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Energy 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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