Bonifacio Martín del Brío

@inproceedings{9243111,

title = {Battery state prediction in photovoltaic standalone installations},

author = {A Guillén-Asensio and I Sanz-Gorrachategui and P Pastor-Flores and J S Artal-Sevil and Antonio Bono and Bonifacio Martín Brío and C Bernal-Ruiz},

doi = {10.1109/EVER48776.2020.9243111},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)},

pages = {1-6},

abstract = {Effective energy treatment is one of the challenges to which scientists are devoting more interest and resources. Currently, there is a need to optimize the generation of energy by renewable methods and to improve the storage and management of battery cells. One of these emerging lines aims to face the problem of uncertainty that exists in the generation of energy in photovoltaic installations. In this paper, we proposed to use machine learning methods to predict the state (voltage) of the batteries at several days into the future. The results obtained from two recurrent neural networks such as NARX and LSTM are compared, getting good results with both. Two approaches are considered: sample-based prediction and pattern-based forecasting.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{8927236,

title = {Nonlinear Dynamic Equivalent Circuit of a Switched Reluctance Motor Considering Core Losses and Leakage Inductance},

author = {Francisco Jose Pérez-Cebolla and Abelardo Martinez-Iturbe and Bonifacio Martín Brío and Carlos Bernal-Ruiz and Jesús Sergio Artal-Sevil and Pablo Pastor-Flores},

doi = {10.1109/IECON.2019.8927236},

issn = {2577-1647},

year  = {2019},

date = {2019-10-01},

booktitle = {IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society},

volume = {1},

pages = {1222-1227},

abstract = {In this paper a nonlinear dynamic lumped circuit model of a switched reluctance motor is proposed. This model includes the effect of core losses and leakage inductance. The parameters of the nonlinear circuit are identified from the experimental flux linkage and phase current. The proposed model, compared to other available static models, allows the coupling between electrical and mechanical phenomena and, therefore, it is suitable for the characterization of the full rotating behaviour of the motor. This nonlinear model is based on the space-state framework and it is implemented in Matlab-Simulink. Model accuracy has been verified at both, constant speed and accelerating; the correct fit between the model and a real switched reluctance machine is shown. The accurate fit of the stator waveform obtained in model simulations with experimental measurements proves both, the accuracy of the proposed model and the correct estimation of its components. All these parameters are non-linearly dependent on the current and the actual rotor position.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{8926907,

title = {Analysis of Li-ion battery degradation using self-organizing maps},

author = {Pablo Pastor-Flores and Carlos Bernal-Ruiz and Iván Sanz-Gorrachategui and Antonio Bono and Bonifacio Martín Brío and Jesús Sergio Artal-Sevil and Francisco J Perez-Cebolla},

doi = {10.1109/IECON.2019.8926907},

issn = {2577-1647},

year  = {2019},

date = {2019-10-01},

booktitle = {IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society},

volume = {1},

pages = {4525-4530},

abstract = {This paper proposes a new methodology to identify the different degradation processes of Li-Ion battery cells. The goal of this study is to determine if different degradation factors can be separated by waveform analysis from aged cells with similar remaining capacity. In contrast to other works, the proposed method identifies the past operating conditions in the cell, regardless of the actual State of Health. The methodology is based on a data-driven approach by using a SOM (Self-organizing map), an unsupervised neural network. To verify the hypothesis a SOM has been trained with laboratory data from whole data cycles, to classify cells concerning their degradation path and according to their discharge voltage patterns. Additionally, this new methodology based on the SOM allows discriminating groups of cells with different cycling conditions (based on depth of discharge, ambient temperature and discharge current). This research line is very promising for classification of used cells, not only depending on their current static parameters (capacity, impedance), but also the battery use in their past life. This will allow making predictions of the Remaining Useful Life (RUL) of a battery with greater precision.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}