Ai driven vfd control for securing membrane in flow batteries

The flow battery system of claim [0001], wherein said AI-based controller incorporates a hybrid neural network architecture combining Long Short-Term Memory (LSTM) networks with attention mechanisms to predict system behavior across multiple time horizons.

The flow battery system of claims [0001] and [0002], wherein said AI-based controller simultaneously monitors differential pressure, temperature gradients, electrolyte conductivity, and flow rates to maintain optimal operating conditions.

The flow battery system of any one of claims [0001] to [0003], wherein said AI-based controller employs a mathematical model that calculates differential pressure as a function of potential difference, flow rates, temperature differentials, and electrolyte conductivity.

The flow battery system of any one of claims [0001] to [0004], further comprising multiple sensor arrays positioned throughout the system to continuously transmit operational data to said AI-based controller.

The flow battery system of any one of claims [0001] to [0005], wherein said AI-based controller implements a hierarchical decision framework with warning and alarm thresholds for each monitored parameter.

The flow battery system of any one of claims [0001] to [0006], further comprising a self-learning module that continuously refines operational parameters based on historical performance data and evolving battery conditions.

The flow battery system of any one of claims [0001] to [0007], further comprising a cell stack containing multiple electrochemical cells to increase current capacity and energy output.

The flow battery system of any one of claims [0001] to [0008], further comprising counter electrodes configured to strip zinc dendrites from anode surfaces during dynamically scheduled reversal cycles.

The flow battery system of claim [0009], wherein said AI-based controller dynamically determines optimal timing and duration of said reversal cycles based on predicted dendrite formation patterns rather than fixed timing cycles.

The flow battery system of any one of claims [0001] to [0010], wherein said AI-based controller predicts system behavior across multiple timeframes simultaneously to enable both immediate response to developing conditions and strategic optimization for long-term performance.

The flow battery system of any one of claims [0001] to [0011], wherein said AI-based controller implements proportional response protocols when parameters approach warning thresholds, with adjustment magnitudes determined by predictive models of system response characteristics.

The flow battery system of any one of claims [0001] to [0012], wherein said AI-based controller initiates appropriate safety protocols when parameters reach alarm thresholds, including controlled system shutdown, emergency electrolyte recirculation, or transition to safe-state operation.

The flow battery system of any one of claims [0001] to [0013], wherein said ion-exchange membrane is metallic and said AI-based controller maintains differential pressure across said membrane within specified safety limits to prevent membrane breach.