PDE estimation techniques for advanced battery management systems — Part II: SOH identification
PDE estimation techniques for advanced battery management systems — Part II: SOH identification
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先进电池管理系统的 PDE 估计技术 - 第 II 部分：SOH 识别
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Krstić
中科院分区：
文献类型：
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作者：
S. Moura;N. Chaturvedi;M. Krstić
电动汽车和可再生能源的一项关键使能技术是电池储能。先进的电池系统代表了这些应用的一种有前途的技术，但是它们的动力学受到相对复杂的电化学现象的影响，这些电化学现象的参数会随着时间的推移而退化，并且在材料设计中会有所不同。此外，存在有限的感测和致动来监测和控制这些系统的内部状态。因此，电池管理系统需要先进的识别、估计和控制算法。在本文中，我们研究状态的健康（SOH）估计，作为一个参数识别问题的抛物型偏微分方程和非线性参数化输出函数的框架。具体来说，我们利用交换识别方法的未知参数的扩散偏微分方程（PDE）。一个非线性最小二乘法应用于输出函数，以识别其未知参数。这些识别算法是从单粒子模型（SPM）合成的。在配套文件中，我们研究了一种新的电池荷电状态（SOC）估计算法的基础上的偏微分方程的反推方法。
A critical enabling technology for electrified vehicles and renewable energy resources is battery energy storage. Advanced battery systems represent a promising technology for these applications, however their dynamics are governed by relatively complex electrochemical phenomena whose parameters degrade over time and vary across material design. Moreover, limited sensing and actuation exists to monitor and control the internal state of these systems. As such, battery management systems require advanced identification, estimation, and control algorithms. In this paper we examine state-of-health (SOH) estimation, framed as a parameter identification problem for parabolic PDEs and nonlinearly parameterized output functions. Specifically, we utilize the swapping identification method for unknown parameters in the diffusion partial differential equation (PDE). A nonlinear least squares method is applied to the output function to identify its unknown parameters. These identification algorithms are synthesized from the single particle model (SPM). In a companion paper we examine a new battery state-of-charge (SOC) estimation algorithm based upon the backstepping method for PDEs.