Neural Network Surrogate for Multi‐Criteria Coordinated Model Predictive Control of Vanadium Redox Flow Battery Stations With Bound Tightening

ABSTRACT

State‐of‐charge (SOC) inconsistency among units in vanadium redox flow battery (VRFB) stations causes voltage‐limit violations and premature charge/discharge cutoffs, thereby degrading station‐level energy utilization and dispatch performance. This paper proposes a station‐level multi‐objective coordinated model predictive control (MPC) strategy using a mixed‐integer‐representable ReLU neural surrogate and hybrid bound tightening. The main innovation is to integrate OCV‐reference selection, SOC‐equalising station‐level MPC and feasibility‐based/optimization‐based bound tightening (FBBT + OBBT) into a unified control‐oriented framework that preserves terminal‐voltage safety whilst reducing online computational burden. First, an open‐circuit‐voltage (OCV) reference trajectory is selected at the single‐stack level to operate in a high flow‐rate sensitivity region and mitigate concentration polarization. Second, a station‐level MPC formulation jointly tracks the OCV reference, equalises inter‐cabin SOC and satisfies dispatch requirements under voltage, concentration, current and power constraints. Third, a ReLU‐based surrogate replaces the nonlinear electrochemical model and is embedded into the MPC through Big‐M constraints, whilst the bilinear power term is handled by McCormick envelopes. The hybrid FBBT + OBBT scheme tightens neuron bounds by 28.64%–59.24% and reduces preprocessing time by 14% compared with pure OBBT. Simulation results show that the proposed strategy shortens charging time by about 10%, improves voltage efficiency from 75.59% to 79.95%, improves energy efficiency from 71.20% to 74.64% and reduces station‐level charge/discharge actions from 31 to 29 under the same 100 MWh dispatch target, demonstrating improved safety, efficiency, SOC consistency and operational economy.