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

Biological System Impedance Identification Using Stochastic Estimation and Control

In an effort to find a less invasive way of testing for different cell abnormalities and finding more practical tests for different cellular mutations, this project makes use of a well-known technique called cellular impedance spectroscopy coupled with stochastic estimation. Impedance spectroscopy, the measurement of the complex resistance of a biological body, is not a new technology; it has been around for many years and has been used to make electrical representations of different biological systems. The problem with this procedure is that models cannot be used for system identification. Stochastic estimation can complement a model produced by analyzing the input/output characteristics of a cell sample to account for modeling inadequacies produced by the linear modeling of electrical impedance spectroscopy alone. In this thesis, biological cell samples were submitted to a sinusoidal voltage at a different range of frequencies. The cell samples created an output which was used to model the electrical behavior of the biological system. This electrical representation was used to build a fixed-interval stochastic smoother. The stochastic smoother was then used to estimate the output measurements of different cell samples and ultimately identify a cell type based on the evaluation of the residuals produced. Results show that, given residual values, one could apply a binary logic windowing technique that would show a difference in the cell samples tested, thereby being able to identify the cell sample in question

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

Recognition of fibrotic infarct density by the pattern of local systolic-diastolic myocardial electrical impedance

Myocardial electrical impedance is a biophysical property of the heart that is influenced by the intrinsic structural characteristics of the tissue. Therefore, the structural derangements elicited in a chronic myocardial infarction should cause specific changes in the local systolic-diastolic myocardial impedance, but this is not known. This study aimed to characterize the local changes of systolic-diastolic myocardial impedance in a healed myocardial infarction model. Six pigs were successfully submitted to 150 min of left anterior descending (LAD) coronary artery occlusion followed by reperfusion. 4 weeks later, myocardial impedance spectroscopy (1–1000 kHz) was measured at different infarction sites. The electrocardiogram, left ventricular (LV) pressure, LV dP/dt, and aortic blood flow (ABF) were also recorded. A total of 59 LV tissue samples were obtained and histopathological studies were performed to quantify the percentage of fibrosis. Samples were categorized as normal myocardium (50%). Resistivity of normal myocardium depicted phasic changes during the cardiac cycle and its amplitude markedly decreased in dense scar (18 ± 2 ·cm vs. 10 ± 1 ·cm, at 41 kHz; P < 0.001, respectively). The mean phasic resistivity decreased progressively from normal to heterogeneous and dense scar regions (285 ± 10 ·cm, 225 ± 25 ·cm, and 162 ± 6 ·cm, at 41 kHz; P < 0.001 respectively). Moreover, myocardial resistivity and phase angle correlated significantly with the degree of local fibrosis (resistivity: r = 0.86 at 1 kHz, P < 0.001; phase angle: r = 0.84 at 41 kHz, P < 0.001). Myocardial infarcted regions with greater fibrotic content show lower mean impedance values and more depressed systolic-diastolic dynamic impedance changes. In conclusion, this study reveals that differences in the degree of yocardial fibrosis can be detected in vivo by local measurement of phasic systolic-diastolic bioimpedance spectrum. Once this new bioimpedance method could be used via a catheter-based device, it would be of potential clinical applicability for the recognition of fibrotic tissue to guide the ablation of atrial or ventricular arrhythmias.Award-winningPostprint (published version

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Cation Discrimination in Organic Electrochemical Transistors by Dual Frequency Sensing

In this work, we propose a strategy to sense quantitatively and specifically cations, out of a single organic electrochemical transistor (OECT) device exposed to an electrolyte. From the systematic study of six different chloride salts over 12 different concentrations, we demonstrate that the impedance of the OECT device is governed by either the channel dedoping at low frequency and the electrolyte gate capacitive coupling at high frequency. Specific cationic signatures, which originates from the different impact of the cations behavior on the poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) polymer and their conductivity in water, allow their discrimination at the same molar concentrations. Dynamic analysis of the device impedance at different frequencies could allow the identification of specific ionic flows which could be of a great use in bioelectronics to further interpret complex mechanisms in biological media such as in the brain.Comment: Full text and supporting informatio