A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

[1]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[2]  Osama Moselhi,et al.  Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models , 2016 .

[3]  Yu-Hua Sun,et al.  Aging Estimation Method for Lead-Acid Battery , 2011, IEEE Transactions on Energy Conversion.

[4]  Khalil Benmouiza,et al.  Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models , 2016, Theoretical and Applied Climatology.

[5]  Yandong Hou,et al.  Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression , 2018, Energies.

[6]  Yoshio Nishi.  Lithium Ion Secondary Batteries , 1998 .

[7]  Jianbo Yu,et al.  State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model , 2015, IEEE Transactions on Instrumentation and Measurement.

[8]  Yu Peng,et al.  A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Zhenhong Du,et al.  Multistep-ahead forecasting of chlorophyll a using a wavelet nonlinear autoregressive network , 2018, Knowl. Based Syst..

[10]  Samir Jemei,et al.  Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles , 2016 .

[11]  Yi-Jun He,et al.  State of health estimation of lithium‐ion batteries: A multiscale Gaussian process regression modeling approach , 2015 .

[12]  Michael Pecht,et al.  Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability , 2013 .

[13]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[14]  Jianbo Yu,et al.  State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble , 2018, Reliab. Eng. Syst. Saf..

[15]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[16]  N. Zerhouni,et al.  Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMMs , 2011, 2011 Aerospace Conference.

[17]  Xinghui Zhang,et al.  Degradation Prediction Model Based on a Neural Network with Dynamic Windows , 2015, Sensors.

[18]  Hojjat Adeli,et al.  A new music-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals , 2015, Digit. Signal Process..

[19]  Zonghai Chen,et al.  An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks , 2016 .

[20]  K. Wang,et al.  Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network , 2017, Epidemiology and Infection.

[21]  Chen Yang,et al.  Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery , 2017, Microelectron. Reliab..

[22]  Shengkui Zeng,et al.  Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model , 2015, Microelectron. Reliab..

[23]  Yandong Hou,et al.  Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm , 2017 .

[24]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[25]  Yu Peng,et al.  An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter , 2018, IEEE Access.

[26]  Dong Gao,et al.  Prediction of Lithium-ion Battery ' s Remaining Useful Life Based on Multi-kernel Support Vector Machine with Particle Swarm Optimization , 2017 .

[27]  Sanjay H Upadhyay,et al.  The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings , 2017 .

[28]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[29]  Li-Ming Deng,et al.  An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery , 2017 .

[30]  Shouwen Ji,et al.  Forecasting of Chinese E-Commerce Sales: An Empirical Comparison of ARIMA, Nonlinear Autoregressive Neural Network, and a Combined ARIMA-NARNN Model , 2018, Mathematical Problems in Engineering.

[31]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[32]  Hongwen He,et al.  Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.

[33]  Y. Nishi.  Lithium ion secondary batteries; past 10 years and the future , 2001 .

[34]  Ram Bilas Pachori,et al.  Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals , 2018, Digit. Signal Process..

[35]  Srdjan M. Lukic,et al.  Energy Storage Systems for Automotive Applications , 2008, IEEE Transactions on Industrial Electronics.