Abstract
A state-of-charge (SoC) monitoring scheme for rechargeable batteries that can predict and filter the voltage dip time period, effectively reduce abnormal fluctuations in SoC data, and improve battery SoC evaluation stability is proposed. This scheme, which is based on the Kalman filtering algorithm and a lightweight neural network, can be applied to evaluate battery SoCs for wearable and portable devices. This method outperforms the Gaussian naive Bayes (GNB) and the support vector machine (SVM) algorithms in terms of predicting voltage dip time periods, with an accuracy of 95.27 %. The experimental results show that the method can be implemented on real hardware systems and used for SoC evaluation of several kinds of rechargeable batteries, demonstrating the scheme's generality.
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