Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

Simolin, Toni; Rauma, Kalle; Järventausta, Pertti; Rautiainen, Antti

doi:10.1049/iet-stg.2020.0100

Cited by 7 publications

(6 citation statements)

References 22 publications

(35 reference statements)

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“…In short, the idea of the CCE algorithm is to use charging current measurements to determine the charging characteristics of each active EV, and then use that information to reallocate any potentially unused charging capacity of an EV to other EVs. Modelling each charging session separately makes the algorithm scalable, which is the main issue in the solution presented in [17]. In addition, the modelling considers three‐phase charging as opposed to the solution presented in [17] that focuses only on single‐phase charging.…”

Section: Charging Control Algorithmmentioning

confidence: 99%

Section: Charging Control Algorithmmentioning

confidence: 99%

“…Modelling each charging session separately makes the algorithm scalable, which is the main issue in the solution presented in [17]. In addition, the modelling considers threephase charging as opposed to the solution presented in [17] that focuses only on single-phase charging. The charging characteristics model is constituted of a maximum current and a matrix of all current limitcorrespondences from 6 A to the maximum current supported by the charging point (e.g.…”

Section: Proposed Charging Characteristic Expectation Algorithmmentioning

confidence: 99%

“…The issue regarding the non-idealities has recently been brought up in [15][16][17] and a solution is presented in [16,17]. In [15], the final charging curves of 304 charging sessions are analysed.…”

Section: Related Research and Motivationmentioning

confidence: 99%

“…It is worth mentioning that even a higher capacity usage percent is possible by exploiting the fact that in some cases, such as [17], the objective is to limit the average power of a 1-h long period to a certain level, and thus, the power is allowed to momentarily be higher than the targeted level.…”

Section: Peak Power-based Capacity Allocationmentioning

confidence: 99%

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Foundation for adaptive charging solutions: Optimised use of electric vehicle charging capacity

et al. 2021

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As electric vehicles (EVs) are emerging, smart and adaptive charging algorithms have become necessary to ensure safe and efficient operation of the grid. In the scientific literature, most of the proposed charging control algorithms focus solely on EV usagerelated behaviour, while the charging characteristics of EVs are overlooked. Herein, realistic charging characteristics are illustrated and discussed. More notably, to overcome the issues caused by the non-idealities in charging characteristics, a new adaptive charging characteristics expectation algorithm is proposed. The objective of this algorithm is to enable accurate estimation of the non-ideal charging characteristics. This can be used to reallocate any unused charging capacity and to ensure that the intended total capacity is used effectively. The effectiveness of the proposed algorithm is demonstrated using hardware-in-the-loop simulations with commercial EVs and real charging data. The results show that the proposed algorithm achieves an 88%-97% capacity usage rate, while the current benchmark solution achieves only 45%.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Charging Control Algorithmmentioning

confidence: 99%

Section: Charging Control Algorithmmentioning

confidence: 99%