State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model

ZHAO Yang; GENG Limin; HU Xunquan; HU Bing; WU Chunling; ZHANG Wenbo; SHAN Shiyu; CHEN Hao

doi:10.7652/xjtuxb202411003

您当前的位置：

首页 >

文章列表页 >

State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model

更新时间：2025-07-25

- State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model
- Vol. 58, Issue 11, Pages: 27-38(2024)
- 作者机构：
  
  1. 长安大学陕西省交通新能源开发、应用与汽车节能重点实验室,西安,710064
  2. 新疆工程学院控制工程学院,乌鲁木齐,830023
  3. 陕西重型汽车有限公司汽车工程研究院,西安,710200
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202411003
  CLC： TM912.8
- Online First：10 November 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
ZHAO Yang, GENG Limin, HU Xunquan, et al. State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model[J]. 2024, 58(11): 27-38.
DOI：

ZHAO Yang, GENG Limin, HU Xunquan, et al. State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model[J]. 2024, 58(11): 27-38. DOI： 10.7652/xjtuxb202411003.

摘要

针对现有电池健康状态(SOH)估计存在估计精度低、时序特征捕捉不足的问题

提出了一种格拉姆角场-卷积神经网络-时序卷积网络(GAF-CNN-TCN)混合模型。利用GAF算法将不同长度的容量增量(IC)曲线转换成图像数据

并采用卷积神经网络从中提取特征; 提出一种特征融合网络

将二维卷积神经网络从图像中提取的图片特征与一维卷积神经网络从IC序列中提取的时序特征进行融合; 将提取的综合特征输入时序卷积网络模型中进行训练

实现了SOH的准确估计。利用美国国家航空航天局和牛津大学的锂离子电池数据集进行模型验证

结果表明:相较于长短期记忆(LSTM)模型

GAF-CNN-TCN混合模型输出的SOH与真实SOH之间的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)分别降低了85.65%、86.12%、84.0%; 相较于CNN-LSTM模型

所提模型的MAE、MAPE和RMSE分别降低了83.24%、83.75%、82.27%; 相较于TCN模型

所提模型的MAE、MAPE和RMSE分别降低了76.92%、77.19%、76.01%。

Abstract

To address the issues of low estimation accuracy and insufficient capture of time series features in the existing battery state of health(SOH)estimation

a Gramian angle field-convolutional neural network-temporal convolution network(GAF-CNN-TCN)hybrid model is proposed. The model converts incremental capacity(IC)curves of varying lengths into image data using the GAF algorithm and extracts features from them through a convolutional neural network. Additionally

a feature fusion network is introduced to integrate the image features extracted from the image by a two-dimensional convolutional neural network with the temporal features extracted from the IC sequence by a one-dimensional convolutional neural network. The integrated features are fed into the temporal convolutional network model for training

leading to precise SOH estimation. Validation of the model is conducted using lithium-ion battery data sets from NASA and University of Oxford. The results show that in comparison to the long short-term memory(LSTM)model

the GAF-CNN-TCN hybrid model reduces the mean absolute error(MAE)

mean absolute percentage error(MAPE)

and root mean square error(RMSE)between the estimated SOH and the true SOH by 85.65%

86.12%

and 84.0%

respectively. Similarly

compared to the CNN-LSTM model

the reductions in MAE

MAPE

and RMSE are 83.24%

83.75%

and 82.27%

respectively. Furthermore

compared to the TCN model

the reductions in MAE

MAPE

and RMSE are 76.92%

77.19%

and 76.01%

respectively.

关键词

Keywords

references

赵轩, 李美莹, 余强, 等. 电动汽车动力锂电池状态估计综述 [J]. 中国公路学报, 2023, 36(6): 254-283.

ZHAO Xuan, LI Meiying, YU Qiang, et al. State estimation of power lithium batteries for electric vehicles: a review [J]. China Journal of Highway and Transport, 2023, 36(6): 254-283.

ZHANG Lei, HU Xiaosong, WANG Zhenpo, et al. A review of supercapacitor modeling, estimation, and applications: a control/management perspective [J]. Renewable and Sustainable Energy Reviews, 2018, 81(Part 2): 1868-1878.

赵云飞, 徐俊, 王霄, 等. 双自适应衰减卡尔曼滤波锂电池荷电状态估计 [J]. 西安交通大学学报, 2018, 52(12): 99-105.

ZHAO Yunfei, XU Jun, WANG Xiao, et al. An estimation method for state of charge of lithium-ion batteries using dual adaptive fading extended Kalman filter [J]. Journal of Xi'an Jiaotong University, 2018, 52(12): 99-105.

CHE Yunhong, HU Xiaosong, LIN Xianke, et al. Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects [J]. Energy Environmental Science, 2023, 16(2): 338-371.

LIN Chuanping, XU Jun, MEI Xuesong. Improving state-of-health estimation for lithium-ion batteries via unlabeled charging data [J]. Energy Storage Materials, 2023, 54: 85-97.

XU Jun, MEI Xuesong, WANG Xiao, et al. A relative state of health estimation method based on wavelet analysis for lithium-ion battery cells [J]. IEEE Transactions on Industrial Electronics, 2021, 68(8): 6973-6981.

DENG Zhongwei, HU Xiaosong, LI Penghua, et al. Data-driven battery state of health estimation based on random partial charging data [J]. IEEE Transactions on Power Electronics, 2022, 37(5): 5021-5031.

HU Xiaosong, CHE Yunhong, LIN Xianke, et al. Battery health prediction using fusion-based feature selection and machine learning [J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 382-398.

HU Xiaosong, XU Le, LIN Xianke, et al. Battery lifetime prognostics [J]. Joule, 2020, 4(2): 310-346.

SHI Mingjie, XU Jun, LIN Chuanping, et al. A fast state-of-health estimation method using single linear feature for lithium-ion batteries [J]. Energy, 2022, 256: 124652.

赵云飞, 徐俊, 王海涛, 等. 采用Morlet小波的锂电池相对健康状态估计 [J]. 西安交通大学学报, 2019, 53(12): 97-103.

ZHAO Yunfei, XU Jun, WANG Haitao, et al. An estimation method of relative state-of-health for lithium-ion batteries using Morlet wavelet [J]. Journal of Xi'an Jiaotong University, 2019, 53(12): 97-103.

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.

XIONG Rui, TIAN Jinpeng, MU Hao, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries [J]. Applied Energy, 2017, 207: 372-383.

ZHU Feng, FU Jingqi. A novel state-of-health estimation for lithium-ion battery via unscented Kalman filter and improved unscented particle filter [J]. IEEE Sensors Journal, 2021, 21(22): 25449-25456.

ZHOU Yanting, WANG Yanan, WANG Kai, et al. Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors [J]. Applied Energy, 2020, 260: 114169.

CHOI Y, RYU S, PARK K, et al. Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles [J]. IEEE Access, 2019, 7: 75143-75152.

CHENG Gong, WANG Xinzhi, HE Yurong. Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network [J]. Energy, 2021, 232: 121022.

刘少卿, 李帅, 苗建国, 等. 基于TCN-BiGRU的锂离子电池健康状态评估 [J]. 电子测量技术, 2023, 46(23): 68-76.

LIU Shaoqing, LI Shuai, MIAO Jianguo, et al. Lithium-ion battery state of health estimation based on TCN-BiGRU [J]. Electronic Measurement Technology, 2023, 46(23): 68-76.

吴晓丹, 范波, 王建祥, 等. 基于VMD-TCN-Attention的锂电池寿命预测 [J]. 电源技术, 2023, 47(10): 1319-1325.

WU Xiaodan, FAN Bo, WANG Jianxiang, et al. Life prediction of lithium battery based on VMD-TCN-Attention [J]. Chinese Journal of Power Sources, 2023, 47(10): 1319-1325.

GOEBEL K, SAHA B, SAXENA A, et al. Prognostics in battery health management [J]. IEEE Instrumentation Measurement Magazine, 2008, 11(4): 33-40.

BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries [D]. Oxford, USA: University of Oxford, 2017.

PASTOR-FERNÁNDEZ C, UDDIN K, CHOUCHELAMANE G H, et al. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems [J]. Journal of Power Sources, 2017, 360: 301-318.

XU Huanwei, WU Lingfeng, XIONG Shizhe, et al. An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries [J]. Energy, 2023, 276: 127585.

WANG Zhiguang, OATES T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks [C]//2015, Association for the Advancement of Artificial Intelligence. Washington, DC, USA: AAAI, 2015: 40-46.

LIN Mingqiang, WU Denggao, MENG Jinhao, et al. A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries [J]. Journal of Power Sources, 2022, 518: 230774.

XU Peng, HUANG Yuan, RAN Wenwen, et al. State of health estimation of Lib based on discharge section with multi-model combined [J]. Heliyon, 2024, 10(4): e25808.

TIAN Aina, CHEN Zhe, PAN Zhuangzhuang, et al. Li-ion battery state of health estimation based on short random charging segment and improved long short-term memory [J]. IET Signal Processing, 2023, 2023(1): 8839034.

YANG Zhanshe, WANG Yunhao, KONG Chenzai. Remaining useful life prediction of lithium-ion batteries based on a mixture of ensemble empirical mode decomposition and GWO-SVR model [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11.

张孝远, 张金浩, 蒋亚俊. 基于改进TCN模型的动力电池健康状态评估 [J]. 储能科学与技术, 2022, 11(5): 1617-1626.

ZHANG Xiaoyuan, ZHANG Jinhao, JIANG Yajun. Power battery health evaluation based on improved TCN model [J].Energy Storage Science and Technology, 2022, 11(5): 1617-1626.

毛百海, 覃吴, 肖显斌, 等. 基于LSTM GRU-Attention多联合模型的锂离子电池SOH估计 [J]. 储能科学与技术, 2023, 12(11): 3519-3527.MAO Baihai, QIN Wu, XIAO Xianbin, et al. SOH estimation of lithium-ion batteries based on LSTM GRU-Attention multijoint model [J]. Energy Storage Science and Technology, 2023, 12(11): 3519-3527.

宁雪峰, 石正禄, 许加柱. 基于健康因子和PSO-LSTM的锂离子电池健康状态估计 [EB/OL].(2023-11-24)[2024-02-05]. https://link.cnki.net/urlid/ 12.1420.TM.20231123.1426.002.

Ning Xuefeng, SHI Zhenglu, XU Jiazhu. State of health estimation of lithium-ion batteries based on health factors and PSO-LSTM [EB/OL].(2023-11-24)[2024-02-05]. https://link.cnki.net/urlid/ 12.1420.TM.20231123.1426.002.

张梦迪, 刘洋, 陈健, 等. 基于 ISSA-GPR 的锂离子电池健康状态估计 [EB/OL].(2023-12-19)[2024-03-10] https://link.cnki.net/urlid/12.1420.TM.20231219.1345.010.

ZHANG Mengdi, LIU Yang, CHEN Jian, et al. Estimation of health state of lithium-ion battery based on ISSA-GPR [EB/OL].(2023-12-19)[2024-03-10]. https://link.cnki.net/urlid/12.1420.TM.20231219.1345.010.

PENG Simin, SUN Yunxiang, LIU Dandan, et al. State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network [J]. Energy, 2023, 282: 128956.

邓鸿枥, 吴松荣, 刘齐, 等. 基于改进高斯过程回归的锂离子电池健康状态估计 [EB/OL].(2024-04-26)[2024-04-28]. https://link.cnki.net/urlid/12.1420.TM.20240426.0915.008.

DENG Hongli, WU Songrong, LIU Qi, et al. State-of-health estimation of lithium-ion batteries based on improved Gaussian process regression [EB/OL].(2024-04-26)[2024-04-28]. https://link.cnki.net/urlid/12.1420.TM.20240426.0915.008.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Mechanism,Application,and Prospects of Flash Joule Heating Technology in Recycling and Regeneration of Spent Lithium-Ion Batteries

Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model

Cooling Performance Analysis and Structural Optimization of Concentric Structure Liquid Cooling Plate for Lithium-Ion Batteries

A Fault Location Method for Hybrid Three-Terminal High Voltage Direct Current Transmission Lines

Related Author

WANG Li

QI Man

REN Shuyang

ZHAO Yawen

SUN Ruyu

LÜ Yongli

LAN Shenyu

YU Ning

Related Institution

Shengli Oilfield Branch, China Petroleum & Chemical Corporation, Dongying, Shandong 257000, China

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

College of Mechanical Engineering, Liaoning Technical University

Nuclear Industry Research Institute

Xi'an Key Laboratory of Advanced Transport Power Machinery, Chang'an University

⁰