End of performance prediction of Lithium-ion battery and its optimum allocation design

• Main Authors: Wu, Jing Sian, 吳璟賢
• Other Authors: Tseng, Sheng Tsaing
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/47928869216310743712

碩士 === 國立清華大學 === 統計學研究所 === 104 === Rechargeable batteries are critical components for the performance of portable electronics and electric vehicles. The long term health performance of rechargeable batteries is characterized by state of health which can be quantified by end of performance (EoP). Focusing on EoP prediction, this thesis first proposed a trend renewal process (TRP) model to address this decision problem. Specifically, we derive an approximate formula for EoP and derive its 95% confidence interval. The proposed model is also applied to analyze a rechargeable battery dataset. Finally, we also use a simulation study to address the issue of the optimal design of TRP model, which includes the determinations of the test samples (units) and its corresponding measurement times. The results demonstrate that the prediction performance of the proposed procedure is very robust even when the process parameters in TRP model are not precisely estimated.