Vector analysis of inverse objective function for electrochemical impedance spectroscopy of big capacity lead-acid battery. (August 2021) - Wang, Wubin; Chen, Dong; Yao, Wenxi; Chen, Wei; Lu, Zhengyu
Highlights: The vector objective function equivalently replaces the regular minimization of root-mean-square error . The linear interpolation algorithm simplifies the gradient descent direction search to a one-dimensional search. The theoretical boundary of the algorithm is the searching range. The proposed algorithm is verified by the Microcontroller at 4200 Ah VRLA batteries. Abstract: The important emergency power supply needs a large capacity battery. The batteries are long-term float charging. Electrochemical impedance spectroscopy (EIS) is important for remaining useful capacity warnings. This paper establishes the EIS inverse calculation technique that is centered in the vector objective function and linear interpolation search algorithm. This technique simplifies the initial value preparation and the gradient descent direction search for the inverse calculation of EIS. The root-mean-square errors of online embedded programming are smaller than the offline results of numerical software for EIS. The vector objective function targets the rotation angle of the initial polarization impedance vector to be zero during the EIS inverse calculation. This equivalently replaces the regular minimization of root-mean-square error. The linear interpolation algorithm simplifies the gradient descent direction search to a one-dimensional search for the double-layer capacitance of the initial polarization impedance. This algorithm replaces the conventional graphic method and evolutionary algorithm. The proposed algorithm is verified by the microcontroller at Valve-regulated lead-acid (VRLA) batteries (4200 Ah). The result of the algorithm is close to special numerical software for EIS. This algorithm improves the performance of an embedded management board.