3,104 research outputs found
Identifiability and parameter estimation of the single particle lithium-ion battery model
This paper investigates the identifiability and estimation of the parameters
of the single particle model (SPM) for lithium-ion battery simulation.
Identifiability is addressed both in principle and in practice. The approach
begins by grouping parameters and partially non-dimensionalising the SPM to
determine the maximum expected degrees of freedom in the problem. We discover
that, excluding open circuit voltage, there are only six independent
parameters. We then examine the structural identifiability by considering
whether the transfer function of the linearised SPM is unique. It is found that
the model is unique provided that the electrode open circuit voltage functions
have a known non-zero gradient, the parameters are ordered, and the electrode
kinetics are lumped into a single charge transfer resistance parameter. We then
demonstrate the practical estimation of model parameters from measured
frequency-domain experimental electrochemical impedance spectroscopy (EIS)
data, and show additionally that the parametrised model provides good
predictive capabilities in the time domain, exhibiting a maximum voltage error
of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on
Control Systems Technolog
General parameter identification procedure and comparative study of Li-Ion battery models
Accurate and robust battery models are required for the proper design and operation of battery-powered systems. However, the parametric identification of these models requires extensive and sophisticated methods to achieve enough accuracy. This article shows a general and straightforward procedure, based on Simulink and Simscape of Matlab, to build and parameterize Li-ion battery models. The model parameters are identified with the Optimization Toolbox of Matlab, by means of an iterative process to minimize the sum of the squared errors. In addition, this procedure is applied to a selection of five different models available in the literature for electric vehicle applications, obtaining a comparative study between them. Also, the performance of each battery model is evaluated through two current profiles from two driven profiles known as the Urban Driving Cycle (ECE-15 or UDC) and the Hybrid Pulse Power Characterization (HPPC). The experimental results obtained from a Li-ion polymer battery have been compared with the data provided by the models, confirming the effectiveness of the proposed procedure, and also, the application field of each model as a function of the required accuracy.This work was supported by the Ministry of Economy and Competitiveness and FEDER funds through the research project “Storage and Energy Management for Hybrid Electric Vehicles based on Fuel Cell, Battery, and Supercapacitors”—ELECTRICAR-AG-(DPI2014- 53685-C2-1-R)
Towards Better Understanding of Failure Modes in Lithium-Ion Batteries: Design for Safety
In this digital age, energy storage technologies become more sophisticated and more widely used as we shift from traditional fossil fuel energy sources to renewable solutions. Specifically, consumer electronics devices and hybrid/electric vehicles demand better energy storage. Lithium-ion batteries have become a popular choice for meeting increased energy storage and power density needs. Like any energy solution, take for example the flammability of gasoline for automobiles, there are safety concerns surrounding the implications of failure. Although lithium-ion battery technology has existed for some time, the public interest in safety has become of higher concern with media stories reporting catastrophic cellular phone- and electric vehicle failures. Lithium-ion battery failure can be dangerously volatile. Because of this, battery electrochemical and thermal response is important to understand in order to improve safety when designing products that use lithium-ion chemistry. The implications of past and present understanding of multi-physics relationships inside a lithium-ion cell allow for the study of variables impacting cell response when designing new battery packs. Specifically, state-of-the-art design tools and models incorporate battery condition monitoring, charge balancing, safety checks, and thermal management by estimation of the state of charge, state of health, and internal electrochemical parameters. The parameters are well understood for healthy batteries and more recently for aging batteries, but not for physically damaged cells. Combining multi-physics and multi-scale modeling, a framework for isolating individual parameters to understand the impact of physical damage is developed in this work. The individual parameter isolated is the porosity of the separator, a critical component of the cell. This provides a powerful design tool for researchers and OEM engineers alike. This work is a partnership between a battery OEM (Johnson Controls, Inc.), a Computer Aided Engineering tool maker (ANSYS, Inc.), and a university laboratory (Advanced Manufacturing and Design Lab, University of Wisconsin-Milwaukee). This work aims at bridging the gap between industry and academia by using a computer aided engineering (CAE) platform to focus battery design for safety
Dynamic model of lithium polymer battery: Load resistor method for electric parameters identification
Maximum battery runtime and its transients behaviors are crucial in many applications. With accurate battery models in hand, circuit designers can evaluate the performance of its developments considering the influence of a finite source of energy which has a particular dynamics; as well as the energy storage systems can be optimized. First, this work describes a complete dynamic model of a lithium polymer battery. In the sequel a simple and novel procedure is used to obtain the electric parameters of adopted model with the advantage of using only one resistor to represent the battery load and a pc-connected multimeter. The methodology used to identify the parameters of the battery model is simple, clearly explained and can be applied to various types of batteries. Simulation and experimental results are presented and discussed, demonstrating the good performance of the proposed identification methodology.Fil: Gandolfo, Daniel. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; ArgentinaFil: Brandao, Alexandre. Universidade Federal de Viçosa; BrasilFil: Patiño, HĂ©ctor Daniel. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de Automática; ArgentinaFil: Molina, Marcelo Gustavo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de IngenierĂa. Instituto de EnergĂa ElĂ©ctrica; Argentin
An accurate time constant parameter determination method for the varying condition equivalent circuit model of lithium batteries.
An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model
Identifiability of generalised Randles circuit models
The Randles circuit (including a parallel resistor and capacitor in series
with another resistor) and its generalised topology have widely been employed
in electrochemical energy storage systems such as batteries, fuel cells and
supercapacitors, also in biomedical engineering, for example, to model the
electrode-tissue interface in electroencephalography and baroreceptor dynamics.
This paper studies identifiability of generalised Randles circuit models, that
is, whether the model parameters can be estimated uniquely from the
input-output data. It is shown that generalised Randles circuit models are
structurally locally identifiable. The condition that makes the model structure
globally identifiable is then discussed. Finally, the estimation accuracy is
evaluated through extensive simulations
Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement
This study presents a method of estimating battery cell core and surface
temperature using a thermal model coupled with electrical impedance
measurement, rather than using direct surface temperature measurements. This is
advantageous over previous methods of estimating temperature from impedance,
which only estimate the average internal temperature. The performance of the
method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate
cell fitted with surface and core thermocouples for validation. An extended
Kalman filter, consisting of a reduced order thermal model coupled with
current, voltage and impedance measurements, is shown to accurately predict
core and surface temperatures for a current excitation profile based on a
vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same
thermal model and impedance measurement input is capable of estimating the
convection coefficient at the cell surface when the latter is unknown. The
performance of the DEKF using impedance as the measurement input is comparable
to an equivalent dual Kalman filter using a conventional surface temperature
sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on
Sustainable Energy, 201
Comparison of State and Parameter Estimators for Electric Vehicle Batteries
A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application
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