首页|基于热电耦合模型和AUKF的锂电池内温状态估算

基于热电耦合模型和AUKF的锂电池内温状态估算

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内温状态(SOIT)可用于锂(Li)电池热失控风险评估以及提高荷电状态/可用性能剩余比例(SOC/SOH)估值精准度.在分析锂电池等效电路模型和热模型的基础上,提出了一种反映锂电池内温升高并对SOC估算形成噪声影响的双重极化热电耦合模型.通过实施恒流放电和混合脉冲功率特性(HP-PC)实验并引入带遗忘因子的递推最小二乘法,分别得到锂电池等效电路模型、等效热模型辨识参数.为应对热噪声,提出将无迹卡尔曼滤波(UKF)与自适应协方差匹配相结合为自适应UKF(AUKF),在SOIT估算中,完成过程噪声协方差、测量噪声协方差的在线修正.实验结果证明,当外界因素引发锂电池内部电流电压波动加剧时,AUKF在抑制SOIT估值误差趋大方面明显优于UKF.
SOIT estimation of Li battery based on thermo-electric coupling model and AUKF
State of internal temperature(SOIT)can be used to assess the risk of thermal runaway and improve the accuracy of state of charge(SOC)and state of health(SOH)estimation of lithium(Li)battery.On the basis of analysis of the equivalent circuit model and thermal model of Li battery,a dual polarization thermo-electric coupling model is proposed to reflect the internal temperature rise and noise influence during SOC estimation of Li battery.The experiments of constant current discharge and hybrid pulse power characteristics(HPPC)and the recursive least square method with forgetting factor are applied to obtain identification parameters of equivalent circuit model and thermal model of Li battery,respectively.In order to deal with thermal noise,adaptive unscented Kalman filtering(AUKF)is proposed by combining UKF with adaptive covariance matching,which completes the online correction of process noise covariance and measurement noise covariance during SOIT estimation.The experimental results verify that when the internal current and voltage fluctuations of Li battery are intensified due to external factors,AUKF is significantly prior to UKF in restraining the SOIT estimation error increasing.

lithium(Li)batterystate of internal temperature(SOIT)estimationthermo-electric couplingdual polarizationadaptive unscented Kalman filtering(AUKF)

张峰凡、张良力、刘江

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武汉科技大学信息科学与工程学院,湖北武汉 430081

武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉 430081

锂电池 内温状态估算 热电耦合 双重极化 自适应无迹卡尔曼滤波

湖北省自然科学基金资助项目

2021CFB030

2024

传感器与微系统
中国电子科技集团公司第四十九研究所

传感器与微系统

CSTPCD北大核心
影响因子:0.61
ISSN:1000-9787
年,卷(期):2024.43(3)
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