Technical Papers
Jul 14, 2022

When to Charge a Small-Range Battery Electric Vehicle: Refining Charging Strategies Using a Scenario-Based Approach

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 9

Abstract

Small-range battery electric vehicles (BEVs), with driving ranges less than 200 km, are susceptible to charging defects. This study applied a scenario-based approach by modeling a cost function to recommend an optimal charging strategy for small-range BEV users. The proposed cost function considers the operating and range limitation costs of BEVs. The charging threshold, which reflects the charging preference of a user, is adjusted using the cost function and analyzed by comparing different scenarios using real data collected from Shanghai, China. The simulation results indicate that every 2.5 times increase in the charging threshold results in a decrease of approximately 48.9% in the charging times and a 10% reduction in the electrified proportion of a journey. For electrified proportions below 85%, small-range BEVs are no longer an economical option over internal combustion engine vehicles. Moreover, the optimal charging threshold is 35%–41% for BEVs with a 170 km range, and the corresponding daily ownership costs are more reasonable than those of medium and long-range BEVs. The scenario analysis of the availability of the charging piles and battery price further demonstrates the economic advantages of small-range BEVs. These results provide instructible charging strategies for small-range BEV users to alleviate the shortcomings of the range limitations and frequent charging.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work is partially supported by the major science and technology project of China Communications Construction Company Ltd. (CCCC) in 2019 (Project No. 2019-ZJKJ-ZDZX02), and the Key Project of National Natural Science Foundation of China (No. 52131203).

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 148Issue 9September 2022

History

Received: Jan 4, 2022
Accepted: Apr 26, 2022
Published online: Jul 14, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 14, 2022

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Ph.D. Candidate, Jiangsu Key Laboratory of Urban Intelligent Transportation System, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast Univ., Nanjing 211189, China. Email: [email protected]
Professor, Development Research Institute of Transportation Governed by Law, Law School, Southeast Univ., Nanjing 211189, China (corresponding author). ORCID: https://orcid.org/0000-0001-7637-2351. Email: [email protected]
Zhiyuan Liu, Ph.D. [email protected]
Professor, Jiangsu Key Laboratory of Urban Intelligent Transportation System, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast Univ., Nanjing 211189, China. Email: [email protected]

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  • Dynamic Systems Modeling and Integrated Transportation Demand-and-Supply Management with a Polynomial Arrival Queue Model, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8136, 150, 4, (2024).
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