Investigation of denoising autoencoder-based deep learning model in noise-riding experimental data for reliable state-of-charge estimation

State-of-charge (SOC) estimation plays a crucial role in battery management systems to ensure safe and reliable operation. However, SOC estimation remains challenging due to the dynamic nature of battery systems and varying ambient conditions. Data-driven methods have emerged as effective tools for...

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Vydáno v:	Journal of energy storage Ročník 72; s. 108421
Hlavní autoři:	Hong, Seonri, Kang, Moses, Kim, Jonghoon, Baek, Jongbok
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 25.11.2023
Témata:	Data-driven method Denoising autoencoder Lithium-ion batteries Long short-term memory State-of-charge estimation Lithium-ion batteries Long short-term memory State-of-charge estimation Denoising autoencoder Data-driven method
ISSN:	2352-152X, 2352-1538
On-line přístup:	Získat plný text
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Popis
Shrnutí:	State-of-charge (SOC) estimation plays a crucial role in battery management systems to ensure safe and reliable operation. However, SOC estimation remains challenging due to the dynamic nature of battery systems and varying ambient conditions. Data-driven methods have emerged as effective tools for analyzing nonlinear dynamical systems, but their performance heavily relies on data quality. In actual applications, data susceptible to distortions caused by external factors such as sensor failure, circuitry, and temperature variations, leading to degraded model performance. To address the performance degradation resulting from data quality deterioration, this paper introduces a denoising autoencoder is implemented as a stacked multi-layer perceptron, which learns to reconstruct distorted data. Furthermore, we propose the ensemble method that combines the autoencoder with an estimation model for SOC estimation in lithium-ion batteries. The effectiveness of the proposed model is demonstrated through tests conducted on a dataset comprising drive cycle profile of Panasonic 18650PF cells. The model validated under two ambient temperatures scenarios: identical and different, using a distorted dataset with added randomly added noise and dropout. The experimental results reveal that the proposed model achieved a 3 % error in training the drive profile relative to the actual values at different ambient temperatures. When compared to the plain model, the proposed ensemble model showed an increased RMSE of 4 %. Additionally, the performance of different estimation models was compared, with the LSTM model achieving an RMSE 0.67 at different ambient temperatures, outperforming the Support Vector Regression (SVR) with an RMSE 1.35 and the Extended Kalman Filter (EKF) with an RMSE of 0.87. •A noise immune state-of-charge method using stacked autoencoder is proposed.•Combinations of the denoising autoencoder and deep learning models•Performance comparisons between model-based methods and data-driven•The framework increases the estimation accuracy of the SOC in different temperatures.
ISSN:	2352-152X 2352-1538
DOI:	10.1016/j.est.2023.108421