Department: Institute of Statistics and Data Science            PI's Name: Nan-Jung Hsu and Sheng-Tsaing Tseng

Journal: The Annals of Applied Statistics

Title: A Multi-Run Step-Stress Model for Trend Renewal Data With Applications to Lifetime Assessment for Rechargeable Batteries

Abstract: Conducting a cost-efficient lifetime-testing plan to timely assess lifetime information of a highly reliable product is often a challenging task in the manufacturing industry. Motivated by a case study of rechargeable lithium-ion batteries, this paper introduces a multi-run k-level step-stress experiment, running under different stresses in repeated cycles, to collect and analyze the degradation data of reusable highly-reliable products. Specifically, we formulate the battery capacity over recharge cycles as a counting process and adopt a trend renewal process (TRP) to characterize the degradation patterns of capacity varying with the stress level of the accelerated factor. By using a Markovian property on cumulative exposure in our counting process, the degradation data observed in a multi-run k-level step-stress TRP model can be converted equivalently to corresponding k constant-stress TRP models. This connection allows us to estimate the parameters using maximum likelihood and to infer with uncertainty quantification the end-of-performance of batteries at normal-use conditions. This novel method is shown to be efficient for the lifetime assessment of reusable products.