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Spatial prediction of thermal power storage field

To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3D thermal field from multiple homogeneous fields.
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About Spatial prediction of thermal power storage field

About Spatial prediction of thermal power storage field

To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3D thermal field from multiple homogeneous fields.

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6 FAQs about [Spatial prediction of thermal power storage field]

Can physics-reserved spatiotemporal modeling predict battery temperature?

Extensive experiments showed that the physics-reserved spatiotemporal modeling method outperforms a widely used data-driven spatiotemporal modeling method for battery temperature prediction.

Can a spatial-temporal model predict the temperature field of a 40-string battery?

This paper introduces a spatial-temporal model that quickly predicts the temperature field of the 40-string battery pack with a cell-level computational consumption using the collected sparse signals, where the prior knowledge of battery mechanisms and complex physical modeling are no longer required.

How does physics-reserved spatiotemporal modeling work?

As shown in Fig. 1, the physics-reserved spatiotemporal modeling method constructs a composite network to model the battery thermal process. Except for the spatial coordinate (i.e., x) and time variable (i.e., t), the composite network adopts the battery current (i.e., I) and terminal voltage (i.e., V) as two extra control inputs.

How does the battery pack temperature field prediction model work?

The general architecture of the battery pack temperature field prediction model considering spatial-temporal characteristics. In the first stage, the LSTM model, the same size as temperature sensors, simultaneously predicts the cell surface temperature. Then, we get the predicted sparse temperature field of the battery pack.

Can the arbitrary location of a pack predict the temperature field?

Overall, the arbitrary location of the pack can achieve the prediction of the temperature field with the aid of the proposed spatiotemporal model. However, the selecting principles of optimal sensor location should be considered to obtain a higher prediction accuracy of the temperature field. Fig. 13.

Can physics improve the performance of spatiotemporal modeling?

Without using physics, the DL method performs worse than the proposed method for battery temperature prediction. That is to say, the physics can improve the performance of spatiotemporal modeling significantly. 4.4.2. Effectiveness of the branch network for parameter identification

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