Renewable–electric vehicle (EV) microgrids must coordinate intermittent generation, stationary storage, uncertain demand, and flexible but service-constrained EV charging or battery swapping. These challenges are amplified in weak grid and off-grid settings, where data may be scarce, grid interruptions frequent, and operational decisions must respect battery, converter, reserve, and service constraints. This paper proposes PEAR-EMS, a Physics-Embedded Adaptive Reinforce ment Learning framework for energy management in resilient renewable–EV microgrids, together with a real-time experimental setup for future validation. PEAR-EMS combines a physics based microgrid model, state- and context-aware reinforcement learning, an adaptive constraint-handling mechanism, and a model-predictive safety filter. The physical model embeds energy balance, storage dynamics, conversion limits, renewable avail ability, grid outages, and EV energy-service requirements into the agent’s observations, action projection, and reward/constraint structure. The adaptive layer adjusts policy exploration and operating margins in response to forecast uncertainty, battery state, and detected distribution shift. A proposed controller in-the-loop / hardware-in-the-loop testbed comprises a real time digital simulation or real-time simulator, a programmable controller or industrial computer, PV and battery emulation, bidirectional EV/charger emulation, grid-interface and protection emulation, measurement and communication interfaces, and a supervisory logging platform. The manuscript defines the mathematical formulation, controller architecture, experimental scenarios, comparison baselines, evaluation metrics, and repro ducibility protocol. It does not claim experimental results: the proposed architecture and test plan require implementation and empirical validation before performance or safety claims can be confirmed. The intended contribution is a transparent route from physics-informed learning to real-time evaluation for renewable EV microgrids in data-constrained environments.