Solving Limited Data Challenges in Battery Parameter Estimators by Using Generative Adversarial Networks

Naaz, Falak; Channegowda, Janamejaya; Lakshminarayanan, Meenakshi; John, Neha Sara; Herle, Aniruddh

doi:10.1109/powerafrica52236.2021.9543237

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…A Multilayer DNN has also been used for SOC prediction [15]. DNN has been a promising methodology which has been employed towards SOC estimation due to its ability to work with non-linear input data [16], [17]. All data-driven models assume access to completely labelled datasets but, privacy concerns limit the access of proprietary datasets to battery researchers.…”

Section: A Literature Reviewmentioning

confidence: 99%

An attention‐based synthetic battery data augmentation technique to overcome limited dataset challenges

Channegowda

Maiya²,

Joshi³

et al. 2022

Energy Storage

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Large‐scale global efforts for electrifying different modes of transportation have fueled the need for energy‐dense storage systems. Lithium‐ion batteries have shown promise to be a favourable technology to power pure electric vehicles, off‐board storage systems for microgrids and other hybrid vehicle applications. Accurate estimation of vital battery parameters, such as state‐of‐charge, enable the user to predict remaining useful life of lithium‐ion batteries. Although multiple data‐driven techniques over the years have improved prediction accuracy, lack of high‐quality data continues to be a challenge which has been largely ignored in literature. Commercial battery manufacturers fail to release their datasets due to privacy concerns thereby worsening this crisis. The limited data challenge has been further aggravated due to advent of novel charging techniques, such as pulsed discharge and constant‐temperature charging profile. This article aims to resolve this data scarcity issue by employing an attention‐based technique to generate reliable synthetic data. The generated data will greatly assist researchers to develop reliable battery ageing prediction models. The approach followed in this article consumes very less computational resources as well.

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Section: A Literature Reviewmentioning

confidence: 99%

An attention‐based synthetic battery data augmentation technique to overcome limited dataset challenges

Channegowda

Maiya²,

Joshi³

et al. 2022

Energy Storage

View full text Add to dashboard Cite

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A Deep Learning Approach Towards Generating High-Fidelity Diverse Synthetic Battery Datasets

Channegowda

Maiya²,

Lingaraj³

2023

IEEE Trans. on Ind. Applicat.

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Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.

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Solving Limited Data Challenges in Battery Parameter Estimators by Using Generative Adversarial Networks

Cited by 2 publications

References 8 publications

An attention‐based synthetic battery data augmentation technique to overcome limited dataset challenges

An attention‐based synthetic battery data augmentation technique to overcome limited dataset challenges

A Deep Learning Approach Towards Generating High-Fidelity Diverse Synthetic Battery Datasets

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