Predicting Battery Aging Trajectory via a Migrated Aging Model and Bayesian Monte Carlo Method

Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.

[1]  Linxia Liao,et al.  Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.

[2]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[3]  Pengjian Zuo,et al.  The effect of elevated temperature on the accelerated aging of LiCoO2/mesocarbon microbeads batteries , 2016 .

[4]  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.

[5]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .

[6]  Kai Goebel,et al.  Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .

[7]  King Jet Tseng,et al.  A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model , 2017 .

[8]  Michael A. Kouritzin,et al.  Residual and stratified branching particle filters , 2017, Comput. Stat. Data Anal..

[9]  B. Kedem,et al.  Bayesian Prediction of Transformed Gaussian Random Fields , 1997 .

[10]  Furong Gao,et al.  Observer based battery SOC estimation: Using multi-gain-switching approach , 2017 .

[11]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

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

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

[14]  Ke Yao,et al.  Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine , 2018 .

[15]  Matthew B. Pinson,et al.  Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction , 2012, 1210.3672.

[16]  Chris Manzie,et al.  Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery , 2016 .

[17]  Yves Dube,et al.  A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures , 2016 .

[18]  Furong Gao,et al.  Model Migration with Inclusive Similarity for Development of a New Process Model , 2008 .