Online degradation estimation for hybrid supercapacitors via data-driven enhanced pseudo-two-dimensional model

Abstract

Hybrid supercapacitor (HSC) delivers high power and energy densities for energy storage applications, but its pronounced temperature dependence and fluctuating capacity fade severely impede accurate state of health (SOH) estimation. Current algorithms focus solely on SOH and do not provide mechanistic understanding of degradation, which limits the effectiveness of hybrid supercapacitor health management. This study proposes a P2D-convolutional neural network (CNN) framework for online SOH monitoring and degradation mechanism analysis of HSC. A P2D model modified with the double-layer equation was employed to generate datasets via Latin hypercube sampling. This model was then coupled with a CNN to extract degradation-related features from incremental capacity curves through physics-data fusion, enabling reliable SOH prediction and quantitative assessment of electrochemical parameter degradation. Experimental results demonstrate that the proposed framework accurately captures the degradation mechanisms of HSC under fluctuating conditions, with the SOH prediction achieving an R² of 0.9387 and a mean absolute percentage error (MAPE) of 1.57% across 13,898 cycles. These findings provide useful guidance for the design and health monitoring of HSC.

Keywords

Hybrid supercapacitors / state of health estimation / P2D-CNN framework / degradation mechanisms

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