Abstract
The usage of lithium-ion batteries has become widespread across various applications, such as electronic devices, electric vehicles, and integrated renewable energy systems. Hence, there is a pressing need to enhance analytical techniques to optimize their performance and prolong their operational lifespan. This study introduces an innovative methodology that utilizes higher-order spectral analysis (HOSA) for evaluating data collected from lithium-ion batteries. The objective is to capture the inherent nonlinear and non-stationary behaviors displayed by battery data. The proposed approach mainly uses Bispectrum and Bicoherence to establish hypothesis tests for nonlinearity and non-Gaussianity of data. Through a customized algorithm, the higher-order spectrum of these refined data is calculated, uncovering intricate characteristics that may go unnoticed by conventional analytical methods. These findings have a wide range of potential applications, including battery modeling, fault detection, state-of-charge estimation, state-of-health estimation and the enhancement of battery performance. Finally, an experiment is conducted to validate the effectiveness of the method.
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