基于三层加权堆叠模型的电动汽车剩余里程预测

doi:10.19562/j.chinasae.qcgc.2025.01.011

Abstract

Abstract:

To achieve accurate prediction of electric vehicle remaining range， a method based on a three-layer weighted stacking model for predicting remaining range of electric vehicles is proposed in this paper. By combining the maximal information coefficient and Spearman correlation coefficient as criteria for variable evaluation， the minimum redundancy maximum relevance algorithm is employed to optimize and obtain the input feature set from the candidate features. A three-layer stacking model that incorporates the original training features is then constructed， and Bayesian optimization algorithm is used to determine the weights of the base models within the stacking model. Finally， the input feature set is used to train the three-layer weighted stacking model and realize electric vehicle remaining range prediction. The results show that the proposed three-layer weighted stacking model has high prediction accuracy and， compared to other models， with stronger generalization capabilities.

Key words: electric vehicle, mRMR algorithm, Stacking model, remaining range

Qin Shi,Weilu Hou,Xiaonan Zhang,Weijiao Wu,Zejia He. Electric Vehicle Remaining Range Prediction with a Three-Layer Weighted Stacking Model[J].Automotive Engineering, 2025, 47(1): 107-116.

Figures/Tables 13

References 30

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缩写词	定义
mRMR	最小冗余最大相关算法
BOA	贝叶斯优化算法
XGBoost	极限梯度提升树模型
RF	随机森林模型
GBDT	梯度提升决策树模型
SVM	支持向量机模型
BP	BP神经网络模型
PCC	皮尔逊相关系数
VTM	方差选择
EMS	手动选择
MSE	平均均方误差
MAE	平均绝对误差
MAPE	平均绝对百分比误差
R²	决定系数

特征类型	特征名称
车辆内在特性	电池荷电状态、最低温度值、电池单体电压最高值、电压、电池单体电压最低值、车速、电流、最高温度值
驾驶员行为习惯	加速踏板行程、制动踏板状态
外部运行环境	气温、湿度

基础层	权重	泛化层	权重
XGBoost	0.450 3	Ridge	0.631 0
RF	0.369 7
GBDT	0.172 0
SVM	0.003 6	Lasso	0.369 0
BP	0.004 4	Lasso	0.369 0

模型	评价指标
模型	MSE	MAE	MAPE	R²
Stacking-A	0.456 8	0.493 2	4.34%	0.985 2
Stacking-B	0.470 2	0.501 0	4.42%	0.985 0
Stacking-C	0.524 7	0.532 6	4.68%	0.984 4
Stacking-D	0.625 6	0.577 1	5.28%	0.983 8

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Electric Vehicle Remaining Range Prediction with a Three-Layer Weighted Stacking Model

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