Research on state-of-health (SOH) assessment of batteries at variable sampling frequencies

Abstract This study investigates the impact of sampling frequency on the accuracy of battery State-of-Health (SOH) assessment, essential for the dependability of electric vehicles and energy storage systems. Our key contribution is identifying the optimal sampling frequency for SOH estimation without distortion, using variance as a feature value for enhanced accuracy. The experimental data demonstrate that, upon weighing the two key factors of measurement accuracy and calculation efficiency, the selection of 0.025 V as the sampling frequency can achieve optimal coordination between them. This research highlights the significance of sampling frequency in SOH prediction and validates the use of neural networks for accurate battery health assessment, offering insights for optimizing battery management systems.