Scalable Safety-Constrained Learning Pipelines for Distributed Digital-Twin-Based Energy Optimization in Large-Scale Electric Mobility Systems

Abstract

Massive implementation of electric mobility systems such as electric vehicle, smart charging systems and grid-interactive transportation infrastructure has increased complexity in managing energy because the demand is highly dynamic, system scales are heterogeneous, and demand is very vulnerable in terms of operational safety. Digital twin technology offers a new promising imago of cyber -physical modelling, monitoring and optimization of such complex systems but the combination of learning based optimization of its distributed digital twin systems remains a challenge due to the issue of scalability, unsafe exploration and imposition of physical and operational problems under uncertain conditions. A scalable safety-constrained learning pipeline to optimise electric mobility systems in large-scale, digitally-twin-based electricity optimization is stated in this paper. The also suggested framework uses the hierarchical digital twin with a vehicle-level, charging-station-level, and grid-level twins to facilitate the use of coordinated but decentralised decision-making. Safe reinforcement learning is employed in order to obtain adaptive and robust energy optimization based on local digital twins learning via decentralised data without losing privacy and ensuring scalability. The combination of Lyapunov-based policy verification, control barrier functions, and real-time constraint monitoring is rigidly implemented as an operational safety strategy in order to avert any violation associated with battery health, thermal limits, charging capacity, and grid stability. Detailed simulations of the evaluations in large-scale and stochastic operating conditions have proven that the proposed approach attains significant energy cost minimization and peak load reduction over centralised, rule-based, and unconstrained learning baselines. Notably, the framework follows a high level of compliance with safety limitations in all of the assessed situations, with no cases of violation of operations and the constant convergence of learning. These findings indicate the applicability of formal safety assurances in distributed digital twin learning pipelines as a feasible and relevant solution towards smart energy optimization in the next-generation electric mobility systems.