Kentsel raylı transit enerji depolama sistemlerinde süperkapasitörler için tanımlanabilirlik güdümlü çift modlu akım koşullu eşdeğer devre modeli

Özet (abstract)

Accurate supercapacitor equivalent circuit models (ECMs) are essential for voltage reconstruction, control design, and energy-efficiency evaluation in urban rail transit (URT) energy storage systems. Under practical URT profiles, supercapacitors are subjected to high-current bidirectional pulses, where charge–discharge asymmetry and current-conditioned apparent capacitance variation may reduce the accuracy of conventional fixed-parameter models. To address these issues, this paper develops a dual-mode current-conditioned supercapacitor ECM and an identifiability-guided parameterization framework. The charging and discharging dynamics are independently parameterized from their respective multi-current capacitance characteristics and integrated into a unified dynamic model through a hysteresis-based transition mechanism. The apparent capacitance is expressed as a function of voltage, current, and operating mode while retaining a compact single-state circuit structure. For parameterization, resistance and baseline capacitance are first directly extracted, and the mode-specific capacitance-law coefficients are initialized from experimentally extracted capacitance characteristics. Sensitivity and normalized correlation analyses are subsequently used as pre-optimization tools to restrict poorly separable parameter directions, after which only the retained parameter subset is refined through constrained optimization. Repeatability and measurement-noise tests are further conducted to evaluate parameterization stability and prediction robustness. Compared with representative RC, ladder, and branch ECMs under the same multi-current datasets and an independent URT-based dynamic profile, the proposed ECM achieves RMSE, MAE, and maximum absolute error of 13, 11, and 38 mV. It also achieves minimal errors in charge-discharge energy and remaining-capacity estimation, enabling the model for system-level voltage reconstruction and regenerative-energy evaluation.

Yazarların özeti; kaynağından alınmıştır. Journal of Energy Storage, 2026 · DOI ↗