Battery_RUL_Benchmark

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Battery_RUL_Benchmark

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# Battery RUL Benchmark（2023）

The gradually increased amount of battery aging data has enabled rapid development of data-driven and machine learning algorithms in battery state assessment and lifetime prediction. Despite deep commitment and broad excitement, significant gaps still exist which hinders a thorough comparison and rapid iterative of the prediction algorithm. First, the formats across many public data sources are inconsistent; second, a majority of the algorithms are close source and lack of reproducibility; Last, the definition of evaluation metrices varies under different prediction scenarios, which pose challenges to compare different algorithms and develop novel one.

If you do not have a `Python` environment locally, you can click on the `Colab` badge below and run the experimental example using the code we shared.

# Battery Datasets

Datasets available for battery RUL prediction tasks

# Prediction RUL

## Direct prediction of RUL in Nature dataset

## Iterative prediction of RUL using linear regression in NASA dataset

## Iterative prediction of RUL using Gaussian process regression method in NASA dataset

# Acknowledge

Battery_RUL_Benchmark

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