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Arxiv
1d
224
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Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset
- Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules and ensuring operational safety.
- A new framework was designed that integrates the neural ordinary differential equation and 2D convolutional neural networks to predict the state of charge (SOC), capacity, and state of health (SOH) of batteries using partial charging profiles as input.
- The model demonstrated high accuracy, with an R^2 accuracy of 0.998 for SOC and 0.997 for SOH across various temperatures.
- The trained model can be used to predict single cells at temperatures outside the training set and battery modules with different capacity and current levels.
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