储能科学与技术2025，Vol.14Issue(11)：4330-4345,16.DOI:10.19799/j.cnki.2095-4239.2025.0490
基于深度特征融合模型的电动汽车不同模式充电时长自适应预测
Adaptive prediction of charging duration for different modes of electric vehicles based on a deep feature fusion model
摘要
Abstract
Accurately predicting the charging time of lithium batteries can improve charging efficiency and optimize resource allocation-an important factor for developing electric vehicles.This study proposes an adaptive prediction method for electric vehicle charging duration under different modes based on a deep feature fusion model.First,vehicle operation data collected by a new energy vehicle monitoring platform are cleaned and segmented,and charging modes are classified based on voltage,current,and average power to form fast-and slow-charging datasets.Next,based on the charging dataset,principal component analysis is applied to extract model input features.Then,a multilayer perceptron(MLP)model is constructed by integrating the attention mechanism to obtain intermediate features through a nonlinear mapping of input features.As features directly extracted from raw data cannot fully capture the complex relationship with charging duration,a random forest(RF)model is introduced to construct leaf-node rule features based on the internal splitting principle of RF,exploring implicit feature information.A"rule layer"is subsequently established in the MLP to fuse intermediate and rule features,achieving structural fusion of the two models.Finally,the prediction results of the attention MLP-RF fusion model are validated,demonstrating an average absolute error of 4.25 and 6.68 minutes for fast-and slow-charging modes,respectively,with an average absolute percentage error of 4.33%and 3.86%,indicating accurate prediction of different electric vehicle charging durations.Moreover,this method maintains high accuracy in predicting charging duration under battery aging and short-term charging conditions,with an average prediction error of less than 2 min.Overall,the fusion model demonstrates strong predictive performance and generalization capabilities.关键词
锂离子电池/数据驱动/充电模式/充电时长/特征融合Key words
lithium-ion battery/data driven/charge mode/charging duration/feature fusion分类
动力与电气工程引用本文复制引用
韦介洋,申江卫,陈峥,魏福星,夏雪磊,刘永刚..基于深度特征融合模型的电动汽车不同模式充电时长自适应预测[J].储能科学与技术,2025,14(11):4330-4345,16.基金项目
云南省基础研究计划项目(202301AT070423) （202301AT070423）
汽车零部件先进制造技术教育部重点实验室开放课题基金(2023KLMT02). （2023KLMT02）
