基于极化衰减特征与通道关注混合神经网络的锂离子电池容量在线估计

doi:10.19595/j.cnki.1000-6753.tces.241344

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (5823 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要容量是衡量电池性能的关键指标,当前容量估计存在特征实用性差、模型准确度与泛化性不足的问题。鉴于此,该文提出一种结合极化衰减特征与通道关注混合神经网络的锂离子电池容量在线估计方法。首先,利用恒压电流和弛豫电压衰减的去极化特性,提取不受充电起点影响的多维实用特征,同时引入相关系数法和主成分分析法对特征进行预处理以用于容量在线估计;其次,通过融合深度置信网络（DBN）、长短期记忆网络（LSTM）和挤压-激励（SE）机制,构建具有自适应通道关注能力的混合神经网络以提高容量估计精度;最后,利用多种工况、多种材料的电池数据,对所提的方法进行了验证。结果表明,容量估计的平均绝对百分比误差、方均根百分比误差分别在1.2%、1.5%以内,验证了该方法的准确性与有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	徐志成
	杨达
	张闯
	陈占群
	张献

关键词 ：锂离子电池, 容量在线估计, 极化衰减特征, 混合神经网络, 通道注意力

Abstract：The capacity of a lithium-ion battery is a key indicator of the state of health (SOH) and remaining useful life (RUL) of the battery, as well as the state of charge (SOC) and equalization management within the battery pack. Therefore, accurate estimation of battery capacity is crucial for the battery management system (BMS). However, in practice, the battery capacity often cannot be measured directly, and needs to be estimated indirectly by combining relevant methods and accessible parameters. Therefore, this paper extracts multidimensional features reflecting the polarization strength of the battery based on a data-driven approach, and constructs a hybrid neural network with adaptive channel focusing capability, so as to realize online accurate estimation of lithium-ion battery capacity.
First, considering that the microscopic manifestation of battery capacity decay is the increase of battery polarization internal resistance, and the constant voltage charging and voltage relaxation processes are directly related to the battery depolarization reaction, the depolarization characteristics of constant voltage current and relaxation voltage decay are utilized to extract the multidimensional practical features that are not affected by the charging starting point. Data preprocessing of the original multidimensional features is performed by correlation coefficient method and principal component analysis (PCA) to complete the screening and dimensionality reduction and fusion of the features, and finally four indirect features are identified as model inputs. Next, the squeeze-excitation (SE) module is integrated into each hidden layer of the deep belief network (DBN) to construct an enhanced DBN network with channel attention capability, and the higher-order feature information extracted from the DBN is used to perform time-series prediction using a long-and short-term memory (LSTM) network to capture the mapping relationship between the battery capacity and the input features. Finally, six sets of simulation experiments are designed for different data structures, prediction models and input features to verify the effectiveness and accuracy of the method proposed in this paper in all aspects.
The simulation results show that the method proposed in this paper can effectively improve the accuracy of capacity estimation. Under different training and prediction data structures, the mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) of capacity estimation are controlled within 1.2% and 1.5%, respectively, and the model has the ability to simultaneously estimate the battery capacity under different charging conditions with good generalization. For different prediction models, the fusion of DBN network, SE mechanism and LSTM network effectively improves the accuracy of capacity estimation, especially the SE module contributes more significantly to the stability and accuracy of the model, but the LSTM network is equally indispensable, and the synergistic effect of both of them improves the performance of the model by more than 25%. Meanwhile, compared with other advanced algorithms, the model proposed in this paper demonstrates better stability while ensuring higher estimation accuracy, which verifies the effectiveness of the model improvement strategy in this paper. Compared with the original input features, the capacity estimation accuracy is improved by about 30% by combining the complementary features of constant pressure and relaxation dual depolarization processes and data preprocessing methods, which validates the effectiveness of the work done on feature engineering in this paper.

Key words： Lithium-ion battery capacity estimation online polarization decay feature hybrid neural network channel attention

收稿日期: 2024-07-28

PACS:

TM912

基金资助:国家自然科学基金项目（52307238）、省部共建电工装备可靠性与智能化国家重点实验室优秀青年创新基金项目（EERI_OY2023007）、河北省省级科技计划（225676163GH）、河北省燕赵青年科学家项目（E2024202109）和天津市重点项目（22JCZDJC00620）资助

通讯作者: 张献男,1983年生,教授,博士生导师,研究方向为无线电能传输技术、工程电磁场与磁技术、储能系统安全管理等。E-mail: zhangxian@hebut.edu.cn

作者简介: 徐志成男,1995年生,副教授,硕士生导师,研究方向为锂离子电池高精建模与状态感知、储能系统多源监测与安全管理等。E-mail: xzc@hebut.edu.cn

引用本文:

徐志成, 杨达, 张闯, 陈占群, 张献. 基于极化衰减特征与通道关注混合神经网络的锂离子电池容量在线估计[J]. 电工技术学报, 2025, 40(17): 5683-5702. Xu Zhicheng, Yang Da, Zhang Chuang, Chen Zhanqun, Zhang Xian. Online Estimation of Lithium-Ion Battery Capacity Based on Polarization Decay Feature and Hybrid Neural Network with Channel Attention. Transactions of China Electrotechnical Society, 2025, 40(17): 5683-5702.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.241344 https://dgjsxb.ces-transaction.com/CN/Y2025/V40/I17/5683

[1] Zhang Lei, Hu Xiaosong, Wang Zhenpo, et al.Hybrid electrochemical energy storage systems: an overview for smart grid and electrified vehicle applications[J]. Renewable and Sustainable Energy Reviews, 2021, 139: 110581.
[2] 衣思彤, 刘雅浓, 马耀浥, 等. 基于贝叶斯优化-卷积神经网络-双向长短期记忆神经网络的锂电池健康状态评估[J]. 电气技术, 2024, 25(5): 1-10, 21.
Yi Sitong, Liu Yanong, Ma Yaoyi, et al.State of health assessment of lithium battery based on Bayesian optimization-convolution neural network-bi-directional long short term memory neural network[J]. Electrical Engineering, 2024, 25(5): 1-10, 21.
[3] 李英顺, 阚宏达, 郭占男, 等. 基于数据预处理和VMD-LSTM-GPR的锂离子电池剩余寿命预测[J]. 电工技术学报, 2024, 39(10): 3244-3258.
Li Yingshun, Kan Hongda, Guo Zhannan, et al.Prediction of remaining useful life of lithium-ion battery based on data preprocessing and VMD-LSTM-GPR[J]. Transactions of China Electrotech-nical Society, 2024, 39(10): 3244-3258.
[4] 戴俊彦, 夏明超, 陈奇芳. 基于双重注意力机制的电池SOH估计和RUL预测编解码模型[J]. 电力系统自动化, 2023, 47(6): 168-177.
Dai Junyan, Xia Mingchao, Chen Qifang.Encoding and decoding model of state of health estimation and remaining useful life prediction for batteries based on dual-stage attention mechanism[J]. Automation of Electric Power Systems, 2023, 47(6): 168-177.
[5] 赵靖英, 胡劲, 张雪辉, 等. 基于锂电池模型和分数阶理论的SOC-SOH联合估计[J]. 电工技术学报, 2023, 38(17): 4551-4563.
Zhao Jingying, Hu Jin, Zhang Xuehui, et al.Joint estimation of the SOC-SOH based on lithium battery model and fractional order theory[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4551-4563.
[6] 刘萍, 李泽文, 蔡雨思, 等. 基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[J]. 电工技术学报, 2024, 39(10): 3232-3243.
Liu Ping, Li Zewen, Cai Yusi, et al.Joint estimation method of SOC and SOH based on fusion of equivalent circuit model and data-driven model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243.
[7] Amir S, Gulzar M, Tarar M O, et al.Dynamic equivalent circuit model to estimate state-of-health of lithium-ion batteries[J]. IEEE Access, 2022, 10: 18279-18288.
[8] Li Chaofan, Yang Lin, Li Qiang, et al.SOH estimation method for lithium-ion batteries based on an improved equivalent circuit model via elec-trochemical impedance spectroscopy[J]. Journal of Energy Storage, 2024, 86: 111167.
[9] Xiong Rui, Li Linlin, Li Zhirun, et al.An electro-chemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy, 2018, 219: 264-275.
[10] Allam A, Onori S.Online capacity estimation for lithium-ion battery cells via an electrochemical model-based adaptive interconnected observer[J]. IEEE Transactions on Control Systems Technology, 2021, 29(4): 1636-1651.
[11] 王宁, 陈志强, 刘明义, 等. 基于模糊综合评价的锂离子电池健康状态评估[J]. 发电技术, 2022, 43(5): 784-791.
Wang Ning, Chen Zhiqiang, Liu Mingyi, et al.Health status assessment of lithium-ion battery based on fuzzy comprehensive evaluation[J]. Power Generation Technology, 2022, 43(5): 784-791.
[12] 张若可, 郭永芳, 余湘媛, 等. 基于数据驱动的锂离子电池RUL预测综述[J]. 电源学报, 2023, 21(5): 182-190.
Zhang Ruoke, Guo Yongfang, Yu Xiangyuan, et al.Review of data-driven RUL prediction for lithium-ion batteries[J]. Journal of Power Supply, 2023, 21(5): 182-190.
[13] 周才杰, 汪玉洁, 李凯铨, 等. 基于灰色关联度分析-长短期记忆神经网络的锂离子电池健康状态估计[J]. 电工技术学报, 2022, 37(23): 6065-6073.
Zhou Caijie, Wang Yujie, Li Kaiquan, et al.State of health estimation for lithium-ion battery based on gray correlation analysis and long short-term memory neural network[J]. Transactions of China Electro-technical Society, 2022, 37(23): 6065-6073.
[14] Park K, Choi Y, Choi W J, et al.LSTM-based battery remaining useful life prediction with multi-channel charging profiles[J]. IEEE Access, 2020, 8: 20786-20798.
[15] Xu Jiangtao, Qu Jie, Xu Haitao.Capacity estimation of lithium-ion batteries with automatic feature extraction and graph-enhanced LSTM[J]. Journal of Energy Storage, 2024, 85: 111131.
[16] Sesidhar D V S R, Badachi C, Green R C II. A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations[J]. Journal of Energy Storage, 2023, 72: 108420.
[17] Liu Datong, Li Lü, Song Yuchen, et al.Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions[J]. International Journal of Electrical Power & Energy Systems, 2019, 110: 48-61.
[18] Cao Mengda, Zhang Tao, Wang Jia, et al.A deep belief network approach to remaining capacity estimation for lithium-ion batteries based on charging process features[J]. Journal of Energy Storage, 2022, 48: 103825.
[19] Wang Yujie, Li Kaiquan, Peng Pei, et al.Health diagnosis for lithium-ion battery by combining partial incremental capacity and deep belief network during insufficient discharge profile[J]. IEEE Transactions on Industrial Electronics, 2023, 70(11): 11242-11250.
[20] 史永胜, 任嘉睿, 李锦, 等. 基于DeepAR与特征选择的锂离子电池在线状态估计[J]. 电源学报, 2023, 21(2): 163-171.
Shi Yongsheng, Ren Jiarui, Li Jin, et al.Online state estimation of lithium-ion batteries based on DeepAR and feature selection[J]. Journal of Power Supply, 2023, 21(2): 163-171.
[21] 顾菊平, 蒋凌, 张新松, 等. 基于特征提取的锂离子电池健康状态评估及影响因素分析[J]. 电工技术学报, 2023, 38(19): 5330-5342.
Gu Juping, Jiang Ling, Zhang Xinsong, et al.Estimation and influencing factor analysis of lithium-ion batteries state of health based on features extraction[J]. Transactions of China Electrotechnical Society, 2023, 38(19): 5330-5342.
[22] Zhang Yingda, Ma Hongyan, Wang Shuai, et al.Indirect prediction of remaining useful life for lithium-ion batteries based on improved multiple kernel extreme learning machine[J]. Journal of Energy Storage, 2023, 64: 107181.
[23] Tang Ruoli, Zhang Peng, Ning Siwen, et al.Prediction of battery SOH and RUL based on cooperative characteristics in voltage-temperature-time dimensions[J]. Journal of the Electrochemical Society, 2023, 170(6): 060535.
[24] Lin Chuanping, Xu Jun, Shi Mingjie, et al.Constant current charging time based fast state-of-health estimation for lithium-ion batteries[J]. Energy, 2022, 247: 123556.
[25] Guo Peiyao, Cheng Ze, Yang Lei.A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources, 2019, 412: 442-450.
[26] Li Xiaoyu, Wang Zhenpo, Zhang Lei, et al.State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources, 2019, 410: 106-114.
[27] Jiang Bo, Dai Haifeng, Wei Xuezhe.Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging con-dition[J]. Applied Energy, 2020, 269: 115074.
[28] Wang Zhenpo, Yuan Changgui, Li Xiaoyu.Lithium battery state-of-health estimation via differential thermal voltammetry with Gaussian process regression[J]. IEEE Transactions on Transportation Electrification, 2021, 7(1): 16-25.
[29] Zhu Jiangong, Wang Yixiu, Huang Yuan, et al.Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation[J]. Nature Communications, 2022, 13(1): 2261.
[30] Wang Wentao, Yang Kaiyi, Zhang Lisheng, et al.An end-cloud collaboration approach for online state-of-health estimation of lithium-ion batteries based on multi-feature and transformer[J]. Journal of Power Sources, 2024, 608: 234669.
[31] Zhang Hao, Gao Jingyi, Kang Le, et al.State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network[J]. Energy, 2023, 283: 128742.
[32] Zhou K Q, Qin Yan, Yuen C.Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve[J]. Journal of Energy Storage, 2024, 100: 113502.
[33] Jiao Zhipeng, Ma Jian, Zhao Xuan, et al.A methodo-logy for state of health estimation of battery using short-time working condition aging data[J]. Journal of Energy Storage, 2024, 82: 110480.
[34] Li Kaiquan, Wang Yujie, Chen Zonghai.A com-parative study of battery state-of-health estimation based on empirical mode decomposition and neural network[J]. Journal of Energy Storage, 2022, 54: 105333.
[35] Zhang Shaowen, Zhu Haiping, Wu Jun, et al.Voltage relaxation-based state-of-health estimation of lithium-ion batteries using convolutional neural networks and transfer learning[J]. Journal of Energy Storage, 2023, 73: 108579.
[36] Hou Xiaokang, Guo Xiaodong, Yuan Yupeng, et al.The state of health prediction of Li-ion batteries based on an improved extreme learning machine[J]. Journal of Energy Storage, 2023, 70: 108044.