State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution.
Sensors (Basel)
; 22(23)2022 Dec 02.
Article
en En
| MEDLINE
| ID: mdl-36502139
ABSTRACT
A battery's charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model's effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model's prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging-discharging behaviour in actual operation.
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Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Líquidos Corporales
/
Litio
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Sensors (Basel)
Año:
2022
Tipo del documento:
Article
País de afiliación:
China