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

Abstract

Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to determine the battery state. The drawbacks of the previous prediction algorithms are insufficient or irrelevant features to explicitly model the battery aging and the use of fully charged/discharged datasets, which might result in poor prediction accuracy. Therefore, this study proposes a feature selection technique to adequately select optimum statistical feature subset and the use of partial charge/discharge data to determine the battery remaining useful life (RUL) using Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM). The proposed approach demonstrated exceptional RUL prediction results, with the root mean square error (RMSE) of 0.00286 and mean average error (MAE) of 0.00222 using partial discharge data. The proposed method shows prediction improvement in comparison with the use of full data and state-of-the-art outcomes from previous studies of the same open data from the National Aeronautics and Space Administration (NASA) prognostic battery data sets. INDEX TERMS Recurrent neural network, long short-term memory, remaining useful life, battery management systems, feature selection.

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

“…At the same time, the MAPE of the multi-channel LSTM model is improved by 63.7% compared to the baseline. Chinomona et al (2020) proposed a recurrent neural networklong-short-term memory (RNN-LSTM) model to select the best subset, and use a partial charge/discharge data set to predict battery RUL performance. The RMSE is 0.00286 and the MAE is 0.00222.…”

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

“…More details can be found in [16]. Recent applications of Machine learning models in reliability engineering include methodology development, system diagnostic, remaining useful life estimation and prognostic health management [17][18][19][20][21]. Unsupervised learning consists in examining datasets with only input variables or features, and no labels or response variable.…”

A Gaussian Process Based Method for Data- Efficient Remaining Useful Life Estimation

“…Especially the application of deep learning (DL) models with architectures of all kinds achieved impressive RUL estimation performances for various applications and often outperformed the state of the art on established benchmark data sets [8]. Chinomona et al, for example, applied long short-term memory neural networks to the problem of battery RUL estimation [16], Sun et al applied auto-encoder neural networks to predict the RUL of cutting tools [17] and Yang et al applied convolutional neural networks to the task of bearing RUL prediction [18]. Recent approaches tackle the issue of uncertainty quantification and utilization in DL applications to RUL prediction by applying Bayesian neural networks [9].…”