Particle Filtering based Estimation of Remaining Useful Life of Lithium-ion Batteries Employing Power Fading Data

被引：0

作者：

Guha, Arijit ^{[1
]}

Patra, Amit ^{[1
]}

机构：

[1] Indian Inst Technol Kharagpur, Dept Elect Engn, Kharagpur, W Bengal, India

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM) | 2017年

关键词：

lithium-ion batteries; power fading; particle filter; remaining useful life; state of health; CAPACITY FADE; PROGNOSTICS; STATE; CHARGE;

D O I：

暂无

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

This paper presents a method for remaining useful life (RUL) estimation of lithium-ion batteries based on its power fading model. Firstly, an empirical model of power fading is developed based on battery test data. Then, the obtained model has been used in a particle filtering (PF) framework for making end of life (EOL) predictions at various stages of its cycle life. Finally, the predictions were validated with battery power fade data. From the results it can be observed that as more volume of data becomes available, the accuracy of prediction gradually improves. The prognostics framework proposed in this work provides a systematic way for monitoring the state of health (SoH) of a battery.

引用

页码：193 / 198

页数：6

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