Electric Vehicles (EVs) have been identified as a significant solution for reducing carbon emissions from the transportation sector by replacing internal combustion engine vehicles. EVs improve air quality, resulting in yearly carbon emissions reductions of up to 1.5 gigatons. The establishment of a prominent EV market and the construction of a robust charging infrastructure are critical components for the success of electromobility. Slow, fast, and rapid chargers may all be used to charge EVs. Rapid chargers draw large quantities of energy from the grid within a short period of time. If a significant number of rapid chargers are used concurrently during the network’s peak load, for example, network may be subjected to critical conditions. Using Deno runtime based on the JavaScript programming language, this paper presents an algorithm for estimating the energy consumption and demand of common EV models capable of rapid charging. The PSCAD/EMTDC is also used to analyse the effect of chargers on distribution cable and substation transformer loading. The paper’s findings show that battery energy storage units may be used to reduce the loading on the distribution cables and transformers.

The sustained deployment of human missions and permanent settlements on Mars will depend on highly reliable electrical power systems to support life-support facilities, habitats, scientific instruments, communication networks, in-situ resource utilization, transportation, and other mission-critical operations. The safety and uninterrupted operation of astronauts’ electrical and electronic equipment,including spacesuits, rovers, autonomous robots, sensing devices, and power electronics are equally essential for mission success. This is because, electrostatic discharge (ESD) generated during Martian dust storms may pose significant risks by affecting spacecraft electronics, communication systems, scientific instruments, and solar photovoltaic (PV) power systems through tribo electric charging, dust deposition, and electrical breakdown. Accurate prediction of these coupled phenomena remains challenging because conventional models do not simultaneously account for atmospheric flow, particle electrification, electric-field evolution, and dynamic gas breakdown under Martian environmental conditions. This research presents an integrated computational and experimental framework that combines Computational Fluid Dynamics (CFD), a Physics-Informed Neural Network (PINN), and a novel Dynamic Mars Paschen Model (DMPM) to predict electrostatic discharge and assess its impact on Martian surface sys tems. The CFD model simulates airflow, dust transport, and pressure distribution within a Martian environmental chamber, while the PINN incorporates the governing equations of fluid flow, charge conservation, and electrostatic field evolution to efficiently predict triboelectric charge accumulation and electric-field intensity. The proposed DMPM extends the classical Paschen law by incorporating the effects of airflow velocity, dust concentration, atmospheric temperature, triboelectric charge density, and carbon dioxide frost coverage to estimate the dynamic breakdown voltage under realistic Martian conditions. Experimental validation con ducted in a purpose-built Martian environmental chamber demonstrated excellent agreement between numerical predictions and laboratory measurements, with coefficients of determination exceeding 0.98 and prediction errors generally below 3%. Beyond electrostatic discharge prediction, the developed laboratory provides a sustainable multidisciplinary research platform for investi gating dust-induced degradation of Martian solar PV systems, evaluating electrostatic mitigation technologies, validating planetary instrumentation, and supporting future Mars exploration research. The proposed CFD–PINN–DMPM framework therefore con tributes to improved mission safety, energy-system reliability, and the long-term sustainability of planetary surface operations.

Energy management in solar-based microgrids that also serve electric vehicles (EVs) in sub-Saharan Africa must cope with scarce measurement data, unreliable grid supply, volatile demand and tight battery and generator limits. Reinforcement learning (RL) can adapt to this uncertainty, but unconstrained RL policies give no assurance that operating limits are respected, and most published methods are validated on data-rich systems outside the region. This paper proposes a methodology, not a set of results, for a safe and data-efficient RL energy management system called SCOPE-EM (Shielded Constrained Policy Optimisation for EV-integrated Microgrids). It combines (i) probabilistic forecasting of photovoltaic (PV) output, load and EV demand with conformal calibration and transfer from data-rich sites; (ii) a constrained Markov decision process solved by a forecast-conditioned Lagrangian actor critic; (iii) a model-predictive shield that projects every action onto a chance-constrained feasible set; and (iv) an operator-facing explanation layer that records shield interventions. We specify the problem formulation, the data strategy, three archetypal case studies, the baselines and ablations, a pre-specified statistical protocol, and evaluation metrics covering cost, reliability, safety, EV service quality and equity. The hypotheses are stated so that the planned studies can refute them.

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.