A Graph‐Based Lithium‐Ion Battery Parameter Estimation Approach to Produce Diverse Synthetic Data. Issue 8 (21st June 2022) - Channegowda, Janamejaya; Lachappa, Yugendra Gowda; Jagwani, Shefali
Abstract: 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. Abstract : Limited availability of battery datasets is a major roadblock towards developing State‐of‐Charge estimation algorithms. They aim to resolve this issue by introducing a graph‐based synthetic data generation technique. The most important contribution of the present approach is production of superior quality synthetic data. The generated diverse and heterogeneous battery parameter datasets help improve accuracy of State‐of‐Charge computations.