance are extracted from the charge-discharge curve of the battery, and the auto encoder reduces the dimensions to extract the features and reduce the redundancy between the data. Secondly, an attention mechanism is introduced to assign weight to input variables and highlight the characteristic quantities that play a key role in SOH estimation. Finally, the BiGRU is used to learn the mapping relationship between input variables and capacity and capture long-term dependence under capacity decay. The results of the University of Maryland battery datasets with different charging rates show that the proposed method can estimate SOH with high precision for different types of batteries, with a root mean square error of less than 1.1%.

Key words: lithium-ion battery, state of health(SOH), attention mechanism, auto encoder, bidirectional gated recurrent unit

中图分类号:

TM 912

王凡, 史永胜, 刘博亲, 左玉洁, 符政, ALI Jamsher. 基于注意力改进BiGRU的锂离子电池健康状态估计[J]. 储能科学与技术, 2021, 10(6): 2326-2333.

Fan WANG, Yongsheng SHI, Boqin LIU, Yujie ZUO, Zheng FU, Jamsher ALI. Health state estimation of lithium-ion batteries based on attention augmented BiGRU[J]. Energy Storage Science and Technology, 2021, 10(6): 2326-2333.

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参考文献 21

1	熊瑞. 动力电池管理系统核心算法[M]. 北京: 机械工业出版社, 2018.
	XIONG R. Core algorithm of battery management system for EVs[M]. Beijing: China Machine Press, 2018.
2	赵鹤, 韩策, 程小露, 等. 采用阳极预锂化技术的锂离子电池高倍率老化容量衰减机理研究[J]. 储能科学与技术, 2021, 10(2): 454-461.
	ZHAO H, HAN C, CHENG X L, et al. Study on capacity attenuation mechanism of high-rate aging lithium ion batteries using anode pre-lithization technology[J]. Energy Storage Science and Technology, 2021, 10(2): 454-461.
3	张金龙, 佟微, 孙叶宁, 等. 锂电池健康状态估算方法综述[J]. 电源学报, 2017, 15(2): 128-134.
	ZHANG J L, TONG W, SUN Y N, et al. Summarize of lithium battery status of health estimation method[J]. Journal of Power Supply, 2017, 15(2): 128-134.
4	TIAN H X, QIN P L, LI K, et al. A review of the state of health for lithium-ion batteries: Research status and suggestions[J]. Journal of Cleaner Production, 2020, 261: doi: 10.1016/j.jclepro.2020. 120813.
5	KIPNESS M.1188—1996-IEEE recommended practice for maintenance, testing, and replacement of value-regulated lead-acid(VRLA) batteries for stationary applications[S]. USA: PE/ESSB-Energy Storage & Stationary Battery Committee,1996.
6	樊亚翔, 肖飞, 许杰, 等. 基于充电电压片段和核岭回归的锂离子电池SOH估计[J/OL].中国电机工程学报, 2021. https://doi.org/10.13334/j.0258-8013.pcsee.201805.
	FAN Y X, XIAO F, XU J, et al. SOH estimation of lithium-ion batteries based on charging voltage fragments and core rig regression[J/OL]. Proceedings of the CSEE, 2021. https://doi.org/10.13334/j.0258-8013.pcsee.201805.
7	SHU X, LI G, SHEN J W, et al. A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization[J]. Energy, 2020, 204: doi: 10.1016/j.energy.2020.117957.
8	任璞, 王顺利, 何明芳, 等. 基于内阻增加和容量衰减双重标定的锂电池健康状态评估[J]. 储能科学与技术, 2021, 10(2): 738-743.
	REN P, WANG S L, HE M F, et al. Health state assessment of lithium battery based on dual calibration of internal resistance increase and capacity decline[J]. Science and Technology of Energy Storage, 2021, 10(2): 738-743.
9	BI Y L, YIN Y L, CHOE S Y. Online state of health and aging parameter estimation using a physics-based life model with a particle filter[J]. Journal of Power Sources, 2020, 476: doi: 10.1016/j.jpowsour.2020.228655.
10	李鹏, 李立伟, 杨玉新. 基于IBOA-PF的锂电池健康状态预测[J]. 储能科学与技术, 2021, 10(2): 705-713.
	LI P, LI L W, YANG Y X. Prediction of Li-ion battery health state based on IBOA-PF[J]. Science and Technology of Energy Storage, 2021, 10(2): 705-713.
11	LI J, WANG D F, DENG L, et al. Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries[J]. Journal of Energy Storage, 2020, 31: doi: 10.1016/j.est.2020.101538.
12	颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948.
	YAN X W, DENG H R, GUO Q, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3937-3948.
13	张任, 胥芳, 陈教料, 等. 基于PSO-RBF神经网络的锂离子电池健康状态预测[J]. 中国机械工程, 2016, 27(21): 2975-2981.
	ZHANG R, XU F, CHEN J L, et al. Li-ion battery SOH prediction based on PSO-RBF neural network[J]. China Mechanical Engineering, 2016, 27(21): 2975-2981.
14	WU Y T, XUE Q, SHEN J W, et al. State of health estimation for lithium-ion batteries based on healthy features and long short-term memory[J]. IEEE Access, 2020, 8: 28533-28547.
15	刘伟霞, 田勋, 肖家勇, 等. 基于混合模型及LSTM的锂电池SOH与剩余寿命预测[J]. 储能科学与技术, 2021, 10(2): 689-694.
	LIU W X, TIAN X, XIAO J Y, et al. Prediction of SOH and residual life of lithium battery based on hybrid model and LSTM[J]. Energy Storage Science and Technology, 2021, 10(2): 689-694.
16	陈琳, 王惠民, 李熠婧, 等. 用新陈代谢极限学习机实现电池健康状态估算[J]. 汽车工程, 2021, 43(1): 10-18.
	CHEN L, WANG H M, LI Y J, et al. Battery state-of-health estimation by using metabolic extreme learning machine[J]. Automotive Engineering, 2021, 43(1): 10-18.
17	JIA J, LIANG J Y, SHI Y H, et al. SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators[J]. Energies, 2020, 13(2): doi: 10.3390/en13020375
18	LIU Q, KANG Y Z, QU S F, et al. An online SOH estimation method based on the fusion of improved ICA and LSTM[C]//2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Weihai, China. IEEE, 2020: 1163-1167.
19	ZHANG J C, HOU J, ZHANG Z J. Online state-of-health estimation for the lithium-ion battery based on an LSTM neural network with attention mechanism[C]//2020 Chinese Control And Decision Conference (CCDC), Hefei, China. IEEE, 2020: 1334-1339.
20	DENG Y W, YING H J, E J, et al. Feature parameter extraction and intelligent estimation of the state-of-health of lithium-ion batteries[J]. Energy, 2019, 176: 91-102.
21	LIU W, XU Y. Data-driven online health estimation of Li-ion batteries using a novel energy-based health indicator[J]. IEEE Transactions on Energy Conversion, 2020, 35(3): 1715-1718.

健康因子	电池型号
健康因子	CS36	CS37	CX37
末端电压值/V	-0.9722	-0.9729	-0.9105
内阻/Ω	-0.9836	-0.9874	-0.8044
电压变化率/(V/s)	0.7109	0.7218	0.9379
测试时间/s	0.9803	0.9721	0.8336
放电能量/(W·h)	0.9980	0.9971	0.9797
放电深度/(A·h)	0.8573	0.8625	0.8432

方法	评价指标	CS36	CS37	CX37
SVM	R_RMSE	0.036	0.034	0.030
	M_MAE	0.025	0.026	0.024
	R²	0.8097	0.8575	0.5402
BiGRU	R_RMSE	0.037	0.031	0.024
	M_MAE	0.027	0.024	0.019
	R²	0.8054	0.8819	0.6993
AE- BiGRU	R_RMSE	0.024	0.016	0.017
	M_MAE	0.020	0.014	0.013
	R²	0.9152	0.9683	0.8503
AE-AM- BiGRU	R_RMSE	0.011	0.008	0.009
	M_MAE	0.008	0.006	0.007
	R²	0.9834	0.9926	0.9583

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