For battery equivalent circuit model parameter identification, the fractional-order modeling and the bionic algorithm are two excellent techniques. The former can describe the impedance characteristics of batteries accurately, while the latter has natural advantages in solving some nonlinear problems. However, the high computational cost limits their application. In this article, a parameter-identification method for a battery fractional-order model based on the coevolutionary particle swarm optimization (CPSO) is proposed. In this algorithm, a large number of optimization calculations are dispersed between the adjacent sampling times in the form of evolutionary steps by CPSO, so the algorithm can run in real time with the sampling process. In addition, the simplified fractional approximation further reduces the computational cost. By conducting tests under various algorithm conditions, we evaluate the main factors affecting the algorithm performance in detail. Our results show that compared with the integer-order model, the fractional-order model can track the optimal value more effectively in a wider optimization space, CPSO can track the time-varying battery parameters in real time by continuous evolution, and computational costs can be effectively reduced by using a fixed-order fractional-order model and appropriately compressing the length of the historical data required for fractional-order computation.

中文翻译：

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用于锂离子电池分数阶建模的基于 CPSO 的参数识别方法

对于电池等效电路模型参数识别，分数阶建模和仿生算法是两种优秀的技术。前者可以准确描述电池的阻抗特性，而后者在解决一些非线性问题方面具有天然优势。然而，高计算成本限制了它们的应用。在本文中，提出了一种基于协同进化粒子群优化（CPSO）的电池分数阶模型的参数识别方法。在该算法中，大量的优化计算通过CPSO以进化步的形式分散在相邻采样时间之间，因此算法可以随着采样过程实时运行。此外，简化的分数近似进一步降低了计算成本。通过在各种算法条件下进行测试，我们详细评估了影响算法性能的主要因素。我们的结果表明，与整数阶模型相比，分数阶模型可以在更广阔的优化空间中更有效地跟踪最优值，CPSO 可以通过持续进化实时跟踪时变电池参数，并且计算成本可以通过使用定阶分数阶模型，并适当压缩分数阶计算所需的历史数据长度，可以有效地减少。