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We analyze a Michelson interferometer with Bessel–Gaussian vortex beams, where acceleration of the reference frame perturbs the interference pattern of the two Bessel–Gaussian vortices with opposite topological charges. The reversal of topological charge in one arm is due to the vortex phase-conjugating mirror being composed of two double passes through the cylindrical lens. The tiny changes of the space metric are time dependent.
Abstract Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH.
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