Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

Abstract

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles.

Publication DOI: https://doi.org/10.1109/TVT.2018.2884812
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publication ISSN: 1939-9359
Full Text Link:
Related URLs: https://ieeexpl ... cument/8581516/ (Publisher URL)
PURE Output Type: Article
Published Date: 2019-02
Published Online Date: 2018-12-19
Accepted Date: 2018-12-01
Authors: Du, Guanhao
Cao, Wenping
Hu, Shubo
Lin, Zhengyu (ORCID Profile 0000-0001-7733-2431)
Yuan, Tiejiang

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