1,720,963 research outputs found
A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems
No full textBattery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling
Improvement of an equivalent circuit model for li-ion batteries operating at variable discharge conditions
No full textA real time simulation of battery conditions is an essential step in the development of energy harvesting devices. Since it is not possible to have a direct measurement, the battery information, such as the remaining charge, need to be estimated by means of model-based estimation algorithms. Most of the existing models describing battery behaviour, are suitable only for a constant discharge current. This paper proposes a study of the dependence of the equivalent circuit model parameters on different discharge conditions. The model presented provides a powerful tool to represent the batteries’ behaviour in energy harvesting systems, involving continuous charge and discharge cycles. The extraction of parameters was performed, starting from a set of reference curves generated in Matlab Simulink environment, referring to Li-ion technology batteries. The parameters were extracted by means of a cascade of global and local search identification algorithms. Finally, the relations describing parameters’ behaviours as functions of the discharge current are presented
Parallel algorithm based on singular value decomposition for high performance training of neural networks
No full textOn circuital topologies and reconfiguration strategies for PV systems in partial shading conditions: A review
No full textPhotovoltaic (PV) power generation is heavily influenced by mismatching conditions, mainly caused by partial or full shading of an array portion. Such a non-uniform irradiation can lead to severe reductions in the power produced; some techniques, such as array reconfiguration or micro-converters and microinverters technology are aimed at retrieving this power together with the use of Maximum Power Point (MPP) tracker algorithms, while others tend to mitigate the effects that power losses have on the PV system, i.e. overheating and aging. Solutions based on the use of bypass diodes and their re-adapted forms belong to this latter case. The complexity of the problem has shown the need of analyzing the role played by each one of the mentioned aspects; the focus of this paper is to give the reader a detailed review of the main solutions to PV arrays shading present in literature
Evaluation of a demand response online algorithm on the costs sustained by the user
No full textThe Demand Response (DR) algorithm is a technique that allows users to reduce their electricity consumption during peak hours, thereby reducing their overall energy costs. This study aims to evaluate the impact of the DR algorithm on the electrical costs sustained by the user. The research was conducted by analyzing the energy consumption patterns of a group of residential users with and without the implementation of the DR algorithm in their homes. The results of the study indicate that the implementation of the DR algorithm resulted in a significant reduction in the electrical costs sustained by the users. The study also found that the effectiveness of the DR algorithm was dependent on various factors such as the type of appliance, the time of day, and the user's behaviour. Overall, the study demonstrates that the DR algorithm is an effective tool for reducing energy costs and can be optimized through user participation and engagement. The findings of this research can help policymakers and energy providers to develop more effective demand response programs that benefit both users and the grid
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Neural Modelling of Magnetic Materials for Aircraft Power Converters Simulations
No full textPower converters often features inductive devices in their architectures. Accurate simulation of the converters requires a well-defined response of the magnetic cores. A computationally efficient approach for the numerical modelling of hysteretic magnetic materials is presented in this work. The approach exploits the simplicity of the identification procedure for the Preisach model of hysteresis and the reduced computational costs of Neural Networks. The model for hysteresis is implemented both in direct and inverse form. Validation is performed against independent dataset, with evident computational speedup, which can be a valuable asset for magnetic cores simulations in the design of complex power systems featuring multiple converters such as the ones used in avionic applications
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