Forecasting driving behavior to enable efficient grid integration of plug-in electric vehicles

Electric vehicles were proposed as a good solution to solving energy crisis and environmental problems caused by the traditional internal combustion engine vehicles. In the last years due to the rapid development of the electric vehicles, the problem of power grid integration was addressed. In order to not put additional pressure onto the power grid several new technologies were developed. This chapter presents the smart grid technology, vehicle-to-grid concept, and electric vehicles grid integration. These technologies made possible the integration of electric vehicles without any major changes in the power grid. Moreover, electric vehicles integration brought new benefits to the power grid like better integration of renewable energy.

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The future of electric vehicles to grid integration in Indonesia

Energy Procedia ◽

10.1016/j.egypro.2019.01.749 ◽

2019 ◽

Vol 158 ◽

pp. 4592-4597 ◽

Cited By ~ 8

Author(s):

Muhammad Huda ◽

Muhammad Aziz ◽

Koji Tokimatsu

Keyword(s):

Electric Vehicles ◽

Grid Integration ◽

The Future

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A General Parameter Identification Procedure Used for the Comparative Study of Supercapacitors Models

Energies ◽

10.3390/en12091776 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1776 ◽

Cited By ~ 10

Author(s):

Henry Miniguano ◽

Andrés Barrado ◽

Cristina Fernández ◽

Pablo Zumel ◽

Antonio Lázaro

Keyword(s):

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Parametric Identification ◽

Grid Integration ◽

Current Profile ◽

Model Accuracy ◽

Complex Processes ◽

General Parameter ◽

Identification Parameter ◽

The Comparative Study

Supercapacitors with characteristics such as high power density, long cycling life, fast charge, and discharge response are used in different applications like hybrid and electric vehicles, grid integration of renewable energies, or medical equipment. The parametric identification and the supercapacitor model selection are two complex processes, which have a critical impact on the system design process. This paper shows a comparison of the six commonly used supercapacitor models, as well as a general and straightforward identification parameter procedure based on Simulink or Simscape and the Optimization Toolbox of Matlab®. The proposed procedure allows for estimating the different parameters of every model using a different identification current profile. Once the parameters have been obtained, the performance of each supercapacitor model is evaluated through two current profiles applied to hybrid electric vehicles, the urban driving cycle (ECE-15 or UDC) and the hybrid pulse power characterization (HPPC). The experimental results show that the model accuracy depends on the identification profile, as well as the robustness of each supercapacitor model. Finally, some model and identification current profile recommendations are detailed.

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