LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles

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. INDEX TERMS Lithium-ion battery, long short-term memory, remaining useful life, capacity estimation.

Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data

“…Hence, the shorter training time and better performance of the model are expected. To validate the performance of the selected partial range for both charge and discharge data, the results were compared to the results presented by Park et al [3]. Table 9 and Fig.…”

“…The lithium-ion (Li-ion) battery is widely used in electric vehicles because of its exceptional high cell voltage, high energy density, electromotive force, high output voltage, long lifetime, high charging efficiency, low self-discharge, low voltage drop, easy maintenance, and recycle [2], [3], [4]. These advantages have contributed to wider applications of lithium-ion batteries in more areas such as vehicles, household equipment, communications, aerospace, and other fields [5].…”

“…Despite some success of model-based methods, in practice, it is difficult to have a precise and well-established model that would allow tuning and updating the parameters during the prediction phase with different operational conditions [17], and there are not well-established failure physical models [3]. Considering the above-mentioned drawbacks of model-based approaches to predict the RUL, data-driven methods have proven to be more efficient because there is no need for mathematical modeling to compute the battery degradation.…”

“…The required training data was 20-50% less using LSTM compared to particle filter (PF), and the performance results indicated that the LSTM generally predicts more accurate and precise than the support vector machine (SVM) and simple recurrent neural networks (SimRNNs). Park et al [3] compared LSTM method with basic RNN, gated recurrent unit (GRU) and simple recurrent unit (SRU) using multi-channel charging profiles, and the mean absolute percentage error (MAPE) of the LSTM was better than basic RNN, GRU, and SRU by 32-52%.…”

Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

“…Artificial intelligence techniques are widely used for shortterm load forecasting, such as artificial neural network (ANN) [6], support vector machine (SVM) [7], extreme learning machine [8], Bayesian neural network [9], deep neural network [10] and recurrent neural network (RNN) [11]. In [12], long short-term memory (LSTM) is used to solve the vanishing gradient problem of RNN and outperforms other neural network methods. In addition, combining ResNet and LSTM was proposed for short-term load forecasting [13].…”