{"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.
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