Campus Units

Civil, Construction and Environmental Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

11-2016

Journal or Book Title

Transportation Research Part C: Emerging Technologies

Volume

72

First Page

272

Last Page

282

DOI

10.1016/j.trc.2016.10.001

Abstract

In this paper, we study battery capacity design for battery electric vehicles (BEVs). The core of such design problems is to find a good tradeoff between minimizing the capacity to reduce financial costs of drivers and increasing the capacity to satisfy daily travel demands. The major difficulty of such design problems lies in modeling the diversity of daily travel demands. Based on massive trip records of taxi drivers in Beijing, we find that the daily vehicle miles traveled (DVMT) of a driver (e.g., a taxi driver) may change significantly in different days. This investigation triggers us to propose a mixture distribution model to describe the diversity in DVMT for various driver in different days, rather than the widely employed single distribution model. To demonstrate the merit of this new model, we consider value-at-risk and mean-variance battery capacity design problems for BEV, with respect to conventional single and new mixture distribution models of DVMT. Testing results indicate that the mixture distribution model better leads to better solutions to satisfy various drivers.

Research Focus Area

Transportation Engineering

Comments

This is a manuscript of an article published as Li, Zhiheng, Shan Jiang, Jing Dong, Shoufeng Wang, Zhennan Ming, and Li Li. "Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled." Transportation Research Part C: Emerging Technologies 72 (2016): 272-282. DOI: 10.1016/j.trc.2016.10.001. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Published Version

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