摘要
准确预测新能源汽车充电过程中的荷电状态(SOC)可以提升充电效率与安全性、延长电池使用寿命以及提高驾驶体验与智能化水平,具有重要研究价值。文章基于卷积神经网络(CNN)与注意力机制和长短时记忆(LSTM)网络结合的方法,首先获取预测所需信息,划分数据集并且进行数据预处理,提高模型的训练效果和泛化能力,然后输入CNN-AttentionLSTM模型进行训练,最后依据评价指标验证该模型的有效性。研究结果表明,该模型能够从大量数据中提取到重要特征,理论上只通过实时获取充电过程中的数据,可以实现充电过程中SOC的准确预测。通过设计不同充电实验,对比充电过程中的实际SOC与预测SOC,得出CNN-Attention-LSTM模型具有可行性与良好的泛化性并且比CNN-LSTM模型性能更佳,在未来将具有广泛应用价值。
Accurately predicting the state of charge(SOC)in the charging process of new energy vehicles can improve charging efficiency and safety,extend battery life,and improve driving experience and intelligence level,which has important research value.This paper is based on the method of combining convolution neural network(CNN)with attention mechanism and long shortterm memory(LSTM)network,information needed for prediction is obtained first.The data set is divided and the data is preprocessed to improve the training effect and generalization ability of the model.Then the CNN-Attention-LSTM model is input for training,and the effectiveness of the model is verified according to the evaluation index.The research results show that the model can extract important features from a large number of data,and in theory,the SOC can be accurately predicted during the charging process only by acquiring the data during the charging process in real time.By designing different charging experiments and comparing the actual SOC and the predicted SOC in the charging process,it is concluded that the CNN-Attention-LSTM model is feasible and has good generalization and better performance than the CNN-LSTM model,which will have wide application value in the future.
作者
刘丰
高有山
黄敏
孙浩然
王爱红
任鸿
LIU Feng;GAO Youshan;HUANG Min;SUN Haoran;WANG Aihong;REN Hong(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China;New Energy Technology Division,VOYAH Automobile Technology Company Limited,Wuhan 430051,China)
出处
《汽车实用技术》
2025年第11期1-7,共7页
Automobile Applied Technology
基金
山西省重型机械电液控制及健康管理技术创新中心
山西省留学人员科技活动择优资助项目(20240021)
山西省研究生实践创新项目(2023SJ257)。
关键词
荷电状态
卷积神经网络
注意力机制
长短时记忆网络
state of charge
convolution neural network
attention mechanism
long short-term memory network
