Lifetime Prognosis of Lithium-Ion Batteries Through Novel Accelerated Degradation Measurements and a Combined Gamma Process and Monte Carlo Method

Abstract

A compositional prognostic-based assessment using the gamma process and Monte Carlo simulation was implemented to monitor the likelihood values of test Lithium-ion batteries on the failure threshold associated with capacity loss. The evaluation of capacity loss for the test LiFePO4 batteries using a novel dual dynamic stress accelerated degradation test, called D2SADT, to simulate a situation when driving an electric vehicle in the city. The Norris and Landzberg reliability model was applied to estimate activation energy of the test batteries. The test results show that the battery capacity always decreased at each measurement time-step during D2SADT to enable the novel test method. The variation of the activation energies for the test batteries indicate that the capacity loss of the test battery operated under certain power and temperature cycling conditions, which can be accelerated when the charge–discharge cycles increase. Moreover, the modeling results show that the gamma process combined with Monte Carlo simulations provides superior accuracy for predicting the lifetimes of the test batteries compared with the baseline lifetime data (i.e., real degradation route and lifetimes). The results presented high prediction quality for the proposed model as the error rates were within 5% and were obtained for all test batteries after a certain quantity of capacity loss, and remained so for at least three predictions.

Multiscale Modelling Methodologies of Lithium-Ion Battery Aging: A Review of Most Recent Developments

“…Rogers et al [107] modelled capacity evolution by simulating a random population of cells. Lin et al [108] presented a compositional prognostic-based model using the Monte Carlo simulation and a gamma process to observe the likelihood of test batteries failing, based on defined failure thresholds.…”

Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms

“…The data-driven model mainly relies on the Wiener or Gamma stochastic process to determine the performance degradation routes for devices under test (DUTs). [8][9][10][11][12][13] The lifetime of a product is defined as the time when the specific value of the product first passes the critical first passage time. Machine learning (ML)-based data-driven models have been applied to predict lifetimes and monitor the SOH of critical products.…”