摘要
低采样频率下锂离子电池的健康状态(SOH)评估在工程应用中具有重要的意义。引入了区域电压(ΔV)和区域采样点数(RSP)的概念,提出了一种在概率密度函数(PDF)框架下的锂离子电池SOH评估方法。以实验室磷酸铁锂(LFP)电池循环老化数据为基础,建立了基于RSP的电池SOH评估模型,对比了基于RSP的方法和传统PDF法,并考察了不同采样频率和区域电压下RSP-SOH模型的效果。研究结果表明,RSP与SOH呈线性正相关,RSPSOH模型无论是充电还是放电过程下的效果均优于传统PDF法。在采样频率较低时,适当的增加区域电压有助于提高RSP-SOH模型的评估效果。电池RSP-SOH模型在充电过程下对采样频率鲁棒性较好,在每五分钟一个采样点的低采样频率情况下模型的R^(2)均大于0.98。在此基础上,采用区域采样点数法相对评估某储能电站的220个LFP电池的SOH。当9个RSP较小的电池被替换掉后,储能电站电能的吞吐能力将提升20.9%。
The state of health(SOH)estimation of lithium-ion batteries at a low sampling frequency has great significance in engineering applications.The concepts of regional voltage(ΔV)and regional sampling points(RSP)are introduced,and an evaluation method of lithium-ion battery SOH under the framework of probability density function(PDF)is proposed.A battery SOH evaluation model based on RSP was established based on the laboratory cycle ageing data of lithium-iron phosphate(LFP)batteries.The RSP method and the traditional PDF method were compared,and the effects of the RSP-SOH models under different sampling frequencies and regional voltages were investigated.The results show that the RSP-SOH model has a linear positive correlation with SOH,and the effect of RSP-SOH model is better than that of the traditional PDF method under both charging and discharging conditions.The evaluation effect of the RSP-SOH model can be improved by increasing the region voltage appropriately when the sampling frequency is low.The battery RSP-SOH model is robust to the sampling frequency under the charging condition,and the R^(2) of the model is greater than 0.98under the low sampling frequency of one sampling point every five minutes.On this basis,the SOH of 220LFP batteries in an energy storage power station is relatively evaluated by using the regional sampling point method.When nine batteries with smaller RSP are replaced,the power handling capacity of the energy storage station will increase by 20.9%.
作者
林志成
钟晶亮
文贤馗
刘思迪
付在国
廖强强
Lin Zhicheng;Zhong Jingliang;Wen Xiankui;Liu Sidi;Fu Zaiguo;Liao Qiangqiang(Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power,Shanghai University of Electric Power,Shanghai 200090,China;Guizhou Power Grid Co.,Ltd.,Electric Science Research Institute,Guiyang 550002,China)
出处
《电子测量技术》
北大核心
2023年第4期12-18,共7页
Electronic Measurement Technology
基金
贵州省科技支撑计划(黔科合支撑[2020]2Y064)
上海市科委项目(19DZ2271100)资助
关键词
锂离子电池
健康状态
概率密度函数
区域电压
区域采样点数
采样频率
lithium-ion battery
state of health
probability density function
regional voltage
regional sampling points
sampling frequency
