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

Abstract

Membraneless micro redox flow batteries are a promising technology that can improve traditional redox flow batteries performance. However, a precise modeling and control of the microfluidic dynamics is a complex task for which only open loop control strategies can be found in the literature. In this work, a strategy for the adaptive modeling of the microfluidic dynamics of a membraneless micro redox flow battery is presented. The model is based on proposed equations whose constant parameters are identified using grey-box modeling techniques. Intrinsic limitations of applying these equations to the real system (stochasticity of the microfluidic system, non-considered variables, non-linearities) are overcome by adapting the model through the addition of correction factors calculated in real time. In the proposed use case, an extended Kalman filter is used to estimate the factors. Also, the model with real-time adaption is proven to be suitable for control design using a model-based control technique such as incremental state optimal control. The modeling and the controller adequacy are validated in simulation and real experiments. Model adequacy to real system is demonstrated, and it allows a model-based control design that improves microfluidic response, which is expected to result in higher battery efficiency and reactant conversion ratio.