• Energy Research

The generation of electrical energy based on renewable sources is essential to successfully achieve the energy transition. In particular, electric vehicle charging stations must be capable of dispatching high levels of power to satisfy the demand during fast charging processes. Taking this into consideration, this paper presents a preliminary analysis of a charging station comprising a wind turbine and a redox flow battery, which acts as a storage system to balance power generation and demand. Electric power profiles extracted from a standardized wind profile are analysed, and the feasibility of its use for the estimation of parameters in redox flow batteries is evaluated. Finally, the estimation results are discussed, obtained by using a methodology based on the combination of sliding mode algorithms and discrete recursive estimators with forgetting factor. Este trabajo fue osible gracias al soporte de la Universidad Nacional de La Plata, el CONICET, y la ANPIDTI, de Argentina; y del Ministerio de Ciencia e Innovación (proyecto MAFALDA PID2021-126001OB-C31 financiado por MCIN/AEI/10.13039/501100011033/ERDF,EU) y una beca de la Fundación Bancaria ”laCaixa” (ID100010434,código LCF/BQ/DI21/11860023), de España. Peer Reviewed

Trabajo presentado en el 16th International Workshop on Variable Structure Systems (VSS), celebrado en Rio de Janeiro (Brasil), del 11 al 14 de septiembre de 2022 This work presents the preliminary findings of a feasibility study for a class of sliding mode differentiators to be used in an on-line parameter estimation methodology for vanadium redox flow batteries (VRFB). Specifically, three high-order continuous differentiators are considered: a Standard Differentiator; a Filtering Differentiator, which excels the former by incorporating rejection to large noises small in average; and a Tracking Filtering Differentiator, which produces smooth consistent derivatives while inheriting the noise rejection capabilities from the previous one. To model the vanadium redox flow batteries an equivalent circuit model is employed, whose time-varying parameters are estimated by a recursive least squares algorithm with forgetting factor, that requires the VRFB measured voltage and current, together with their derivatives. To assess the performance of the different differentiation algorithms in obtaining reliable values of the VRFB parameters, the storage system is excited with standardised current demand profiles. Finally, representative simulation results are presented and discussed.

This paper considers an electric vehicle charging station based on the combination of a wind turbine, as a primary power source, and a vanadium redox flow battery (VRFB), as an energy storage system. The latter plays a key role in the application under study, storing the intermittent power produced by the turbine and timely dispatching it when demanded. To guarantee VRFB proper operation, it is necessary to have information regarding its internal parameters which, in general, cannot be directly measured. Therefore, this work analyses the feasibility of conducting a model-based estimation by studying a classic identifiability measure, the persistence of excitation. Special attention is given to the influence of the wind power profile, as well as the rated power of the turbine, on the performance of the estimation algorithms. It is demonstrated that increasing the wind energy conversion system nominal power might compromise the estimation results, provided that systems with higher inertia reduce the persistence of excitation levels. This work was possible thanks to the support of Facultad de Ingeniería, UNLP, CONICET, and Agencia I+D+i, from Argentina; and Projects MAFALDA (PID2021-126001OBC31) funded by MCIN/ AEI /10.13039/501100011033 and by "ERDF A way of making Europe", and MASHED (TED2021-129927B-I00), funded by MCIN/ AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. Peer Reviewed

SummaryA new insight into vanadium redox flow batteries (VRFB) parameter estimation is presented. Driven by the electric vehicles proliferation, a hybrid fast‐charging station with grid and a renewable energy connection is particularly considered. In this stationary application, the VRFB is operating as buffering module. This hybrid topology could contribute to reduce the grid connection cost of the charging station. However, to make VRFB a viable technology, improvements are needed. Among these, some of the most important are in the field of the estimation of the battery's State of Charge, State of Health, and internal parameters. The proposed estimation method is based on a recursive least square (RLS) estimation algorithm with forgetting factor, combined with a sliding mode finite‐time convergent differentiation algorithm. The latter provides robust exact derivatives of both VRFB's current and voltage with a high degree of noise rejection, required by the RLS algorithm to perform a precise estimation. The proposed sliding mode‐based estimation setup is completed with a systematic methodology to guarantee the validity of the on‐line estimated values, depending on the persistence of excitation of the measured current and voltage. Finally, the methodology is thoroughly analysed and validated by computer simulation.

Electrolyte imbalance is the main cause of capacity loss in vanadium redox flow batteries. It has been widely reported that imbalance caused by vanadium crossover can be readily recovered by remixing the electrolytes, while imbalance caused by a net oxidation of the electrolyte can only be reverted by means of more complex chemical or electrochemical methods. At the moment, however, the joint effect of both types of imbalances on the battery capacity is still not well understood. To overcome this limitation, generalised State of Charge and State of Health indicators that consider both types of imbalances are derived in this work. Subsequently, a thorough analysis on how the battery capacity depends on electrolyte imbalance is performed. As a result of this analysis, two specific outcomes are highlighted. Firstly, it is shown that standard electrolyte remixing may be counterproductive under certain imbalance conditions, further reducing the battery capacity instead of augmenting it. Secondly, it is demonstrated that most of the capacity loss caused by oxidation can be mitigated by inducing an optimal mass imbalance in the system. Consequently, a systematic procedure to track this optimum is proposed and validated through computer simulation. The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI21/11860023. This work is part of the Project MAFALDA (PID2021-126001OB-C31) funded by MCIN/ AEI /10.13039/501100011033 and by “ERDF A way of making Europe”. This work is part of the project MASHED (TED2021-129927B-I00), funded by MCIN/ AEI/10.13039/50110 0011033 and by the European Union Next GenerationEU/PRTR. Peer Reviewed

This work develops and compares three different controllers for PEMFC systems in automotive applications. All the controllers have a cascade control structure, where a generator of setpoints sends references to the subsystems controllers with the objective to maximize operational efficiency. To develop the setpoints generators, two techniques are evaluated: off-line optimization and Model Predictive Control (MPC). With the first technique, the optimal setpoints are given by a map, obtained off-line, of the optimal steady state conditions and corresponding setpoints. With the second technique, the setpoints time profiles that maximize the efficiency in an incoming time horizon are continuously computed. The proposed MPC architecture divides the fast and slow dynamics in order to reduce the computational cost. Two different MPC solutions have been implemented to deal with this fast/slow dynamics separation. After the integration of the setpoints generators with the subsystems controllers, the different control systems are tested and compared using a dynamic detailed model of the automotive system in the INN-BALANCE project running under the New European Driving Cycle. Peer Reviewed