Vanadium Redox Flow Battery State of Charge Estimation Using a Concentration Model and a Sliding Mode Observer

Abstract

Vanadium redox flow batteries are very promising technologies for large-scale, inter-seasonal energy storage. Tuning models from experimental data and estimating the state of charge is an important challenge for this type of devices. This work proposes a non-linear lumped parameter concentration model to describe the state of charge that differentiates the species concentrations in the different system components and allows to compute the effect of the most relevant over-potentials. Additionally, a scheme, based on the particle swarm global optimization methodology, to tune the model taking into account real experiments is proposed and validated. Finally, a novel state of charge estimation algorithm is proposed and validated. This algorithm uses a simplified version of previous models and a sliding mode control feedback law. All developments are analytically formulated and formally validated. Additionally, they have been experimentally validated in a home-made single vanadium redox flow battery cell. Proposed methods offer a constructive methodology to improve previous results in this field. ; This work was supported in part by the Spanish National Research Council Consejo Superior de Investigaciones Científicas (CSIC) under Project PIE-201980E101, in part by the Spanish Ministry of Science and Innovation through the Project DOVELAR (Ministerio de Ciencias yUniversidades (MCIU)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE) under Grant RTI2018-096001-B-C31 and Grant RTI2018-096001-B-C32, in part by the Laboratorio de Investigación en Fluidodinámica y Tecnologías de la Combustión (LIFTEC) Research Team through the Aragon Government under Project LMP246_18, and to the group T01_20R, in part by the Universidad Politécnica de Cataluña (UPC) Research Team through the María de Maeztu Seal of Excellence to Instituto de Robótica e Informática (IRI) under Grant MDM-2016-0656, and in part by the Generalitat de Catalunya under Project 2017 SGR482.