A new method of accelerated life testing based on the Grey System Theory for a model-based lithium-ion battery life evaluation system

Abstract The lack of data samples is the main difficulty for the lifetime study of a lithium-ion battery, especially for a model-based evaluation system. To determine the mapping relationship between the battery fading law and the different external factors, the testing of batteries should be implemented to the greatest extent possible. As a result, performing a battery lifetime study has become a notably time-consuming undertaking. Without reducing the number of testing items pre-specified within the test matrices of an accelerated life testing schedule, a grey model that can be used to predict the cycle numbers that result in the specific life ending index is established in this paper. No aging mechanism is required for this model, which is exclusively a data-driven method obtained from a small quantity of actual testing data. For higher accuracy, a specific smoothing method is introduced, and the error between the predicted value and the actual value is also modeled using the same method. By the verification of a phosphate iron lithium-ion battery and a manganese oxide lithium-ion battery, this grey model demonstrated its ability to reduce the required number of cycles for the operational mode of various electric vehicles.

[1]  Gan Ning,et al.  Cycle Life Modeling of Lithium-Ion Batteries , 2004 .

[2]  Weijun Gu,et al.  Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications , 2012 .

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

[4]  Ralph E. White,et al.  Development of First Principles Capacity Fade Model for Li-Ion Cells , 2004 .

[5]  I. Bloom,et al.  Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes , 2011 .

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

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

[8]  Herbert L Case,et al.  Calendar- and cycle-life studies of advanced technology development program generation 1 lithium-ion batteries , 2002 .

[9]  Venkat Srinivasan,et al.  Discharge Model for the Lithium Iron-Phosphate Electrode , 2004 .

[10]  J. Newman,et al.  Monte Carlo Simulation of the Open-Circuit Potential and the Entropy of Reaction in Lithium Manganese Oxide , 2002 .

[11]  Balaji Krishnamurthy,et al.  A capacity fade model for lithium-ion batteries including diffusion and kinetics , 2012 .

[12]  Herbert L Case,et al.  An accelerated calendar and cycle life study of Li-ion cells. , 2001 .

[13]  J. Newman,et al.  Porous‐electrode theory with battery applications , 1975 .

[14]  Ralph E. White,et al.  A generalized cycle life model of rechargeable Li-ion batteries , 2006 .

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

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