413 research outputs found
Enhanced Kalman Filter-Based Identification of a Fuel Cell Circuit Model in Impedance Spectroscopy Tests
No full textModel parameters identification plays an important role in enhancing the currently available diagnosis techniques for fuel cells (e.g. electrochemical impedance spectroscopy). In this work, the dual Kalman filter (DKF) has been used for the parametric identification of a Randles circuit model. The fuel cell has been stimulated with typical EIS input signals, and the results of the identification have been validated by using the impedance spectra produced by the Fouquet impedance model. The obtained results allow to infer a functional relation between the filter settings and the input signal, thus enabling the possibility of detecting faults by inspecting the deviation of model parameters
EIS Diagnostics for Fuel Cells/VRFBs
No full textThis chapter reviews the application of electrochemical impedance spectroscopy (EIS) to fuel cells (FCs) and redox flow batteries (RFBs). The work is focused specifically on diagnostic aspects and large-scale systems. The basics of EIS are briefly summarized, alongside their theoretical premises, good practices and typical laboratory equipment. The online applicability of EIS is also discussed. The interpretation of the EIS response is presented with particular reference to the model-based approaches. The chapter continues presenting the EIS application to proton exchange membrane FCs, discussing the specific phenomena affecting the impedance response. Online and on-board applications of EIS to stacks and large-scale systems are also analyzed. The EIS application to all-vanadium RFBs is also discussed, with particular attention to large-scale systems
Open Circuit Voltage of Lithium-ion batteries for energy storage in DC microgrids
No full textRechargeable batteries, particularly Lithium-ion ones, are emerging as a solution for energy storage in DC microgrids. This paper reviews the issues faced in the characterization of the Open Circuit Voltage (OCV) of a Lithium-ion battery, starting from the problem of OCV measurement and ending with the modeling of OCV hysteresis. An accurate OCV modeling is necessary for a reliable estimation of the internal battery states, such as State-of-Charge and State-of-Health. These state variables are useful for a better control and a more efficient utilization of the energy storage system in the microgrid. We also compare with experiments two models that account for the hysteresis in Lithium-Iron-Phosphate batteries
Photovoltaic-Fed LED Lighting System with SOC-Based Dimmable LED Load
No full textThis paper deals with the design of a PhotoVoltaic
(PV)-fed Light Emitting Diode (LED) lighting system including
an energy storage unit. The system is aimed at exploiting the
generated energy in the absence of sunshine, provided that the
power absorbed by the LED load can be varied within a power
range corresponding to a sufficient lighting brightness. The
main focus of this work is to discuss issues related to the sizing
of the main components of the lighting system, i.e., the PV panel
and the battery, for a dimmable LED load. The paper proposes
a battery State Of Charge (SOC)-based LED dimming allowing
to minimize the components’ size. The discussion is based on the
minimization of the following two quantities: 1) the excess of
energy produced by the PV source, which the battery cannot
store, and 2) the missing energy that the battery cannot deliver
to the load. The application of the proposed methodology to a
PV-fed LED-based street lighting system with dimmable LED
load are presented and discussed
Rao-Blackwellised Particle Filter for Battery State-Of-Charge and Parameters Estimation
No full textState-of-charge and parameters online estimation is one of the key features of battery management systems for hybrid-electric vehicles applications. Using model-based approaches, simultaneous sequential Bayesian estimation of battery state and parameters has been shown to be a very powerful tool for the tracking, even in the presence of non-perfectly known models. Monte Carlo implementations are very suited to strongly nonlinear and unreliable dynamics, such those of batteries. In this framework, current paper proposes the use of a Rao-Blackwellized Particle Filter (RBPF) for the joint estimation of battery state and parameters. The results are compared with the existing approaches, highlighting the appealing features of RBPF, both in terms of performances and robustness
Comparing Particle Filter and Extended Kalman Filter for Battery State-Of-Charge Estimation
No full textThe battery State-Of-Charge (SOC) and parameters estimation is one of the crucial points to be addressed in the development of innovative electric/hybrid electric vehicles. Extended Kalman Filter (EKF) and Particle Filters (PF) are two possible approaches to the problem. While EKF is attractive for its computational efficiency, it may not be accurate for the non-linearity and for the uncertainties involved in the battery modelling. PF is a promising alternative, even if it is computationally more demanding. In this paper, we compare the EKF and PF performance in the dual Bayesian estimation of battery state and parameters, with particular reference to lithium batteries, showing that PF is attractive, especially in the presence of inaccurate battery models
State of health assessment of Li-ion batteries using a multiple linear regression model
No full textIn this study, we investigate the impact of peak area in the incremental capacity curve, current, temperature, and charge capacity on battery performance through a regression model. We employ data from the University of Michigan Battery Lab to characterise the degradation of lithium-ion pouch cells under different cycling conditions. We propose two linear regression models, with and without interaction terms, to describe the relationships between the battery capacity and three independent variables: the test current, the temperature of the battery, and the area under the main peak of the IC curve. The results show that the inclusion of interaction terms in the model significantly improves the accuracy of the model, especially under varying thermal and current conditions
Rich, Sturmian, and trapezoidal words
No full textIn this paper we explore various interconnections between rich words, Sturmian words, and trapezoidal words. Rich words, first introduced by the second and third authors together with J. Justin and S. Widmer, constitute a new class of finite and infinite words characterized by having the maximal number of palindromic factors. Every finite Sturmian word is rich, but not conversely. Trapezoidal words were first introduced by the first author in studying the behavior of the subword complexity of finite Sturmian words. Unfortunately this property does not characterize finite Sturmian words. In this note we show that the only trapezoidal palindromes are Sturmian. More generally we show that Sturmian palindromes can be characterized either in terms of their subword complexity (the trapezoidal property) or in terms of their palindromic complexity. We also obtain a similar characterization of rich palindromes in terms of a relation between palindromic complexity and subword complexity
ELECTRIMACS 2019 Selected papers - Volume 1
No full textThis book collects a selection of papers presented at ELECTRIMACS 2019, the 13th international conference of the IMACS TC1 Committee, held in Salerno, Italy, on 21st-23rd May 2019. The conference papers deal with modelling, simulation, analysis, control, power management, design optimization, identification and diagnostics in electrical power engineering. The main application fields include electric machines and electromagnetic devices, power electronics, transportation systems, smart grids, electric and hybrid vehicles, renewable energy systems, energy storage, batteries, supercapacitors and fuel cells, and wireless power transfer. The contributions included in Volume 1 are particularly focused on electrical engineering simulation aspects and innovative applications
Broad band modeling of a superconducting magnetic energy storage (SMES) coil
No full textIn this paper, we compute with accuracy the impedance parameters of a superconducting magnetic energy storage (SMES) system. We do this by using a three-dimensional integral formulation of the full set of frequency domain Maxwell's equations in the quasistatic limit. The impedance parameter allows the construction of a time domain equivalent circuit to be used for the simulation of the overall SMES system. The numerical model is based on a volume integral formulation where the unknown is the total current density J, expressed as the sum of its solenoidal and non-solenoidal components. This separation allows to avoid the ill-conditioning of the relevant stiffness matrix at low frequencies, being essential for developing a numerical model accurately working where the SMES resonances are located. The model is applied to the case of an ideal model coil consisting of an 18-layers solenoid
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