Abstract
AbstractEnergy dense lithium‐ion batteries are extensively used in all portable electronic devices and in electric vehicles as well. State‐of‐charge estimation of these batteries has been of considerable commercial interest as this key metric can be construed as the available range in electric vehicles. State‐of‐charge is also important to ascertain the remaining usage time in battery powered devices. In this paper a graph neural network‐based approach is employed to estimate key battery parameters such as, voltage, battery capacity, etc. To the best of the authors' knowledge, this is the first paper to employ a graph‐based approach to improve battery estimates. The pairwise interdependencies within the battery dataset are exploited to provide better battery estimates. The graph‐based approach is compared with related statistical methods to highlight the effectiveness of this approach.
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