电化学储能系统中磷酸铁锂电池荷电状态估计研究
Research on State of Charge Estimation of Lithium Iron Phosphate Batteries in Electrochemical Energy Storage Systems
张可信 1胡凯 1芦勃睿 1苏楠 1白雪杰1
作者信息
- 1. 平高集团储能科技有限公司,天津 300300
- 折叠
摘要
常规的磷酸铁锂电池荷电状态估计观测方程被设定为问题点位测定,其估计范围受限,导致最终得出的荷电状态估计稳态误差较大,为此,提出电化学储能系统中磷酸铁锂电池荷电状态估计研究.根据当前的荷电状态估计需求,先计算SOC估计补偿系数,再采用多阶的方式,打破估计范围的限制,建立多阶观测方程.以此为基础,设计深度神经网络磷酸铁锂电池荷电状态估计模型,并采用OCV核验修正实现状态估计.经过 4 个测试周期的分析,在0.1 s、0.3 s和0.5s3个放电时间背景下,磷酸铁锂电池最终得出的荷电状态估计稳态误差都被控制在 0.4 以下,且泛化能力明显提高,表明该方法更高效、具体,实用性强.
Abstract
The conventional observation equation for estimating the state of charge of lithium iron phosphate batteries is set as a problem point measurement,and its estimation range is limited,resulting in a large steady-state error in the final state of charge estimation.Therefore,a study on estimating the state of charge of lithium iron phosphate batteries in electrochemical energy storage systems is proposed.Based on the current demand for state of charge estimation,first calculate the compensation coefficient for SOC estimation,and then use a multi-step approach to break the limitation of estimation range and establish a multi-step observation equation.Based on this,a deep neural network model for estimating the state of charge of lithium iron phosphate batteries is designed,and OCV verification correction is used to achieve state estimation.After four testing cycles of analysis,under three discharge time backgrounds of 0.1 s,0.3 s,and 0.5 s,the steady-state error of the estimated state of charge of lithium iron phosphate batteries was controlled below 0.4,and the generalization ability was significantly improved,indicating that this method is more efficient,specific,and practical.
关键词
电化学/储能系统/磷酸铁锂电池/电池荷电/荷电状态Key words
electrochemistry/energy storage systems/lithium iron phosphate batteries/battery charge/SOC引用本文复制引用
出版年
2024
NETL
NSTL
万方数据