采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计

赵扬; 耿莉敏; 胡循泉; 胡兵; 巫春玲; 张文博; 山世玉; 陈昊

doi:10.7652/xjtuxb202411003

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采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计

专题电动车辆先进电池管理 | 更新时间：2025-07-25

- 采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计
- State of Health Estimation of Lithium-Ion Batteries Using a Gramian Angle Field-Convolutional Neural Network-Temporal Convolution Network Hybrid Model
- 西安交通大学学报 2024年58卷第11期页码：27-38
- 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：
  
  : 2024-04-30。作者简介: 赵扬(1999—),男,博士生
  耿莉敏(通信作者),女,教授,博士生导师。基金项目: 陕西省重点研发计划资助项目(2022GY-1930)
- 基金信息：
  
  : 2024-04-30。作者简介: 赵扬(1999—),男,博士生;耿莉敏(通信作者),女,教授,博士生导师。基金项目: 陕西省重点研发计划资助项目(2022GY-1930);西安市重点产业链技术攻关一般资助项目(24ZDCYJSGG0048);长安大学中央高校基本科研业务费资助项目(300102384201)。
- DOI：10.7652/xjtuxb202411003
  中图分类号： TM912.8
- 网络首发：2024-11-10，
  
  纸质出版：2024
- 稿件说明：
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赵扬, 耿莉敏, 胡循泉, 等. 采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计[J]. 西安交通大学学报, 2024,58(11):27-38.

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.
赵扬, 耿莉敏, 胡循泉, 等. 采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计[J]. 西安交通大学学报, 2024,58(11):27-38. DOI： 10.7652/xjtuxb202411003.

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

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