储能科学与技术2024，Vol.13Issue(5)：1688-1698,11.DOI:10.19799/j.cnki.2095-4239.2023.0721
基于AUKF的可穿戴式设备用锂离子电池SOE在线估计方法
Online state-of-energy estimation method for lithium-ion batteries used in wearable devices based on adaptive unscented Kalman filter
摘要
Abstract
Wearable devices(WDs)with small sizes and long working time are widely used in industrial monitoring and other fields.Lithium-ion batteries provide energy for electronics used in WDs,and their online accurate estimation of state of energy(SOE)critically impacts the real-time power management and life extension of WDs.Traditional model-based estimation methods must obtain the offline relationship between SOE and open circuit voltage(OCV).However,this requires a large amount of test time and is challenging to adapt to actual working conditions,thus hindering its online application.This paper proposes an SOE estimation method based on the online identification of OCV for lithium-ion batteries used in WDs.Based on the first-order RC model of the battery,the forgetting factor recursive least squares is used to identify the OCV online and other parameters of the lithium-ion battery.After analyzing the characteristics of load changes in the WD operation,the working condition and parameter identification condition are constructed,and the experiments are conducted on the test bench.Combined with the workload characteristics of WDs,the relationship between OCV and terminal voltage is discussed,and the relationship curves between OCV and SOE are obtained online.The adaptive unscented Kalman filter is used to estimate the SOE online and is compared with the traditional method based on the offline OCV-SOE relationship.The results show that the proposed SOE estimation method based on OCV online identification has good accuracy and robustness against different initial values.关键词
可穿戴式设备/SOE估算/OCV在线辨识/自适应无迹卡尔曼滤波Key words
wearable device/SOE estimation/OCV online identification/adaptive unscented Kalman filter分类
信息技术与安全科学引用本文复制引用
柳明贤,李继标,唐炳南,杨毅,肖仁鑫..基于AUKF的可穿戴式设备用锂离子电池SOE在线估计方法[J].储能科学与技术,2024,13(5):1688-1698,11.基金项目
中国南方电网有限责任公司科技项目(YNKJXM20220216) （YNKJXM20220216）
