Electrical Model of a Membraneless Micro Redox Flow Battery—Fluid Dynamics Influence

Abstract

Membraneless micro redox flow batteries are an incipient technology that has been shown to extend some properties of traditional redox flow batteries. Due to their microfluidic scale and the absence of membrane, the fluid dynamics operation is critical in the electrical response. In this work, an electrical model is established to evaluate the influence on three battery performance metrics: steady-state power, power transient dynamics, and mixing and self-discharge losses. First, an equivalent electrical circuit, derived from a state-of-the-art regular battery equivalent circuit, is defined by studying the influence of flow changes on its impedances and source, aggregating it as a variable. Then, empirical data are used to demonstrate the proposed equations defining the variation of the electrical response relative to fluid dynamics, and their parameters are identified with grey box methods. The steady-state power model incorporates the interphase position, extending conventionally used redox flow batteries expressions, such as Faraday´s Law and Nersnt´s equation, for the membraneless analysis. A transient response model is built, which becomes effectively relevant in intermittent power applications (such as many renewable energy storage ones). Finally, mixing and self-discharge losses are evaluated with the variation state of charge at the outputs of the cell, using spectrophotometry measurements, and compared with flowmeter mixing values. This demonstrates that flow-rate values can provide a precise quantification of these losses. The electrical model with dependent parameters from the three fluid dynamics analyses can be used to evaluate the performance of micro membraneless redox flow batteries and their response to fluidic operation.INDEX TERMS Battery efficiency, electric equivalent model, grey box identification, microfluidics, redox flow battery.

Adaptive Microfluidic Modeling of a Membraneless Micro Redox Flow Battery Using Extended Kalman Filter

“…Where 𝑌 and 𝑋 are reference output and reference state (which increment part is zero). The feedback gain is calculated with new state matrices that, as in (22), now are expressed as:…”

“…In [21] reinforcement learning algorithms are applied to two coflowing laminar streams at the inlets of a simple general microchannel with computer vision feedback. The first attempt for microfluidic reactors of fluidic modelling has been done in [22], where the influence of the fluid dynamics configuration on the proposed equivalent electrical model has been studied. This work demonstrated the importance of fluidic control for a good electrochemical performance, but it does not tackle the dynamic fluidic modelling and control itself.…”

“…In this work, the dynamic microfluidic model is tackled, so that system response is known and interphase in the cell can be guaranteed by precisely controlling flow rates at the inlets and outlets of the cell. Consequently, self-discharge and electrolytes crossover are minimized, improving efficiency and making recirculation possible, as the model from [22] studying fluid dynamics influence on electrochemical response indicates. The dynamic fluidic model reflects the microfluidic response of the flow rates to actions of the active elements of the system (pumps and valves) measured with sensors for feedback (flowmeters), and it is used for designing the closed-loop control.…”