A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables

LIANG Wei; LIU Xiaonan; ZHANG Zhida; YANG Zhaohui; CUI Wenqing; JIN Shangting

doi:10.19725/j.cnki.1007-2322.2023.0344

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A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables

更新时间：2025-10-22

- A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables
- Vol. 42, Issue 5, Pages: 1052-1059(2025)
- 作者机构：
  
  1. 国网天津市电力公司, 天津市南开区,300384
  2. 智能电网教育部重点实验室(天津大学), 天津市南开区,300072
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0344
  CLC： 5
- Published：2025
- 稿件说明：
移动端阅览
LIANG Wei, LIU Xiaonan, ZHANG Zhida, et al. A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables[J]. 2025, 42(5): 1052-1059.
DOI：

LIANG Wei, LIU Xiaonan, ZHANG Zhida, et al. A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables[J]. 2025, 42(5): 1052-1059. DOI： 10.19725/j.cnki.1007-2322.2023.0344.

摘要

针对电动私家车(electric private car

EPC)充电决策行为，介绍一种考虑用户个体异质性和态度潜变量的EPC随机充电决策方法。该方法首先通过分析充电决策的影响因素，并针对不同因素设计调查问卷。其次，针对态度潜变量设计问题，得到测量指标以间接量化态度潜变量，并通过问卷信度和效度分析调查数据的质量。然后，考虑影响用户个体充电决策的充电场景变量、态度潜变量以及个体社会经济属性，利用混合选择模型(hybrid choice model

HCM)建立充电效用，确定充电概率。通过调查数据估计模型参数，最后确定充电效用与3类影响因素的具体表达式。与基于混合Logit模型的预测结果相比，所提方法的预测结果精度更高，采用所提方法后误差缩小了将近14%。

Abstract

According to the charging decision behavior of electric private cars (EPCs)

a random charging decision method for EPCs is proposed with individual heterogeneity and latent attitude variables taken into account. Firstly

an analysis on the influencing factors of charging decision is conducted

and questionnaires for the different factors are designed. Secondly

to address the design problem of latent variables of attitude

a measurement index is derived to indirectly quantify latent variables of attitude. Additionally

the quality of the survey data is analyzed through an examination of questionnaire data reliability and validity. Then

considering the charging scene variables

attitude latent variables and individual socioeconomic attributes that affect individual charging decisions

the hybrid choice model (HCM) is employed to establish the charging utility

and the charging probability is subsequently determined. Through the investigation data

the model parameters are estimated

leading to the determination of specific expressions of charging utility and three types of influencing factors. Compared to the prediction results based on the mixed Logit model

the proposed method achieves higher prediction accuracy

and the error has been reduced by nearly 14% when adopting the proposed method.

关键词

Keywords

references

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