GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation
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Abstract (arXiv)
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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References · 12
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
- 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
- 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
- 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
- 2021Merlion: A Machine Learning Library for Time Seriescited by 25
- 2021Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warpingcited by 6
- 2021On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurementscited by 2
- 2021An Algorithmic Safety VEST For Li-ion Batteries During Fast Chargingcited by 2
- 2020A Temporal Convolution Network Approach to State-of-Charge Estimation in Li-ion Batteriescited by 1
- 2020Battery Model Calibration with Deep Reinforcement Learningcited by 1
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