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

作　　者：柳明贤 李继标 唐炳南 杨毅 肖仁鑫[3] LIU Mingxian;LI Jibiao;TANG Bingnan;YANG Yi;XIAO Renxin(Lijiang Power Supply Bureau of Yunnan Power Grid Co.Ltd,Lijiang 674100,Yunnan,China;Lijiang Old Town Power Supply Bureau of Yunnan Power Grid Co.Ltd,Lijiang 674199,Yunnan,China;Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,Yunnan,China)

机构地区：[1]云南电网有限责任公司丽江供电局,云南丽江674100 [2]云南电网有限责任公司丽江古城供电局,云南丽江674199 [3]昆明理工大学交通工程学院,云南昆明650500

出　　处：《储能科学与技术》2024年第5期1688-1698,共11页Energy Storage Science and Technology

基　　金：中国南方电网有限责任公司科技项目(YNKJXM20220216)。

摘　　要：可穿戴式设备(wearable devices,WDs)体积小、工作时间长,在工业监测等领域应用越来越广泛。锂离子电池为WD上电子设备提供能量,其准确的能量状态(state of energy,SOE)在线估算对WDs的电源实时管理与延长设备寿命有重要影响。传统的基于模型的估算方法需要离线获取SOE与开路电压(open circuit voltage,OCV)的关系,实验时间长,不能适应实际工况变化,难以在线实施,本工作提出一种基于开路电压(opencircuit voltage,OCV)在线辨识的可穿戴式设备用锂离子电池SOE在线估算方法。首先基于锂离子电池的一阶RC模型,采用带遗忘因子的递推最小二乘法(forgetting factor recursive least squares,FFRLS)在线辨识电池OCV等参数。分析了WDs运行负载变化特征,构建了WD运行工况和参数辨识工况,并开展锂离子电池实验。结合WDs工作负载特性,研究了开路电压和端电压的关系,在线获得OCV与SOE的关系曲线。采用无迹卡尔曼滤波(adaptive unscented Kalman filter,AUKF)算法实现SOE的在线估计,与传统通过离线实验获得OCVSOE关系的方法进行了对比。研究结果表明,所提的SOE在线估算方法具有较好的精度,并在不同的SOE初始值时具有较好的鲁棒性。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 

分 类 号：TM912[电气工程—电力电子与电力传动]