Abstract
Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTM prediction that matches input layer with output layer as one-to-one structure, we leverage many-to-one structure to be flexible for various input types and to substantially reduce the number of parameters for better generalization. Using the NASA lithium-ion battery datasets, we verify the accuracy of the proposed LSTM-based RUL prediction. The experimental results show that the proposed single-channel LSTM model improves the mean absolute percentage error (MAPE) by 39.2% compared to the baseline LSTM model. Furthermore, the proposed multi-channel LSTM model significantly improves the MAPE, e.g., by 63.7% compared to the baseline; the proposed model achieves 0.47–1.88% of MAPE while the state-of-the-art baseline LSTM shows 0.6–6.45% of MAPE.
Highlights
Lithium-ion batteries are widely used for many applications such as home appliances, smartphones, power tools, energy storage systems and electric vehicles because of high energy density, high electromotive force, high output voltage, low self-discharge, low voltage drop and easy management [1], [2]
We demonstrated that this structure change is effective to capture the capacity regeneration phenomenon
We exploited the multi-channel charging profiles of voltage, current, and temperature, which are the essential features for remaining useful life (RUL) prediction
Summary
Lithium-ion batteries are widely used for many applications such as home appliances, smartphones, power tools, energy storage systems and electric vehicles because of high energy density, high electromotive force, high output voltage, low self-discharge, low voltage drop and easy management [1], [2]. Unlike previous methods using single-channel data only, i.e., capacity per cycle, we show that leveraging multi-channel charging profiles of voltage, current, and temperature significantly improves the RUL prediction accuracy even in the presence of capacity regeneration. The aged batteries reach the maximum temperature faster than the fresh battery This shows that the charging profiles of voltage, current, and temperature depend on SoH, and we leverage these multichannel charging features to predict the remaining battery capacity in addition to historic capacity data {Ck }. In using LSTM for RUL prediction, the state-of-theart technique uses only the historic capacity data {Ck } as input [1], [15], and the input and output sizes are same [19], [20].
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