Electric Vehicle (EV)
This dataset contains simulation results for the research article "Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control". Each CSV file corresponds to an experimental setup with a certain number of EVs included in the fleet and the risk-aversion determined by the risk-aversion factor Ω.
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This data provides an overview of the simulation of Hybrid Electric Vehicles (HEVs) using MATLAB. HEVs have emerged as a promising solution for reducing emissions and improving fuel efficiency. MATLAB offers a flexible platform to accurately model and analyze the complex dynamics of hybrid powertrains, including various components and control strategies. The simulation capabilities enable the evaluation of HEV performance under different conditions, optimization of designs, and exploration of factors impacting energy consumption.
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Two electric vehicles were used in this study, namely the Renault Zoe Q210 2016 and the Renault Kangoo ZE 2018. The EVs were equipped with data loggers connected to the CAN bus recording data on the HV battery current, voltage, SoC, and instantaneous speeds. We also used a GPS logger mobile application to record GPS tracks and altitudes. Data were collected from six drivers (four men and two women) with varying levels of driving experience (from less than two months to more than 10 years) on a variety of roads and driving conditions for nearly 200 kilometers
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The SiCWell Dataset contains data of battery electric vehicle lithium-ion batteries for modeling and diagnosis purposes. In this experiment, automotive-grade lithium-ion pouch bag cells are cycled with current profiles plausible for electric vehicles.
The analysis of current ripples in electric vehicles and the corresponding aging experiments of the battery cells result in a dataset, which is composed of the following parts:
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This file contains one-year measurements of demand (average 11 kWh/day), Electric vehicle charging (3 kW rating), and PV generation (3.3 kWp) for a household in London, UK.
This dataset is associated with the following paper:
A. A. R. Mohamed, R. J. Best, X. A. Liu and D. J. Morrow, "A Comprehensive Robust Techno-Economic Analysis and Sizing Tool for the Small-Scale PV and BESS," in IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2021.3107103.
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