A Hierarchical Model for Lithium-Ion Battery Degradation Prediction

Developing prognostics and health management (PHM) approaches for lithium-ion batteries has received increasing attention in recent years. This paper presents a new modeling framework to characterize lithium-ion battery degradation by examining detailed discharging voltage profiles in different discharging cycles. We propose a hierarchical model, combining discharging processes and degradation processes, to predict the end of discharges in different cycles and remaining useful cycles integratively. We use a real case study to demonstrate the effectiveness and promising features of the proposed framework.

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

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

[3]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

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

[5]  Min Li,et al.  Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter , 2014, IEEE Transactions on Instrumentation and Measurement.

[6]  M. Karim.  Generalized Linear Models With Random Effects , 1991 .

[7]  B. Saha,et al.  Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles : Some Lessons Learned , 2012 .

[8]  B. Bowerman,et al.  Forecasting, time series, and regression : an applied approach , 2005 .

[9]  A. Gelman.  Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

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

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

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

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

[14]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

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

[16]  Maxime Montaru,et al.  Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use , 2014 .

[17]  J. D. Kozlowski.  Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[18]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[19]  A. Zellner.  An Introduction to Bayesian Inference in Econometrics , 1971 .

[20]  Mihai V. Micea,et al.  Online State-of-Health Assessment for Battery Management Systems , 2011, IEEE Transactions on Instrumentation and Measurement.