Findings from O. Rahbari and Co-Researchers Advance Knowledge in Algorithms (A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm)
- October 12, 2018
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By a News Reporter-Staff News Editor at Journal of Mathematics -- Current study results on Algorithms have been published. According to news originating from Heverlee, Belgium, by VerticalNews correspondents, research stated, "Increasing interest in the successful coordination of electric vehicles and renewable energy sources has recently been shown by researchers and power generation companies, in large part due to its impact on de-carbonization of urban areas and its capability of contributing towards ancillary services. Nevertheless, this coordination requires a bi-directional communication infrastructure, between combined electric vehicles, renewable energy systems, and power plants since one of the main reasons of this combination is to address the temporal fluctuations in renewable power generation."
Financial support for this research came from Flanders Make.
Our news journalists obtained a quote from the research, "This bi-directional communication enables the power grid to adapt to different power source structures and improves the acceptability of intermittent renewable energy generation. Whereas electric vehicles equipped with lithium-ion batteries appear to be feasible options for stationary energy storage systems, known as a new distributed generation, the flexibility of electric vehicles in vehicle-to-grid connections is completely dependent on the maximum practical capacity and state of charge of each vehicle. Hence this infrastructure for the integration of electric vehicles as new distributed generation and renewable energy systems with electrical grids, emphasizes the need for an off-board state estimation of electric vehicles in aggregators. Moreover, an accurate estimation of the state of health and state of charge when electric vehicles are not charged or discharged by a constant current profile is required to overcome the challenges of existing methods. Each of introduced methods has different limitations, which are presented in this article. This article proposes a novel off-board state estimation technique for such parameters as state of charge and maximum practical capacity by employing a metaheuristic algorithm and an adaptive neuro-fuzzy inference system to overcome the limitations of existing methods. Due to drawbacks of filtering techniques, inverse theory is used in this article to convert the filtering problem to an optimization problem in order to take advantage of its capability."
According to the news editors, the research concluded: "The results exhibit not only a high convergence rate (low settling time) but also a high robustness."
For more information on this research see: A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm. Energy, 2018;155():1047-1058. Energy can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Energy - www.journals.elsevier.com/energy/)
The news correspondents report that additional information may be obtained from O. Rahbari, Flanders Make, B-3001 Heverlee, Belgium. Additional authors for this research include N. Omar, Y. Firouz, M.A. Rosen, S. Goutam, P. Van Den Bossche and J. Van Mierlo.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.energy.2018.05.079. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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