Open AccessAccuracy improvement of remaining capacity estimation for energy storage batteries
Author(s) -
Xu Cheng,
Liu Guo'an,
Wang Kangli
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0648
Subject(s) - computer science , estimation , scheduling (production processes) , energy storage , energy (signal processing) , artificial neural network , power (physics) , reliability engineering , mathematical optimization , engineering , statistics , artificial intelligence , mathematics , physics , systems engineering , quantum mechanics
Scheduling lithium‐ion batteries for energy storage applications in power systems requires accurate estimation of their remaining capacity. Due to the varying discharge rate during a cycle caused by complex operating conditions, conventional estimation methods suffer greatly from poor estimation accuracy. Therefore, this study proposes an improved Ah‐counting method for remaining capacity estimation, in which a neural network‐based model is introduced to consider the rate capability of batteries. Moreover, to avoid the inherent error caused by Ah‐counting, a straightforward method is applied to the estimation. Simulations show that the estimation accuracy can be effectively improved by the proposed method.