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first_page settings Order Article Reprints Open AccessArticle Battery Energy Management System Using Edge-Driven Fuzzy Logic by Mustapha Habib 1,*, Elmar Bollin 2and Qian Wang 1,3 1 Division of Building Technology and Design, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden 2 Institute of Energy System Technology, Offenburg University of Applied Sciences, 77652 Offenburg, Germany 3 Uponor AB, Hackstavägen 1, 72132 Västerås, Sweden * Author to whom correspondence should be addressed. Energies2023,16(8), 3539;https://doi.org/10.3390/en16083539 Received: 1 April 2023/Revised: 14 April 2023/Accepted: 18 April 2023/Published: 19 April 2023 (This article belongs to the Special IssueFuzzy Decision Support Systems for Efficient Energy Management) Download Browse Figures VersionsNotes Abstract Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes.

The significant market growth of stationary electrical energy storage systems both for private and commercial applications has raised the question of battery lifetime under practical operation conditions. Here, we present a study of two 8 kWh lithium-ion battery (LIB) systems, each equipped with 14 lithium iron phosphate/graphite (LFP) single cells in different cell configurations. One system was based on a standard configuration with cells connected in series, including a cell-balancing system and a 48 V inverter. The other system featured a novel configuration of two stacks with a parallel connection of seven cells each, no cell-balancing system, and a 4 V inverter. The two systems were operated as part of a microgrid both in continuous cycling mode between 30% and 100% state of charge, and in solar-storage mode with day–night cycling. The aging characteristics in terms of capacity loss and internal resistance change in the cells were determined by disassembling the systems for regular checkups and characterizing the individual cells under well-defined laboratory conditions. As a main result, the two systems showed cell-averaged capacity losses of 18.6% and 21.4% for the serial and parallel configurations, respectively, after 2.5 years of operation with 810 (serial operation) and 881 (parallel operation) cumulated equivalent full cycles. This is significantly higher than the aging of a reference single cell cycled under laboratory conditions at 20 °C, which showed a capacity loss of only 10% after 1000 continuous full cycles.

In this study, a high-performance controller is proposed for single-phase grid-tied energy storage systems (ESSs). To control power factor and current harmonics and manage time-shifting of energy, the ESS is required to have low steady-state error and fast transient response. It is well known that fast controllers often lack the required steady-state accuracy and trade-off is inevitable. A hybrid control system is therefore presented that combines a simple yet fast proportional derivative controller with a repetitive controller which is a type of learning controller with small steady-state error, suitable for applications with periodic grid current harmonic waveforms. This results in an improved system with distortion-free, high power factor grid current. The proposed controller model is developed and design parameters are presented. The stability analysis for the proposed system is provided and the theoretical analysis is verified through stability, transient and steady-state simulations.

There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.

There is a growing trend toward the use of thermo-active building systems (TABS) for heating and cooling buildings, since these systems are known to be very economical and efficient. However, due to the large thermal inertia, the control of these systems turns out to be complicated and time-consuming in the parameterization. With standard control strategies, the required thermal comfort in buildings often cannot be met, especially if the internal heat sources are suddenly changed. This paper presents a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS. Through the self-learning effect, no parameterization of heating and cooling curves is necessary. The algorithm is compared to a conventional control strategy for TABS. In this simulation case, it turns out that the adaptive and predictive control strategy can achieve a saving of pump-running time of up to 81% while increasing thermal comfort. In addition, the algorithm is validated by a three-week laboratory experiment. With simple calculation approaches, this algorithm proves to be very practical and can easily be integrated into a building's automation system. Furthermore, it offers the possibility for load shifts from the use of the thermal mass of a building.