Traditional physics-based models of lithium-ion batteries rely on complete and high-precision charge/discharge datasets that restrict their applicability in electric vehicle scenarios where charging data are often incomplete and operating conditions are highly dynamic. To overcome this limitation, an impedance-informed dual-tank model for degradation analysis and capacity estimation is proposed. By integrating the experimentally obtained state-of-charge (SOC)-dependent impedance characteristics into the dual-tank framework, the model accurately captures the dynamic evolution of polarization and internal resistance during cycling. The identified parameters quantitatively characterize the degrees of loss of active material (LAM) and loss of lithium inventory (LLI), thereby enabling a quantitative interpretation of degradation mechanisms and accurate capacity estimation. Validated on ternary lithium-ion cells at various current rates, the results demonstrate that the proposed model maintains superior stability under diverse charge/discharge conditions, exhibiting high robustness and strong physical interpretability. This method provides a feasible approach for quantitative degradation analysis and state of health (SOH) estimation under complex conditions, demonstrating significant potential for integration into electric vehicle battery management systems.