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基于特征选择和数据增强的电池荷电状态预测 被引量:1

Battery State of Charge Prediction Based on Feature Selection and Data Augmentation
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摘要 现有基于神经网络的电池荷电状态(state of charge,SOC)预测研究大多把重点放在模型结构和相关参数的优化上,却忽略了训练数据的重要作用.针对该问题,文中提出了一种基于特征选择和数据增强的电池SOC预测方法.首先,方法根据原始电池充放电数据进行特征工程,并使用排列重要性(permutation importance,PI)方法选出对模型预测最有帮助的7个特征;其次,通过加入高斯噪声来扩大训练数据样本总量,达到数据增强的目的.实验使用双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)作为预测模型,使用Panasonic 18650PF数据集作为训练数据.使用标准Bi-LSTM进行预测时,平均绝对误差(mean absolute error,MAE)和最大误差(max error,MaxE)分别为0.65%和3.92%,而在进行特征选择和数据增强后,模型预测的MAE和MaxE分别为0.47%和2.62%,表明PI特征工程与高斯数据增强方法可以进一步提升电池荷电状态预测模型的精度. The existing research on battery state of charge(SOC)prediction based on neural networks mostly focuses on the optimization of model structure and related parameters,ignoring the important role of training data.A battery SOC prediction method based on feature selection and data augmentation is proposed to overcome this problem.Specifically,feature engineering is carried out according to the original battery charge and discharge data,and seven features that are most helpful to model prediction are selected by the permutation importance(PI)method;then,Gaussian noise is added to expand the total number of training data samples and thereby achieve the purpose of data augmentation.In the experiment,a bidirectional long short-term memory(Bi-LSTM)network is used as the prediction model,and the Panasonic 18650PF dataset is adopted as the training data.When the standard Bi-LSTM model is employed for prediction,the mean absolute error(MAE)and the maximum error(MaxE)are 0.65%and 3.92%respectively.After feature selection and data augmentation,the MAE and MaxE of model prediction are 0.47%and 2.62%respectively,indicating that the accuracy of the battery SOC prediction model can be further improved by PI feature engineering and the Gaussian data augmentation method.
作者 朱月凡 蒋国平 高辉 李炜卓 归耀城 ZHU Yue-Fan;JIANG Guo-Ping;GAO Hui;LI Wei-Zhuo;GUI Yao-Cheng(College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Modern Posts,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处 《计算机系统应用》 2023年第2期45-54,共10页 Computer Systems & Applications
基金 国家自然科学基金(52077107)。
关键词 电池荷电状态预测 双向长短时记忆网络 特征选择 数据增强 高斯噪声 state-of-charge prediction bidirectional long short-term memory(Bi-LSTM)network feature selection data augmentation Gaussian noise
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