In this proposal, we will discuss the Bayesian reliability inference for the degradation data of rechargeable lithium-ion batteries. Rechargeable batteries have become more and more popularly used due to the competitive advancement of the electronic equipment in recent decades. To study the long-term performance of the rechargeable batteries, more interests are in the remaining performance of a battery over its whole cycle. The current, voltage and capacity are measured during the repeatedly charging/discharging processes from a degradation test of the lithium-ion batteries.The periodic degradation data show similar patterns among discharging cycles but with decreasing trend. Traditional research in engineering usually uses filtering based on state spaces. Statistical modeling and analysis for the rechargeable batteries degradation data are getting more and more attention. Recently, Wang et al. (2019) proposed an accelerated testing version of the trend-renewal process (TRP) model based on the repairable system model to capture the battery capacity function which reveals a new direction in analyzing the lithium-ion battery data. Due to high reliability and high testing cost, experiments with small sample sizes are often encountered in degradation tests of lithium-ion batteries, thus Bayesian approach may provide more useful and accurate inference as an alternative. In this proposal, we consider the Bayesian reliability inference on the ultimate end of performance based on the degradation process test of lithium-ion battery data, by incorporating prior information through different but similar tests of lithium-ion batteries. In the first year, we will begin with the Bayesian analysis based on the accelerated trend renewal process model of Wang et al. (2019) with different trend functions and/or renewal distributions. By adjusting the hyperparameters of the prior distributions, we expect to obtain Bayesian inference for the end of performance of the lithium-ion battery. As the periodic degradation data show similar patterns among discharging cycles but with decreasing trend, in the second year, we shall analyze the data within each cycle and try to combine the models with empirical Bayes approach to come out with a more satisfactory result by means of the concept of "borrowing the strength". In the last year of this sub-project, we will focus on the Bayesian optimal designs, based on models established in the first two years, by determining the appropriate stress levels as well as sample size allocation to get the most accurate estimates of the end of performance in accelerated degradation tests of the lithium-ion batteries.
|Effective start/end date||1/08/21 → 31/07/22|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):