However, to fully maximize their potential, it is crucial to understand the battery performance and characteristics in these vehicles, specifically the battery charge/discharge characteristic, battery performance due to various load conditions and electronics used in the system, and, more importantly, an aging battery performance. Low-power e-mobility vehicles such as e-bikes and e-scooter have gained immense popularity as sustainable and efficient modes of transportation while offering various other environmental benefits. On the other hand, the FEM Eigenmode solver allows for finding a full χ matrix while the frequency domain solvers yield the entries in the χ matrix that only represent the interactions between neighboring elements. The MoM solvers require less computational resources compared to the FEM solvers due to the fewer number of unknowns. Employing Momentum RF, Momentum Microwave, and FEM Eigenmode solvers, we derive and compare the quantum parameters. We show two QuantumPro models, the one-qubit-one-resonator and the four-qubit-four-resonator examples. In this application note, we describe the methods that QuantumPro utilizes to extract quantum parameters. From the energy participation ratios, QuantumPro calculates the quantum parameters. The Energy Participation Analysis supports the FEM Eigenmode Solver, which calculates the eigenfrequencies of the qubits and the resonators.
From the scattering parameters, QuantumPro calculates the resonant frequencies and the quantum parameters. The Full EM Analysis supports frequency sweep of the circuits using the MoM and FEM solvers. QuantumPro allows for modeling superconducting qubit circuits analyzing their EM performance and their nonlinear properties in the quantum regime with ultra-low power signal excitation.
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