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arXiv · 2111.09314

GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation

Edward Elson Kosasih·Rucha Bhalchandra Joshi·Janamejaya Channegowda
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Abstract

Lithium-ion batteries are powering the ongoing transportation electrification revolution. Lithium-ion batteries possess higher energy density and favourable electrochemical properties which make it a preferable energy source for electric vehicles. Precise estimation of battery parameters (Charge capacity, voltage etc) is vital to estimate the available range in an electric vehicle. Graph-based estimation techniques enable us to understand the variable dependencies underpinning them to improve estimates. In this paper we employ Graph Neural Networks for battery parameter estimation, we introduce a unique graph autoencoder time series estimation approach. Variables in battery measurements are known to have an underlying relationship with each other in a certain correlation within variables of interest. We use graph autoencoder based on a non-linear version of NOTEARS as this allowed us to perform gradient-descent in learning the structure (instead of treating it as a combinatorial optimisation problem). The proposed architecture outperforms the state-of-the-art Graph Time Series (GTS) architecture for battery parameter estimation. We call our method GAETS (Graph AutoEncoder Time Series).

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Edward Elson Kosasih, Rucha Bhalchandra Joshi, Janamejaya Channegowda. 2022-08-19. GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation. https://arxiv.org/abs/2111.09314

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