A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is introduced. Finally, we apply the attention mechanism to further improve the accuracy of the algorithm. Compared with traditional neural network methods, the method proposed in this paper has higher efficiency and accuracy.

[1]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[2]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[3]  P. Saratchandran,et al.  Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  Jun Wu,et al.  Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC , 2015 .

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

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

[7]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

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

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

[10]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[12]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Bowen Zhou,et al.  A Structured Self-attentive Sentence Embedding , 2017, ICLR.

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

[15]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[16]  Xue Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error , 2014 .

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

[18]  Zhenhai Gao,et al.  Influence of coupling of overcharge state and short-term cycle on the mechanical integrity behavior of 18650 Li-ion batteries subject to lateral compression , 2018 .

[19]  Bin Jiang,et al.  Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains , 2019, IEEE Transactions on Industrial Electronics.

[20]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[21]  Mirella Lapata,et al.  Long Short-Term Memory-Networks for Machine Reading , 2016, EMNLP.

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

[23]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

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

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

[26]  Ming Yang,et al.  A pathological brain detection system based on kernel based ELM , 2016, Multimedia Tools and Applications.

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

[28]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[29]  Andrew McCallum,et al.  Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction , 2018, NAACL.

[30]  Sungzoon Cho,et al.  Distance-based Self-Attention Network for Natural Language Inference , 2017, ArXiv.

[31]  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.

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

[33]  Shervin Motamedi,et al.  Extreme learning machine approach for sensorless wind speed estimation , 2016 .

[34]  Guang-Bin Huang,et al.  Face recognition based on extreme learning machine , 2011, Neurocomputing.

[35]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[36]  Richard Socher,et al.  A Deep Reinforced Model for Abstractive Summarization , 2017, ICLR.

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

[38]  이창기.  Long Short-Term Memory 기반의 Recurrent Neural Network를 이용한 개체명 인식 , 2015 .

[39]  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 .

[40]  Jian Wang,et al.  基于节点类型标注的网页主题信息抽取方法 (Approach of Extracting Web Page Informational Content Based on Node Type Annotation) , 2016, 计算机科学.

[41]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[42]  Yudong Zhang,et al.  A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm. , 2017, CNS & neurological disorders drug targets.

[43]  Miaohua Huang,et al.  Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model , 2016, Microelectron. Reliab..

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

[45]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[46]  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..

[47]  Tao Shen,et al.  DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding , 2017, AAAI.

[48]  Noureddine Zerhouni,et al.  Remaining Useful Life Estimation of Critical Components With Application to Bearings , 2012, IEEE Transactions on Reliability.

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

[50]  Xin Zhang,et al.  An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction , 2018, Microelectron. Reliab..

[51]  Yidong Chen,et al.  Deep Semantic Role Labeling with Self-Attention , 2017, AAAI.

[52]  Alexandros Iosifidis,et al.  Graph Embedded Extreme Learning Machine , 2016, IEEE Transactions on Cybernetics.

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

[54]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[55]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[56]  K. V. Price,et al.  Differential evolution: a fast and simple numerical optimizer , 1996, Proceedings of North American Fuzzy Information Processing.

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

[58]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

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

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

[61]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.