面向动力电池SOC估计的时间卷积优化网络  

Temporal convolutional optimization network for SOC estimation of power batteries

作　　者：王娟[1] 叶永钢 武明虎[1] 张凡 曹烨 张则涛 WANG Juan;YE Yonggang;WU Minghu;ZHANG Fan;CAO Ye;ZHANG Zetao(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学电气与电子工程学院,武汉430068

出　　处：《重庆理工大学学报（自然科学）》2024年第6期39-46,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(62006073);湖北省中央引导地方科技发展专项项目(2023GEA027);湖北省自然科学基金项目(2022CFA007);湖北省科技项目(2022BEC017);湖北工业大学绿色工业科技引领计划项目(2022020801020267)。

摘　　要：在电动汽车的实际驾驶场景中,由于复杂多变的运行条件和动力电池的非线性,对电池荷电状态(SOC)的准确估计存在较大的误差,从而造成车主的里程焦虑。针对上述问题,提出了一种时间卷积优化网络(TCON)方法,用于实时估计动力电池SOC。首先,建立无归一化的时间卷积网络(TCN)模型,通过并行计算提取时序信息,具有参数少、精度高的优点。其次,为了解决TCN输出波动性较强的问题,设计了时间优化模块(TOM),该模块通过生成时序优化权值,对TCN输出进行优化,有效抑制数据噪声,进一步提高了预测精度。最后,使用电动汽车实时运行数据集进行验证。实验结果表明:文中所提方法与TCN相比,在仅增加5.8%的参数量的前提下减少了18.3%的误差;SOC估计的平均绝对误差小于1%,均方根误差小于2%,为SOC提供了更准确的估计,在一定程度上缓解驾驶员的里程焦虑。Electric vehicles,as the main development direction of new energy vehicles,are entering a new stage of accelerated development.However,in the actual operating conditions of electric vehicles,factors such as driving mode,road conditions,aging of battery lines,and environmental temperature lead to significant errors in power batteries state of charge(SOC)estimation.This can cause drivers to experience range anxiety,ultimately affecting the sales of electric vehicles.Therefore,it is crucial to embed an accurate algorithm for estimating SOC in the battery management system.At present,most of the research on SOC estimation are based on individual battery cells,while data of power batteries is multi-scale data composed of multiple battery cells.The accuracy of power batteries SOC estimation is not only affected by the internal characteristics of the battery,but also by the connection method,driving conditions,and usage mode.Then,battery data has temporal characteristics,and battery capacity will degrade over time.This poses a challenge for traditional machine learning algorithms or ordinary neural networks in capturing temporal features.Although existing studies employ RNNs,such as LSTM,for processing time series data,the serial nature of RNNs restricts their ability to process only one time step at a time.The computing resources and time will significantly increase with the increase of time steps.In addition,some studies use hybrid neural networks,such as CNN-LSTM.Because relying solely on CNN for network modeling is not ideal,it is often set at the forefront of the neural network for feature extraction.However,this method disrupts the temporal structure of the data,causing LSTM to lose the significance of extracting temporal features.To address these challenges,this paper proposes a temporal convolutional optimization network(TCON)for real-time SOC of power batteries.Firstly,a non-normalized temporal convolutional network(TCN)model is established.It can perform parallel computation to extract temporal information,wit

关 键 词：动力电池 SOC估计 里程焦虑 时间卷积优化网络 

分 类 号：TK011[动力工程及工程热物理]