Parameter estimation of the 2R-1C model is usually performed using iterative methods that require high-performance processing units. Consequently, there is a strong motivation to develop less time-consuming and more power-efficient parameter estimation methods. Such low-complexity algorithms would be suitable for implementation in portable microcontroller-based devices. In this study, we propose the quadratic interpolation non-iterative parameter estimation (QINIPE) method, based on quadratic interpolation of the imaginary part of the measured impedance, which enables more accurate estimation of the characteristic frequency. The 2R-1C model parameters are subsequently calculated from the real and imaginary parts of the measured impedance using a set of closed-form expressions. Comparative analysis conducted on the impedance data of the 2R-1C model obtained in both simulation and measurements shows that the proposed QINIPE method reduces the number of required measurement points by 80% in comparison with our previously reported non-iterative parameter estimation (NIPE) method, while keeping the relative estimation error to less than 1% for all estimated parameters. Both non-iterative methods are implemented on a microcontroller-based device; the estimation accuracy, RAM, flash memory usage, and execution time are monitored. Experiments show that the QINIPE method slightly increases the execution time by 0.576 ms (about 6.7%), and requires 24% (1.2 KB) more flash memory and just 2.4% (32 bytes) more RAM in comparison to the NIPE method. However, the impedance root mean square errors (RMSEs) of the QINIPE method are decreased to 42.8% (for the real part) and 64.5% (for the imaginary part) of the corresponding RMSEs obtained using the NIPE method. Moreover, we compared the QINIPE and the complex nonlinear least squares (CNLS) estimation of the 2R-1C model parameters. The results obtained show that although the estimation accuracy of the QINIPE is somewhat lower than the estimation accuracy of the CNLS, it is still satisfactory for many practical purposes and its execution time reduces to\({1 \over {45}} - {1 \over {30}}\).

2R-1C模型的参数估计通常使用需要高性能处理单元的迭代方法进行。因此，有强烈的动机去开发更省时，更省电的参数估计方法。这种低复杂度的算法将适合在基于便携式微控制器的设备中实现。在这项研究中，我们基于测量阻抗的虚部的二次插值，提出了二次插值非迭代参数估计（QINIPE）方法，从而可以更准确地估计特征频率。随后，使用一组闭式表达式从所测阻抗的实部和虚部计算2R-1C模型参数。对在仿真和测量中获得的2R-1C模型的阻抗数据进行的比较分析表明，与我们先前报告的非迭代参数估计（NIPE）方法相比，拟议的QINIPE方法将所需的测量点数减少了80％ ，同时将所有估算参数的相对估算误差均控制在1％以内。两种非迭代方法都在基于微控制器的设备上实现。监视估计准确性，RAM，闪存使用情况和执行时间。实验表明，与NIPE方法相比，QINIPE方法的执行时间略微增加了0.576 ms（约6.7％），并且需要增加24％（1.2 KB）的闪存和仅2.4％（32字节）的RAM。然而，QINIPE方法的阻抗均方根误差（RMSE）降低为使用NIPE方法获得的相应RMSE的42.8％（实部）和64.5％（虚部）。此外，我们比较了2R-1C模型参数的QINIPE和复数非线性最小二乘（CNLS）估计。所得结果表明，尽管QINIPE的估计精度略低于CNLS的估计精度，但对于许多实际目的还是令人满意的，并且其执行时间减少到\（{1 \ over {45}}-{1 \ over {30}} \）。