A Reduced-Order Electrochemical Model for All-Solid-State Batteries
A Reduced-Order Electrochemical Model for All-Solid-State Batteries
复制标题
全固态电池的降阶电化学模型
DOI：
10.1109/tte.2020.3026962
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发表时间：
2021-06
影响因子：
7
通讯作者：
Wenchao Guo
中科院分区：
文献类型：
--
作者：
Zhongwei Deng;Xiaosong Hu;Xianke Lin;Le Xu;Jiacheng Li;Wenchao Guo
全固态电池（ASSB）被认为是下一代锂离子电池。基于物理的模型具有提供内部电化学信息的优点。为了促进基于物理的模型在实时应用中的应用，在本研究中，应用了一系列模型简化方法来获得ASSB的降阶模型（ROM）。首先，通过拉普拉斯变换导出偏微分方程（PDE）的解析解。然后，采用Padé近似方法将超越传递函数转换为低阶分数传递函数。接下来，分别用抛物线和三次函数来近似电极和电解质中的浓度分布。由于实时快速计算浓度分布，现在可以直接计算平衡电位、过电位和电池电压。与原始基于 PDE 的模型相比，所提出的 ROM 的电压误差小于 2.6 mV。与实验数据的电压响应相比，ROM在三种大倍率放电条件下可以观察到良好的一致性。 ROM每步的计算时间在0.2ms以内，这意味着它可以集成到电池管理系统中。所提出的 ROM 实现了优异的性能以及模型保真度和计算复杂度之间的更好权衡。
All-solid-state batteries (ASSBs) have been considered as the next generation of lithium-ion batteries. Physics-based models have the advantage of providing internal electrochemical information. To promote physics-based models in real-time applications, in this study, a series of model reduction methods are applied to obtain a reduced-order model (ROM) for ASSBs. First, analytical solutions of the partial differential equations (PDEs) are derived by the Laplace transform. Then, the Padé approximation method is used to convert the transcendental transfer functions into lower order fractional transfer functions. Next, the concentration distributions in electrodes and electrolytes are approximated by parabolic and cubic functions, respectively. Due to the fast calculation of concentration distributions in real time, the equilibrium potential, overpotentials, and battery voltage can now be directly calculated. Compared with the original PDE-based model, the voltage errors of the proposed ROM are less than 2.6 mV. Compared with the voltage response of experimental data, a good agreement can be observed for the ROM under three large C-rates discharging conditions. The calculation time of ROM per step is within 0.2 ms, which means that it can be integrated into a battery management system. The proposed ROM achieves excellent performance and a better tradeoff between model fidelity and computational complexity.
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