摘要
锂电池的安全高效运行依赖于准确的荷电状态(SOC)估计,但是传统的电池模型和SOC协同估计在噪声干扰下的鲁棒性和可靠性较差。针对噪声干扰下SOC协同估计问题,首先对电池的最大可用容量和电池开路电压(OCV)特性进行分析,研究了锂电池SOC—OCV的曲线特性。然后研究了噪声干扰下的在线模型参数辨识和SOC估计问题,提出了基于自适应动态滑动窗口的双粒子群协同优化参数辨识(TCPSO)方法,通过实验验证了所提方法的SOC估计最大误差小于1%,表明所提方法可实现在线参数辨识,并且在抗噪性能和SOC估计精度等方面均优于现有协同估计方法。
The safe and efficient operation of lithium batteries depends on accurate state of charge(SOC)estimation.However,the traditional battery model and SOC estimation have poor robustness and reliability under noise interference.Aiming at the problem of SOC cooperative estimation under noise interference,firstly,the maximum available capacity and open circuit voltage(OCV)characteristics of the battery were analyzed,and the curve characteristics of lithium battery SOC—OCV were studied.Then,the problem of online model parameter identification and SOC estimation under noise interference was studied,and a two-swarm cooperative particle swarm optimization(TCPSO)method based on adaptive dynamic sliding window was proposed.Experimental results show that the maximum SOC estimation error of the proposed method is less than 1%,which shows that the proposed method can realize online parameter identification,and it is superior to the existing collaborative estimation methods in terms of anti-noise performance and SOC estimation accuracy.
作者
朱业
陈渊睿
陈阳
王镇霖
ZHU Ye;CHEN Yuanrui;CHEN Yang;WANG Zhenlin(School of Electric Power,South China University of Technology,Guangzhou 510641,Guangdong,China;School of Intelligent Science and Engineering,Harbin Engineering University,Harbin 150000,Heilongjiang,China;School of Electronic Engineering,South China Agricultural University,Guangzhou 510642,Guangdong,China)
出处
《电气传动》
2024年第2期12-20,64,共10页
Electric Drive
基金
广东省自然科学基金-面上项目(2023A1515010184)。
关键词
荷电状态估计
噪声干扰
参数辨识
双粒子群协同优化参数辨识
state of charge(SOC)estimation
noise interference
parameter identification
two-swarm cooperative particle swarm optimization(TCPSO)
