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Predictive Model for Battery Electri...

Pi, Lijuan.

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Predictive Model for Battery Electric Vehicle Range in the U.S. Market.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive Model for Battery Electric Vehicle Range in the U.S. Market./
Author:	Pi, Lijuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	100 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30815138
ISBN:	9798381184624

Predictive Model for Battery Electric Vehicle Range in the U.S. Market.
Pi, Lijuan.

Predictive Model for Battery Electric Vehicle Range in the U.S. Market. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 100 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2024.

This item must not be sold to any third party vendors.

Predicting the suitable driving range for battery electric vehicles (BEVs) that meets regional consumer preferences is a challenge for BEV manufacturers. The purpose of this praxis is to identify key predictive factors and develop a multiple linear regression model to assist BEV manufacturers in determining the suitable BEV range for regional markets in the United States. Significant predictor variables identified for this model include average personal income, household density, density of Level 3 direct current (DC) chargers, average yearly temperature, and state. These variables contribute to a multiple linear regression model that explains 88.05% of the variance in the average BEV range in Core-Based Metropolitan Statistical Areas (CBSAs). With an R-square value exceeding the target of 70%, this model demonstrates the feasibility of using regression analysis to predict average BEV range in U.S. metropolitan areas. The model's reliability is further reinforced by a K-fold cross-validation R-square value of 85.16%. In future research, enhancements to the model's accuracy could be achieved by incorporating factors such as clean energy policy incentives and subsidies, which significantly influence BEV adoption. Additionally, the regression model can be expanded to include more metropolitan areas as new BEV registration data becomes available through the Open Vehicle Registration Initiative (Atlas Public Policy, 2022).

ISBN: 9798381184624Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Battery electric vehicle line Planning

Predictive Model for Battery Electric Vehicle Range in the U.S. Market.
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245 1 0 $a Predictive Model for Battery Electric Vehicle Range in the U.S. Market.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 100 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Bierce, Jonathan.
502 $a Thesis (D.Engr.)--The George Washington University, 2024.
506 $a This item must not be sold to any third party vendors.
520 $a Predicting the suitable driving range for battery electric vehicles (BEVs) that meets regional consumer preferences is a challenge for BEV manufacturers. The purpose of this praxis is to identify key predictive factors and develop a multiple linear regression model to assist BEV manufacturers in determining the suitable BEV range for regional markets in the United States. Significant predictor variables identified for this model include average personal income, household density, density of Level 3 direct current (DC) chargers, average yearly temperature, and state. These variables contribute to a multiple linear regression model that explains 88.05% of the variance in the average BEV range in Core-Based Metropolitan Statistical Areas (CBSAs). With an R-square value exceeding the target of 70%, this model demonstrates the feasibility of using regression analysis to predict average BEV range in U.S. metropolitan areas. The model's reliability is further reinforced by a K-fold cross-validation R-square value of 85.16%. In future research, enhancements to the model's accuracy could be achieved by incorporating factors such as clean energy policy incentives and subsidies, which significantly influence BEV adoption. Additionally, the regression model can be expanded to include more metropolitan areas as new BEV registration data becomes available through the Open Vehicle Registration Initiative (Atlas Public Policy, 2022).
590 $a School code: 0075.
650 4 $a Engineering. $3 586835
650 4 $a Automotive engineering. $3 2181195
653 $a Battery electric vehicle line Planning
653 $a Battery electric vehicle range
653 $a Range estimation
653 $a Regional markets
653 $a Clean energy policy
690 $a 0454
690 $a 0537
690 $a 0540
690 $a 0338
710 2 $a The George Washington University. $b Engineering Management. $3 1262973
773 0 $t Dissertations Abstracts International $g 85-06B.
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792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30815138