Battery repurposing of plug-in electric vehicles: a framework for the integration of renewable energy and electrified transportation
| dc.contributor.author | Shokrzadeh, Shahab | |
| dc.contributor.examiningcommittee | Kuhn, David (Mechanical Engineering) Gole, Ani (Electrical and Computer Engineering) Etcheverry, Jose (Environmental Studies, York University) | en_US |
| dc.contributor.supervisor | Bibeau, Eric (Mechanical Engineering) Jafari Jozani, Mohammad (Statistics) | en_US |
| dc.date.accessioned | 2015-07-15T17:00:35Z | |
| dc.date.available | 2015-07-15T17:00:35Z | |
| dc.date.issued | 2014-10 | en_US |
| dc.date.issued | 2015 | en_US |
| dc.degree.discipline | Mechanical Engineering | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | A comprehensive framework is presented for the integration of electrified transportation and renewable energy through repurposing batteries of plug-in electric vehicles towards a sustainable energy future. The framework considers future market penetration scenarios of plug-in electric vehicles, availability of batteries at their vehicular end of life, and the storage capacity required to generate base-load wind power in the region of study. The objective is to develop a model that can be used as a policy tool to investigate how different scenarios and pertinent parameters can effectively meet the challenges of sustainability in the energy and transportation sectors when the ultimate goal is to simultaneously displace fossil fuels with new generation of low-cost intermittent renewable energy. A sample case study is performed for Canada to investigate and verify the performance of the model. The analysis shows that the proposed approach can further improve the energy sustainability performance of Canada in 2050 by 1.65–4.11%, depending on the confidence level and in addition to electrification of transportation. In the framework, a statistical algorithm is developed to calculate the capacity of an energy storage system required for delivering base-load electricity for a wind farm in the future electric grids. The algorithm contributes towards the goal of utilizing low- cost intermittent wind energy to base-load power generation in the future electric grids. The introduced algorithm presents three methods to perform the sizing calculations each representing a scenario associated with the stages of the wind energy industry. The results of the studied case are applied to estimate the cost of wind energy to produce rated power at different confidence levels, which show cost-effectiveness and less intermittency on the power systems allowing for larger penetrations of renewables. Advanced statistical methods are used to more accurately characterize the operational wind power output versus manufacturer’s power curve. This is essential for effective integration of wind power into the power systems. Four parametric and nonparametric models are applied to estimate the power curve of wind turbines based on the available operational wind power data. The results of this study suggest that the penalized spline regression method presents a better performance over the other analyzed methods. Finally, an experimental testing is performed in laboratory to show the proof of concept of the capacity degradation of used batteries of plug-in electric vehicles in stationary applications using a 25 kWh repurposed energy storage system obtained from a taxi fleet in their “as-is” condition. The proposed comprehensive framework herein presents an approach leading to a sustainable transportation system by providing low-cost renewable energy, and can be used as a gold standard to compare other policies like hydrogen energy technologies. | en_US |
| dc.description.note | October 2015 | en_US |
| dc.identifier.citation | S. Shokrzadeh, M. Jafari Jozani, and E. Bibeau, “Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods,” Sustainable Energy, IEEE Transactions on, vol. 5, no. 4, pp. 1262–1269, Oct 2014. | en_US |
| dc.identifier.citation | S. Shokrzadeh, M. Jafari-Jozani, E. Bibeau, and T. Molinski, “A Statistical Algorithm for Predicting the Energy Storage Capacity for Baseload Wind Power Generation in the Future Electric Grids,” in press, Energy, 2015. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/30626 | |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | open access | en_US |
| dc.subject | Renewable energy | en_US |
| dc.subject | Sustainable transportation | en_US |
| dc.subject | Battery repurposing | en_US |
| dc.subject | Electric vehicles | en_US |
| dc.title | Battery repurposing of plug-in electric vehicles: a framework for the integration of renewable energy and electrified transportation | en_US |
| dc.type | doctoral thesis | en_US |