摘要
为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。
To enhance the accuracy of state-of-health(SOH)estimation for lithium-ion batteries,this study proposes a convolutional neural network(CNN)-transformer fusion model based on polynomial feature expansion.The model leverages the powerful local feature extraction capability of CNNs and the sequence processing ability of transformers.Key health factors,highly correlated with battery capacity,such as peak values of incremental capacity curves,corresponding voltages,areas,and charging time,were extracted and expanded using polynomial features.This expansion enhances the model's ability to handle nonlinearities in the input features.Principal component analysis was employed to reduce the dimensionality of the feature space,which aided in capturing adequate data information and reduced training time.The effectiveness and accuracy of the proposed fusion algorithm were validated using open-source datasets from the National Aeronautics and Space Administration(NASA)and the University of Maryland.Comparative analyses of SOH estimation were conducted for the CNN-transformer model with and without polynomial features and for single-model algorithms.The results indicate that the SOH estimation accuracy of the proposed model,compared to the CNN-transformer model without polynomial features,improved by 38.71%,50.28%,4.71%,and 17.58%for datasets B0005,B0006,B0007,and B0018,respectively.
作者
陈媛
章思源
蔡宇晶
黄小贺
刘炎忠
CHEN Yuan;ZHANG Siyuan;CAI Yujing;HUANG Xiaohe;LIU Yanzhong(Anhui University School of Artificial Intelligence,Hefei 230601,Anhui,China)
出处
《储能科学与技术》
CAS
CSCD
北大核心
2024年第9期2995-3005,共11页
Energy Storage Science and Technology
基金
国家重点研发计划“智能电网技术与装备”专项(2023YFB2406900)
湖北省重点研发计划项目(2021BEA162)。
