Artificial Rabbits Optimization for Refining Extra Trees Regression in Accurate Electric Vehicle Range Prediction

IF 2.5 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Sinem Bozkurt Keser
{"title":"Artificial Rabbits Optimization for Refining Extra Trees Regression in Accurate Electric Vehicle Range Prediction","authors":"Sinem Bozkurt Keser","doi":"10.1049/itr2.70085","DOIUrl":null,"url":null,"abstract":"<p>Electric vehicles (EVs) provide significant advantages for sustainable transportation, such as reduced energy consumption, the ability to integrate with renewable energy sources, and emission reductions. Nevertheless, range anxiety, high battery costs, and long charging times limit the adoption of EVs. Accurately estimating driving range is one of the solutions to overcome these limitations. This study proposes a method that combines an extra tree regressor (ETR) model and an artificial rabbit optimization (ARO) algorithm to predict the driving distance using a comprehensive dataset for EVs. In our experiments, we compared ARO with well-known hyperparameter optimization methods such as grid search (GS) and random search (RS), and tested the models across multiple train and test splits. Besides using the complete feature set, we applied recursive feature elimination (RFE) to select an informative subset and re-evaluated all methods. With all features, the best configuration of the proposed algorithm achieved an R-squared (R<sup>2</sup>) of 0.84, a root mean square error (RMSE) of 14.38, a mean absolute error (MAE) of 7.70, and a mean squared error (MSE) of 220.12. Using the selected subset of seven features, the proposed model reached an R<sup>2</sup> of 0.84, with an RMSE of 14.88, an MAE of 6.75, and an MSE of 221.53. Finally, the contribution of each feature's to the predicted driving range was analysed using shapely additive explanations (SHAP). The findings of the study emphasize the value of integrating machine learning (ML) models and hyperparameter search methods into electric vehicle range-estimation systems to improve driver confidence and support sustainable transportation.This study advances the current understanding of range prediction and contributes to reducing range anxiety, thereby supporting extensive adoption of EVs. The findings of the study indicate that the integration of ML approaches in the range estimation of EVs can play a critical role in increasing driver confidence and supporting sustainable transportation. This study contributes to the existing knowledge in the field of range estimation and is an important step toward the broader adoption of EVs.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70085","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70085","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Electric vehicles (EVs) provide significant advantages for sustainable transportation, such as reduced energy consumption, the ability to integrate with renewable energy sources, and emission reductions. Nevertheless, range anxiety, high battery costs, and long charging times limit the adoption of EVs. Accurately estimating driving range is one of the solutions to overcome these limitations. This study proposes a method that combines an extra tree regressor (ETR) model and an artificial rabbit optimization (ARO) algorithm to predict the driving distance using a comprehensive dataset for EVs. In our experiments, we compared ARO with well-known hyperparameter optimization methods such as grid search (GS) and random search (RS), and tested the models across multiple train and test splits. Besides using the complete feature set, we applied recursive feature elimination (RFE) to select an informative subset and re-evaluated all methods. With all features, the best configuration of the proposed algorithm achieved an R-squared (R2) of 0.84, a root mean square error (RMSE) of 14.38, a mean absolute error (MAE) of 7.70, and a mean squared error (MSE) of 220.12. Using the selected subset of seven features, the proposed model reached an R2 of 0.84, with an RMSE of 14.88, an MAE of 6.75, and an MSE of 221.53. Finally, the contribution of each feature's to the predicted driving range was analysed using shapely additive explanations (SHAP). The findings of the study emphasize the value of integrating machine learning (ML) models and hyperparameter search methods into electric vehicle range-estimation systems to improve driver confidence and support sustainable transportation.This study advances the current understanding of range prediction and contributes to reducing range anxiety, thereby supporting extensive adoption of EVs. The findings of the study indicate that the integration of ML approaches in the range estimation of EVs can play a critical role in increasing driver confidence and supporting sustainable transportation. This study contributes to the existing knowledge in the field of range estimation and is an important step toward the broader adoption of EVs.

Abstract Image

Abstract Image

Abstract Image

基于人工兔子优化的额外树回归在电动汽车里程预测中的应用
电动汽车(ev)为可持续交通提供了显著的优势,例如降低能源消耗、与可再生能源整合的能力以及减少排放。然而,里程焦虑、高电池成本和长充电时间限制了电动汽车的普及。准确估计行驶里程是克服这些限制的方法之一。本研究提出了一种结合额外树回归(ETR)模型和人工兔子优化(ARO)算法的方法,利用综合数据集预测电动汽车的行驶距离。在我们的实验中,我们将ARO与众所周知的超参数优化方法(如网格搜索(GS)和随机搜索(RS))进行了比较,并在多个列车和测试分割中对模型进行了测试。除了使用完整的特征集外,我们还使用递归特征消除(RFE)来选择一个信息子集并重新评估所有方法。综合所有特征,该算法的最佳配置的r平方(R2)为0.84,均方根误差(RMSE)为14.38,平均绝对误差(MAE)为7.70,均方误差(MSE)为220.12。使用选取的7个特征子集,该模型的R2为0.84,RMSE为14.88,MAE为6.75,MSE为221.53。最后,利用形状加性解释(SHAP)分析了各特征对预测驾驶里程的贡献。研究结果强调了将机器学习(ML)模型和超参数搜索方法集成到电动汽车里程估计系统中的价值,以提高驾驶员的信心并支持可持续交通。该研究促进了目前对里程预测的理解,有助于减少里程焦虑,从而支持电动汽车的广泛采用。研究结果表明,将机器学习方法整合到电动汽车的里程估计中,可以在提高驾驶员信心和支持可持续交通方面发挥关键作用。该研究对现有的里程估计领域的知识做出了贡献,是电动汽车更广泛采用的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信