Adaptive Battery Diagnosis/Prognosis for Efficient Operation

被引：3

作者：

Kim, Eugene ^{[1
]}

Wu, Bin ^{[1
]}

Shin, Kang ^{[1
]}

Lee, Jinkyu ^{[2
]}

He, Liang ^{[3
]}

机构：

[1] Univ Michigan, Ann Arbor, MI 48109 USA

[2] Sungkyunkwan Univ, Suwon, South Korea

[3] Univ Colorado, Denver, CO 80202 USA

来源：

E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS | 2019年

关键词：

LITHIUM-ION BATTERIES; MODEL; DEGRADATION; MANAGEMENT; PROGNOSTICS;

D O I：

10.1145/3307772.3328286

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Since most modern mobile and electric vehicles are powered by Lithium-ion batteries, they need advanced power management that jointly considers battery performance and related electrochemical reactions. To meet these needs, we develop a charging diagnosis and prognosis system for effective battery control. We first examine the battery model that can capture electrochemical reactions and battery performance. Based on this model, we develop a charging diagnosis system that determines battery internal states and predicts their trend over operational cycles in various environments. Next, we propose a prognosis system to estimate battery's end-of-life (EOL) and determine optimal operating environments. Our in-depth experiments demonstrate that the proposed diagnosis and prognosis system estimates battery parameters and their degradation over operational cycles in various environments with high accuracy and without degrading user-perceived experience.

引用

页码：150 / 159

页数：10

共 23 条

[1] Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications [J].