Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.

[1]  Chen Lu,et al.  Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach , 2015 .

[2]  Wilson Wang,et al.  A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction , 2014, IEEE Transactions on Instrumentation and Measurement.

[3]  Ye Tao,et al.  A novel health indicator for on-line lithium-ion batteries remaining useful life prediction , 2016 .

[4]  Taejung Yeo,et al.  A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .

[5]  Furong Gao,et al.  A fast estimation algorithm for lithium-ion battery state of health , 2018, Journal of Power Sources.

[6]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[7]  Datong Liu,et al.  Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning , 2015 .

[8]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[9]  Zhen Liu,et al.  An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries , 2013, Microelectron. Reliab..

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

[11]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

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

[13]  Hicham Chaoui,et al.  Aging prediction and state of charge estimation of a LiFePO 4 battery using input time-delayed neural networks , 2017 .

[14]  Bhaskar Saha,et al.  An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .

[15]  Yu Peng,et al.  Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction , 2013 .

[16]  Massoud Pedram,et al.  An analytical model for predicting the remaining battery capacity of lithium-ion batteries , 2003, 2003 Design, Automation and Test in Europe Conference and Exhibition.

[17]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[18]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[19]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[20]  Thomas D. Gautheir.  Detecting Trends Using Spearman's Rank Correlation Coefficient , 2001 .

[21]  Noureddine Zerhouni,et al.  State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels , 2017 .

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

[23]  Zonghai Chen,et al.  A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve , 2018 .

[24]  Heinz Wenzl,et al.  Comparison of different approaches for lifetime prediction of electrochemical systems—Using lead-acid batteries as example , 2008 .

[25]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[26]  Huajing Fang,et al.  An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction , 2015, Reliab. Eng. Syst. Saf..

[27]  Jinquan Huang,et al.  Reduced kernel recursive least squares algorithm for aero-engine degradation prediction , 2017 .

[28]  Michael Buchholz,et al.  On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models , 2013 .

[29]  Junwei Han,et al.  Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries , 2017, IEEE Transactions on Instrumentation and Measurement.

[30]  Bin Jiang,et al.  Accurate Prediction of RUL under Uncertainty Conditions: Application to the Traction System of a High-speed Train , 2018 .

[31]  Xiaohong Su,et al.  Interacting multiple model particle filter for prognostics of lithium-ion batteries , 2017, Microelectron. Reliab..

[32]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[33]  Mehmed M. Kantardzic,et al.  SOM-based partial labeling of imbalanced data stream , 2017, Neurocomputing.

[34]  A. Tikhonov.  On the stability of inverse problems , 1943 .

[35]  Wei Sun,et al.  State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network , 2018, Energy.