Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Yuefeng Liu Guangquan Zhao Xiyuan Peng Cong Hu

Abstract

Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A novel RUL prediction approach based on Long Short-term Memory (LSTM) Recurrent Neural Network (RNN) is proposed in this paper. LSTM is able to capture long-term dependencies and model sequential data among the capacity degradation of lithium-ion batteries. The advantages of our proposed method include: 1) obtaining high prediction accuracy without accurate physics-based model or expertise and 2) decreasing the cumulation errors by multi-step ahead prediction each time, while traditional RUL method predicts one-step ahead once and then uses the current estimated value to predict next one, which causes cumulation errors increased. The Center for Advanced Life Cycle Engineering (CALCE) battery datasets are used to demonstrate the effectiveness of the proposed method. The results show that, compared with echo state networks (ESN), the proposed method has higher accuracy, more stable and reliable performance for lithium-ion batteries RUL prediction.

How to Cite

Liu, Y., Zhao, G., Peng, X., & Hu, C. (2017). Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2447
Abstract 386 | PDF Downloads 300

##plugins.themes.bootstrap3.article.details##

Keywords

Lithium-ion battery, remaining useful life prediction, Long short-term memory recurrent neural network

References
Dong H., Jin X., Lou Y. & Wang C. (2014). Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter. Journal of Power Sources, 271, 114-123.
Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on (pp. 6645-6649). IEEE.
He W., Williard N., Osterman M., & Pecht M. (2011). Prognostics of Lithium-ion batteries based on dempster-shafer theory and the bayesian monte carlo method. Journal of Power Sources., 196, 10314–10321.
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120-135.
Li, H., Pan, D., & Chen, C. P. (2014). Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(7), 851-862.
Liu, D., Xie, W., Liao, H., & Peng, Y. (2015). An integrated probabilistic approach to lithium-ion battery remaining useful life estimation. IEEE Transactions on Instrumentation and Measurement, 64(3), 660-670.
Liu D., Zhou J. & Guo L. (2015). Survey on lithium-ion battery health assessment and cycle life estimation. Chinese Journal of Scientific Instrument, 36(1), 1-16.
Liu, Y., Zhao, G., & Peng, X. (2016). A fusion prognostic approach based on multi-kernel relevance vector machine and Bayesian model averaging. In Prognostics and System Health Management Conference (PHM-Chengdu), 2016 (pp. 1-6). IEEE.
Miao Q., Xie L., Cui H., Liang, W. & Pecht M. (2013). Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron. Rel., vol. 53, pp. 805-810.
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
Xing, Y., Ma, E. W., Tsui, K. L., & Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 53(6), 811-820.
Yang, W. A., Xiao, M., Zhou, W., Guo, Y., & Liao, W. (2016). A hybrid prognostic approach for remaining useful life prediction of lithium-ion batteries. Shock and Vibration, 2016.
You, Q., Jin, H., Wang, Z., Fang, C., & Luo, J. (2016). Image captioning with semantic attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4651-4659).
Yuan, M., Wu, Y., & Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network. In Aircraft Utility Systems (AUS), IEEE International Conference on (pp. 135-140). IEEE.
Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bi-directional lstm networks. Sensors, 17(2), 273.
Wang, S., & Jiang, J. (2015). Learning natural language inference with LSTM. arXiv preprint arXiv:1512.08849.
Section
Technical Papers