With the rapid development of urban rail transit

supercapacitors have demonstrated remarkable advantages in regenerative braking applications owing to their high power density and rapid charge/discharge capabilities. However

the nonlinear aging phenomena induced by high-frequency power cycling and dynamic ambient temperature fluctuations significantly compromise their lifetime. Current lifetime prediction methodologies predominantly overlook the deterioration acceleration effect caused by equivalent series resistance rise and the impact of dynamic environmental temperature variations

resulting in inadequate prediction accuracy. To address these limitations

this paper proposes an innovative lifetime prediction approach for supercapacitors that incorporates a coupled mechanism of deterioration acceleration and dynamic ambient temperature effects. The methodology involves establishing a DC traction network model integrated with an electro-thermal coupled aging model of supercapacitor in urban rail transit to quantitatively analyze the synergistic interactions among operational conditions

temperature fluctuations

and aging feedback

while implementing daily parameter iteration for lifetime estimation. Case study results demonstrate that the proposed method achieves a 61%~75% reduction in relative prediction error compared to single-factor models that consider either deterioration acceleration or dynamic temperature effects alone. The necessity of the coupled modeling approach has been further validated through comprehensive aging tests conducted on supercapacitor cells using the Arbin test platform.