Multiphysics modeling of hybrid thermo-electrochemical energy storage integration for industrial energy systems: A path to sustainable manufacturing under dynamic policy scenarios

Industrial energy systems integrating renewable generation, carbon capture, and hybrid storage are essential for sustainable manufacturing, yet many existing approaches rely on steady-state assumptions and neglect dynamic thermo-electrochemical behavior, safety constraints, and evolving policy incentives. This study proposes a transient multiphysics hybrid energy storage framework that combines a physics-based P2D battery model, thermal energy storage, and supercapacitors within a unified simulation environment. To accelerate prediction and optimization, a hybrid physics–deep learning surrogate strategy using physics-informed neural networks and Transformer-based models is introduced for Carbon Capture, Utilization, and Storage reactor kinetics, renewable forecasting, and multi-scenario operational planning. A dynamic policy scenario engine incorporating carbon pricing, hydrogen incentives, and renewable subsidies is integrated through stochastic optimization to evaluate policy-driven performance. Simulation-based digital-twin validation demonstrates improvements of 38%–52% in storage utilization, a 14.7% reduction in exergy losses, and 9.3%–16.8% lower lifecycle costs, while surrogate models reduce computation time by 27%. A Model Predictive Control - Reinforcement Learning (MPC-RL) hybrid control strategy further enhances operational resilience and decreases peak thermal loading by 22% under fluctuating industrial demand. Results are derived from simulation and surrogate-assisted digital-twin studies rather than real plant deployment, highlighting a scalable and policy-aware framework for efficient hybrid energy management.