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

• URL: http://arxiv.org/abs/2111.09314v1
• Date: Wed, 17 Nov 2021 16:04:01 GMT
• Title: GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation
• Authors: Edward Elson Kosasih, Rucha Bhalchandra Joshi, Janamejaya Channegowda
• Abstract summary: Lithium-ion batteries are powering the ongoing transportation revolution. Precise estimation of battery parameters is vital to estimate the available range in an electric vehicle. Graph-based estimation techniques enable us to understand the variable underpinning them to improve estimates.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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