A two-stage deep learning framework for early-stage lifetime prediction for lithium-ion batteries with consideration of features from multiple cycles

Abstract

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Lithium-ion batteries are a crucial element in the electrification and adoption of renewable energy. Accurately predicting the lifetime of batteries with early-stage data is critical to facilitating battery research, production, and deployment. But this problem remains challenging because batteries are complex, nonlinear systems, and data acquired at the early-stage exhibit a weak correlation with battery lifetime. In this paper, instead of building features from specific cycles, we extract features from multiple cycles to form a time series dataset. Then the time series data is compressed with a GRU-based autoencoder to reduce feature dimensionality and eliminate the time domain. Further, different regression models are trained and tested with a feature selection method. The elastic model provides a test RMSE of 187.99 cycles and a test MAPE of 10.14%. Compared with the state-of-art early-stage lifetime prediction model, the proposed framework can lower the test RMSE by 10.22% and reduce the test MAPE by 28.44%.

Keywords

• lithium-ion battery
• machine learning
• lifetime prediction
• early-stage
• autoencoder
• data-driven model