Accelerated Degradation Analysis for the Quality of a System Based on the Gamma Process

2015 ◽  
Vol 64 (1) ◽  
pp. 463-472 ◽  
Author(s):  
Man Ho Ling ◽  
Kwok Leung Tsui ◽  
Narayanaswamy Balakrishnan
Keyword(s):  
Gamma Process ◽  
Accelerated Degradation ◽  
Degradation Analysis

A Wiener process model for accelerated degradation analysis considering measurement errors

2016 ◽  
Vol 65 ◽  
pp. 8-15 ◽  
Author(s):  
Junxing Li ◽  
Zhihua Wang ◽  
Xia Liu ◽  
Yongbo Zhang ◽  
Huimin Fu ◽  
...  
Keyword(s):  
Wiener Process ◽  
Measurement Errors ◽  
Process Model ◽  
Accelerated Degradation ◽  
Degradation Analysis

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

2019 ◽  
Vol 9 (3) ◽  
pp. 559 ◽  
Author(s):  
Yu-Chang Lin ◽  
Kuan-Jung Chung
Keyword(s):  
Monte Carlo ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Temperature Cycling ◽  
Test Method ◽  
Gamma Process ◽  
Time Step ◽  
Accelerated Degradation ◽  
Capacity Loss ◽  
Battery Capacity

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.

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