Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model
Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model
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DOI：
10.1109/tcst.2018.2838097
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发表时间：
2019-09-01
影响因子：
4.8
通讯作者：
Howey, David A.
中科院分区：
文献类型：
--
作者：
Bizeray, Adrien M.;Kim, Jin-Ho;Howey, David A.
研究了用于锂离子电池仿真的单粒子模型(SPM)的可辨识性和参数估计问题。可辨识性在原则上和实践中都有论述。该方法首先对参数进行分组，并对SPM进行部分无量纲化，以确定问题中的最大预期自由度。我们发现，不包括开路电压(OCV)，只有六个独立参数。然后，我们通过考虑线性化后的SPM的传递函数是否唯一来检验结构可辨识性。研究发现，只要电极OCV函数具有已知的非零梯度，参数是有序的，且电极动力学集中于单个电荷转移电阻参数，则该模型是唯一的。然后，我们演示了从测量的频域实验电化学阻抗谱数据对模型参数的实际估计，并进一步表明，参数化模型在时间域上提供了良好的预测能力，在10min的动态放电中，模型与实验之间的最大电压误差为20 mV。
This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially nondimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that excluding open-circuit voltage (OCV), there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearized SPM is unique. It is found that the model is unique provided that the electrode OCV functions have a known nonzero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge-transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy data, and show additionally that the parametrized model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between the model and the experiment over a 10-min dynamic discharge.