Generative AI Driven Process Calculations for Fuel Cells and Flow Batteries

Electrochemical energy systems such as proton-exchange membrane fuel cells (PEMFCs), solid-oxide fuel cells (SOFCs), and vanadium redox flow batteries (VRFBs) are governed by strongly coupled, nonlinear transport-kinetics equations spanning multiple scales. Mechanistic solvers provide physical fidelity but impose modeling and software burdens that hinder rapid iteration, while purely data-driven surrogates, such as artificial neural networks (ANNs) and deep reinforcement learning (DRL), can be brittle under distribution shift. This paper proposes a Generative AI assisted computational framework that utilizes large language models (LLMs) to orchestrate retrieval-augmented generation (RAG), physics-constrained prompting, and tool-integrated reasoning for electrochemical process calculations. We evaluate this framework on two complementary data sets: (1) synthetic data from physics-based simulators for controlled benchmarking, and (2) Aspen Plus data from high-fidelity industrial process simulations validated against experimental measurements. For PEMFC polarization curve decomposition, the framework achieves RMSE of 9.6 mV (synthetic) and 7.8 mV (Aspen data), with constraint violations reduced from 48%/42% to 1.2%/0.5% respectively. For VRFB optimization, energy efficiency reaches 79.1% (synthetic) and 74.9% (Aspen data). The dual-data set evaluation demonstrates robustness across data characteristics while a preliminary user study (<i>N</i> = 5) shows 85% reduction in human effort. We compare against ePCDNN, ANN-based models, DRL parameter tuning, and mechanistic approaches, providing an ablation study isolating the effects of RAG quality, physics constraints, and prompt engineering. We discuss integration with digital twins, fault detection, and responsible deployment.