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基于连续-离散校正策略的锂离子电池组温度区间估计方法

宋晓娜,  郑丹菁,  宋帅,  冯运

宋晓娜, 郑丹菁, 宋帅, 冯运. 基于连续-离散校正策略的锂离子电池组温度区间估计方法. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260101
引用本文: 宋晓娜, 郑丹菁, 宋帅, 冯运. 基于连续-离散校正策略的锂离子电池组温度区间估计方法. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260101
Song Xiao-Na, Zheng Dan-Jing, Song Shuai, Feng Yun. Temperature interval estimation approach for lithium-ion battery packs: a continuous-discrete correction strategy. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260101
Citation: Song Xiao-Na, Zheng Dan-Jing, Song Shuai, Feng Yun. Temperature interval estimation approach for lithium-ion battery packs: a continuous-discrete correction strategy. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260101

基于连续-离散校正策略的锂离子电池组温度区间估计方法

doi: 10.16383/j.aas.c260101 cstr: 32138.14.j.aas.c260101
基金项目: 国家自然科学基金(62473130, 62573178), 河南省自然科学基金(卓越青年创新研究群体项目) (252300421004)资助
详细信息
    作者简介:

    宋晓娜:河南科技大学信息工程学院(人工智能学院)教授. 2011年获得南京理工大学控制科学与工程专业博士学位. 主要研究方向包括反应扩散系统, 柔性系统的自适应与学习控制, 无人集群系统的智能控制. E-mail: xiaona_97@163.com

    郑丹菁:河南科技大学信息工程学院(人工智能学院)博士研究生. 2025年获得河南科技大学信息工程学院(人工智能学院)硕士学位. 主要研究方向为分布参数系统的控制与优化. E-mail: zhengdanjing1999@163.com

    宋帅:河南科技大学信息工程学院(人工智能学院)副教授. 2021年获得南京理工大学控制科学与工程专业博士学位. 主要研究方向包括非线性系统的自适应与学习控制, 无人系统的协同控制与优化. 本文通信作者. E-mail: songshuai_1010@163.com

    冯运:湖南大学人工智能与机器人学院教授, 机器人视觉感知与控制技术国家工程研究中心研究员. 2020年获得香港城市大学系统工程与工程管理系博士学位. 主要研究方向为机器人数字孪生, 软体机器人建模与控制. E-mail: fyrobot@hnu.edu.cn

    通讯作者:

    宋帅  河南科技大学信息工程学院(人工智能学院)副教授. 2021年获得南京理工大学控制科学与工程专业博士学位. 主要研究方向包括非线性系统的自适应与学习控制, 无人系统的协同控制与优化. 本文通信作者. E-mail: songshuai_1010@163.com

  • 中图分类号: Y

Temperature Interval Estimation Approach for Lithium-ion Battery Packs: A Continuous-Discrete Correction Strategy

Funds: Supported by National Natural Science Foundation of China (62473130, 62573178), Natural Science Foundation of Henan Province (Outstanding Youth Innovation Research Group Project) (252300421004)
More Information
    Author Bio:

    SONG Xiao-Na Professor at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. She received her Ph.D. degree in control science and engineering from Nanjing University of Science and Technology in 2011. Her research interests include reaction-diffusion systems, adaptive and learning control of flexible systems, intelligent control of unmanned swarm systems

    ZHENG Dan-Jing Ph.D. candidate at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. She received her master degree at the School of Information Engineering (School of Artificial Intelligence) from Henan University of Science and Technology in 2025. Her main research interest is the control and optimization of distributed parameter systems

    SONG Shuai Associate professor at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. He received his Ph.D. degree in control science and engineering from Nanjing University of Science and Technology in 2021. His research interests include adaptive and learning control for nonlinear systems, cooperative control and optimization of unmanned systems. Corresponding author of this paper

    FENG Yun Professor at the School of Artificial Intelligence and Robotics, Hunan University, researcher at the National Engineering Research Center of Robot Vision Perception and Control Technology. He received his Ph.D. degree in the Department of Systems Engineering and Engineering Management from City University of Hong Kong in 2020. His research interests include robot digital twins and soft robot modeling and control

    Corresponding author: SONG Shuai    Associate professor at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. He received his Ph.D. degree in control science and engineering from Nanjing University of Science and Technology in 2021. His research interests include adaptive and learning control for nonlinear systems, cooperative control and optimization of unmanned systems. Corresponding author of this paper
  • 摘要: 针对复杂非线性、域内扰动、测量噪声、环境温度变化及初始条件等不确定因素影响下的锂离子电池组, 提出一种基于偏微分方程模型的区间观测器来以上下界的形式反映电池组内部温度. 首先, 将电池组视为一个温度由内向外呈放射状分布的圆柱形结构电池, 基于其轴对称结构以及径向为主导的热传导行为, 建立描述其热行为的一维径向偏微分方程模型. 然后, 采用空间点测量与时间非周期离散测量相结合的方式获取电池组表面温度数据, 并基于各类未知不确定参数的已知上下界, 设计基于连续-离散混合系统的区间观测器, 引入一个辅助计时器变量以驱动连续系统与离散系统之间的切换, 确保在测量时刻及时校正估计误差, 有效缩小估计区间宽度. 进一步, 构造包含函数时间导数与负部分函数的方程, 通过分析负函数部分为零和非负离散时间系统理论证明了估计误差的非负性, 在此基础上建立包含计时器的Lyapunov函数, 结合递推归纳法得到估计误差有界的充分条件. 最后, 以3并3串锂电池组为例, 仿真验证本文所提区间观测器的有效性和优越性.
  • 图  1  热电偶传感器植入电池内部示意图

    Fig.  1  Schematic diagram of thermocouple sensor implanted inside battery

    图  2  锂离子电池组温度分布图

    Fig.  2  Temperature distribution diagram of lithium-ion battery packs

    图  3  3并3串电池组的平面布局示意图

    Fig.  3  Schematic diagram of the planar layout of 3-parallel-3-series battery packs

    图  4  电池组温度$T$以及区间观测器的动态演化轮廓

    Fig.  4  The dynamic evolution profiles of battery packs temperature $T$ and interval observer

    图  5  电池组温度$T$以及区间观测器在不同空间位置下的动态演化轮廓

    Fig.  5  The dynamic evolution profiles of battery packs temperature $T$ and interval observer at different spatial locations

    图  6  不同观测器下不同空间位置的观测结果

    Fig.  6  Estimation results for different spatial locations under different observers

    表  1  18650圆柱形电池的模型参数

    Table  1  Model parameters for the 18650 cylindrical battery

    符号 参数值 单位
    $T_{ref}$ 295.150 K
    $T_{\infty}$ $298.150-\cos(\pi t)$ K
    $E_{a}$ 33.800 KJ/mol
    $R_{g}$ 8.314 J/(mol K)
    $k_{p}$ 0.980 W/(m K)
    $\rho_{p}$ 2118 $\text{kg/m}^3$
    $C_{p}$ 711 J/(kg K)
    $h_{p}$ 69.890 $\text{W/(m}^2{\text{ K)}}$
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