Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification
Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification
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通过快速、无损的参数识别，实现锂离子电池的高保真电化学 P2D 建模
DOI：
10.1016/j.ensm.2021.12.044
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发表时间：
2021-12
影响因子：
20.4
通讯作者：
Xiaosong Hu
中科院分区：
文献类型：
--
作者：
Le Xu;Xianke Lin;Yi Xie;Xiaosong Hu
基于物理的电化学模型提供了对电池内部状态的洞察，并在电池设计优化以及汽车和航空航天应用中显示出巨大的潜力。然而，由于电化学模型的复杂性，很难得到准确的参数值。在这项研究中，提出了一种新的非破坏性参数识别方法来对最常用的电化学准二维模型进行参数化。整个鉴定过程由三个关键步骤组成。首先对模型参数的灵敏度进行了分析，根据最敏感的条件将参数分为三类，找出最优的辨识条件。其次，利用深度学习算法得到这些未知参数的可行初值，不仅可以避免辨识算法的发散问题，而且可以加快后续的辨识过程。最后，结合两种不同的方法进行参数辨识，对灵敏度较高的参数进行分步估计。我们利用模拟和实验数据，在1h内可以准确地估计出14个电化学参数。对模型参数进行估计后，模型预测电压的均方根误差小于14 mV。
Physics-based electrochemical models provide insight into the battery internal states and have shown great potential in battery design optimization and automotive and aerospace applications. However, the complexity of the electrochemical model makes it difficult to obtain parameter values accurately. In this study, a novel non-destructive parameter identification method is proposed to parameterize the most commonly used electrochemical pseudo-two-dimensional model. The whole identification process consists of three key steps. First, in order to find the optimal identification conditions, the sensitivity of model parameters is analyzed, and parameters are classified into three types according to their most sensitive conditions. Second, feasible initial guess values of these unknown parameters are obtained using a deep learning algorithm, which can not only help avoid the divergence problem of the identification algorithm but also speed up the subsequent identification process. Finally, two different approaches are combined and used for parameter identification, and parameters that have high sensitivity are estimated in a step-wise manner. We show that 14 electrochemical parameters can be estimated accurately within 1 h using simulation and experimental data. After estimating the model parameters, the root-mean-square error of the predicted voltage from the model is less than 14 mV.
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