Modeling degradation of lithium-ion batteries considering cell-to-cell variations

Abstract

Battery packs exhibit both intrinsic cell-to-cell variations and spatio-temporal cell-to-cell differences in temperature and other stress factors, shaping the evolution of the degradation paths of the cells. To account for these variations and differences in degradation or cell spreading, we propose a statistical approach for modeling the degradation of lithium-ion batteries that utilizes a 3-parameter non-homogeneous Gamma process. This approach predicts the capacity fade or time-to-failure for any battery architecture and adjusts the distributions of the fitted degradation data of the cells with acceleration factors. At the pack level, cells are modeled with compositions of Gamma-distributed variables for configurations in parallel and series. Actual values of the capacity fade or time-to-failure at different thermal conditions are compared with predicted values, showing relative errors in the range 1 – 12%. We also present a methodology for estimating the minimum number of cells required for modeling the evolution of the spreading and degradation paths by analyzing the effect of the sample size on estimating the degradation for different sets of cells. This sampling strategy is particularly useful for reducing the computational cost of running simulations required for designing battery packs, battery management systems and battery thermal management systems.