  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).
