Validation of the methodology for lithium-ion batteries lifetime prognosis

Battery lifetime prognosis is a key requirement for successful market introduction of rechargeable Energy Storage Systems (ESS) based on lithium-ion (Li-ion) technology. In order to make decisions at the system design stage, a procedure for making efficient predictions of battery performance over time is necessary to be developed. In this paper, a general methodology for the evaluation of lifetime prediction is presented, covering the semi-empirical aging model precision and validity. Both calendar-life and cycle-life performance were investigated. Moreover, standing time and working operation were examined jointly using realistic operating profiles. The aim was the predictive model to be suitable for any application, including electric vehicle (EV), within the considered operating range. The efforts were especially focused on model ratification procedures and predictions goodness evaluation. The validation processes not only dealt with static impact factors evaluation but also with dynamic operation schemes. Besides, integration of ageing monitoring algorithm into Battery Management System (BMS) was evaluated. Battery pack design and operation strategies definition criteria were also discussed based on the stress factors influence on cell performance. The presented results correspond to a lithium iron phosphate (LFP) cathode 26650-size Li-ion cell.

[1]  Andreas Jossen,et al.  Optimierung der Regelung zwischen Brennstoffzelle und Batterie in Bezug auf KomponentenalterungOptimization of Fuel Cell and Battery Control in Relation to Component Aging of Fuel Cell Power Train , 2009, Autom..

[2]  Jens Groot,et al.  State-of-Health Estimation of Li-ion Batteries: Cycle Life Test Methods , 2012 .

[3]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[4]  Kuan-Jung Chung,et al.  Accelerated Degradation Assessment of 18650 Lithium-Ion Batteries , 2012, 2012 International Symposium on Computer, Consumer and Control.

[5]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[6]  Ira Bloom,et al.  Statistical methodology for predicting the life of lithium-ion cells via accelerated degradation testing , 2008 .

[7]  Dirk Uwe Sauer,et al.  Cycle and calendar life study of a graphite|LiNi1/3Mn1/3Co1/3O2 Li-ion high energy system. Part A: Full cell characterization , 2013 .

[8]  M. Verbrugge,et al.  Aging Mechanisms of LiFePO4 Batteries Deduced by Electrochemical and Structural Analyses , 2010 .

[9]  Craig L. Schmidt,et al.  A Practical Longevity Model for Lithium-Ion Batteries: De-coupling the Time and Cycle-Dependence of Capacity Fade , 2006 .

[10]  Martin Cifrain,et al.  Design-of-Experiment and Statistical Modeling of a Large Scale Aging Experiment for Two Popular Lithium Ion Cell Chemistries , 2013 .

[11]  Heinz Wenzl,et al.  Life prediction of batteries for selecting the technically most suitable and cost effective battery , 2005 .

[12]  Ralph E. White,et al.  Solvent Diffusion Model for Aging of Lithium-Ion Battery Cells , 2004 .

[13]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[14]  Ira Bloom,et al.  Rate-based degradation modeling of lithium-ion cells , 2012 .

[15]  Heinz Wenzl,et al.  Degradation of Lithium Ion Batteries under Complex Conditions of Use , 2012 .

[16]  M. Safari,et al.  Aging of a Commercial Graphite/LiFePO4 Cell , 2011 .

[17]  Frieder Herb,et al.  Alterungsmechanismen in Lithium-Ionen-Batterien und PEM-Brennstoffzellen und deren Einfluss auf die Eigenschaften von daraus bestehenden Hybrid-Systemen , 2010 .