Improving SOH estimation for lithium-ion batteries using TimeGAN

Seol, Sujin; Lee, Jungeun; Yoon, Jaewoo; Kim, Byeongwoo

doi:10.1088/2632-2153/acfd08

Cited by 2 publications

(1 citation statement)

References 27 publications

(47 reference statements)

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“…Therefore, the potential spatial accuracy is judged in this section by the similarity between the decoder output and the original data. Three evaluation criteria in TimeGAN [ 30 ] are used: (1) Diversity—the generated data distribution should cover the real data distribution, that is, t-SNE analysis is applied to the original data set and the generated data set, which visualizes the degree of similarity between the generated sample distribution and the original sample distribution in two-dimensional space, and gives a qualitative assessment of diversity. (2) Fidelity (discrimination score)—The generated data should be no different from the real data.…”

Section: Experimental Analysismentioning

confidence: 99%

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

Cai,

Zheng,

Peng

et al. 2024

PLoS ONE

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Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE’s potential in time series clustering anticipated in future research.

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Section: Experimental Analysismentioning

confidence: 99%