Impedance-based Capacity Estimation for Lithium-Ion Batteries Using Generative Adversarial Network

by   Seongyoon Kim, et al.

This paper proposes a fully unsupervised methodology for the reliable extraction of latent variables representing the characteristics of lithium-ion batteries (LIBs) from electrochemical impedance spectroscopy (EIS) data using information maximizing generative adversarial networks. Meaningful representations can be obtained from EIS data even when measured with direct current and without relaxation, which are difficult to express when using circuit models. The extracted latent variables were investigated as capacity degradation progressed and were used to estimate the discharge capacity of the batteries by employing Gaussian process regression. The proposed method was validated under various conditions of EIS data during charging and discharging. The results indicate that the proposed model provides more robust capacity estimations than the direct capacity estimations obtained from EIS. We demonstrate that the latent variables extracted from the EIS data measured with direct current and without relaxation reliably represent the degradation characteristics of LIBs.


page 1

page 2

page 3

page 4


Detecting Causal Relations in the Presence of Unmeasured Variables

The presence of latent variables can greatly complicate inferences about...

Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks

The effectiveness of biosignal generation and data augmentation with bio...

Exploring How Generative Adversarial Networks Learn Phonological Representations

This paper explores how Generative Adversarial Networks (GANs) learn rep...

Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks

Training deep neural networks on well-understood dependencies in speech ...

Guiding InfoGAN with Semi-Supervision

In this paper we propose a new semi-supervised GAN architecture (ss-Info...

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

This paper describes InfoGAN, an information-theoretic extension to the ...

Nonsparse learning with latent variables

As a popular tool for producing meaningful and interpretable models, lar...

Please sign up or login with your details

Forgot password? Click here to reset