Accelerating Sustainable Mobility - Empirical Insights into Machine Learning-Based Electric Vehicle Price Prediction

Aug 5, 2026

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This thesis identifies key variables driving pricing differences among electric vehicle manufacturers. Using advanced machine learning methods, it evaluates the influence of fifteen features on electric vehicle price differentials. Models are trained on a comprehensive dataset and validated on an independent sample to determine optimal precision and accuracy. The research addresses a relatively underexplored topic, offering value for manufacturers’ pricing decisions and consumers’ access to appropriately valued EV options.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages104

Reading Time4 h 22 min

AccessFree to download

Introduction

Overview

Landscape of EV

The Pricing Conundrum

Research Objectives

Literature Review

Research within the automotive domain

Resources

Coding Language, Libraries & Software

Coding Language and Libraries

Methodology

The Integration of Machine Learning in the EV Industry

Key concepts to consider

Data

Dataset Description

Data Extraction

Avoiding Website Blocks

Data Cleaning

Accelerating Sustainable Mobility: Empirical Insights into Machine Learning-Based Electric Vehicle Price Prediction Reece Cavan Moraes 152022011 Dissertation written under the supervision of professor Pedro Afonso Fernandes Dissertation submitted in partial fulfilment of requirements for the MSc in Business Analytics, at the Universidade Católica Portuguesa, on December 2023. Accelerating Sustainable Mobility: Empirical Insights into Machine Learning-Based Electric Vehicle Price Prediction Reece Cavan Moraes Abstract This thesis endeavours to discern the primary variables influencing pricing differentials among electric vehicle manufacturers. Leveraging advanced machine learning algorithms, the study scrutinizes the impact of fifteen distinct features that potentially contribute to pricing variations. The models are meticulously trained and constructed on a comprehensive dataset, subsequently tested on an independent sample to ascertain optimal precision and accuracy metrics. While too many studies have successfully implemented this innovative methodology within the domain of internal combustion vehicles, its application to the electric vehicle domain remains a nascent area of inquiry. This pioneering approach, coupled with the evolving landscape of machine learning, holds the promise of delivering dual benefits: affording companies the ability to establish an appropriate pricing spectrum for their vehicles, and providing consumers with access to value-added electric vehicles. The study incorporates a diverse set of regression techniques, including Multiple Linear Regression, Support Vector Machine, Random Forest Regression, Decision Trees and XGBoost Regression. The target variable under consideration is price, characterized by its continuous nature. Consequently, the utilization and implementation of regression methodologies exclusively aligns with the nature of the output variable. In the domain of electric vehicles (EVs), research focused on employing machine learning for pricing determination has been relatively limited in its momentum and significance within the automotive industry. Despite the anticipated proliferation of these battery-powered vehicles driven by global imperatives for carbon neutrality, their widespread adoption is still in its early stages in the 21st century. Keywords: Machine learning; Electric Vehicles; Multiple Linear Regression; Support Vector Machine; Random Forest Regression; Decision Trees; XGBoost Regression. Resumo Esta tese procura identificar as principais vari´aveis que influenciam os diferenciais de pre¸co no consumidor final entre ve´ıculos el´etricos. Recorrendo a modelos avan¸cados de aprendizagem autom´atica, este estudo analisa o impacto nesse pre¸code quinze carater´ısticas distintas dos referidos ve´ıculos. Os modelos foram meticulosamente estimados com recurso a uma base de dados exaustiva e testados numa sub-amostra distinta de modo a otimizar a precis˜ao e a exatid˜ao das predi¸c˜oes. Estametodologia tem vindo a ser aplicada ao estudo dos pre¸cos dos ve´ıculos tradicionais com motor de combust˜ao interna, mas n˜ao tanto no caso dos ve´ıculos el´etricos. Quando conciliada com a evolu¸c˜ao dos m´etodos de aprendizagem autom´atica, esta abordagem pode, por um lado, suportar o processo de fixa¸c˜ao de pre¸cos por partedos fabricantes e, por outro lado, informar os consumidores finais, facilitando o respetivo processo de compra de ve´ıculos el´etricos. Este estudo incorpora um conjunto de t´ecnicas de regress˜ao, nomeadamente, regress˜ao linear m´ultipla, vetores de suporte, florestas aleat´orias, ´arvores de decis˜ao e boosting. A vari´avel dependente ´eo pre¸co no consumidor final, caraterizando-se pelo seu car´ater num´erico e continuo. Assim, a ado¸c˜ao de t´ecnicas de regress˜ao ´e coerente com a natureza da vari´avelexplicada. No ˆambito dos ve´ıculos el´etricos, a investiga¸c˜ao com recurso a m´etodos de aprendizagem autom´atica ´e ainda muito l

FAQs

What problem does the thesis address?

The thesis focuses on uncovering the primary variables that explain pricing differences among electric vehicle manufacturers.

Which machine learning methods are used to predict electric vehicle prices?

It applies several regression techniques, including Multiple Linear Regression, Support Vector Machine, Random Forest Regression, Decision Trees, and XGBoost Regression.

How is model performance evaluated in the study?

Models are trained on a comprehensive dataset and tested on an independent sample, using precision and accuracy metrics to assess optimal performance.

Aug 5, 2026

Upload by Violet