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Publicação
Lithium-ion battery lifespan prediction using a transfer learning approach
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Resumo(s)
Lithium-ion battery (LIB) cycling data was collected from two publicly available online sources for use in a Gradient Boosting Machine, (LightGBM), and Long-Short-Term-Memory Recurrent Neural Network, (LSTM-RNN) towards predicting capacity decay and remaining useful life (RUL) in a transfer learning format. The two models were compared for their ability to predict state of health (SOH), knee-point (accelerating capacity loss), and second-life regions (< 80% nominal capacity), to inform potential applications of second-life lithium-ion batteries (SLBs). LightGBM proves accurate to an MAE of 3.4% SOH during cross-fold validation while the LSTM-RNN returned an MAE of 4.2% in SOH prediction. Knee-points and second-life regions are more accurately predicted by the LSTM-RNN but a stacked approach of both models is hypothesized as superior. Both models’ transfer learning is limited by cycling regimes and fails to generalize to alternatively cycled LIBs highlighting a need for more empirical LIB cycling data in future research.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Lithium-ion battery Light gradient boosting Second-life Knee-point Long-short term memory recurrent neural network Transfer learning SDG 7 - Affordable and clean energy SDG 13 - Climate action