Deep Learning-Enhanced Parameter Extraction for Equivalent Circuit Modeling in Electrochemical Impedance Spectroscopy

Reliable and automatic parameter extraction in equivalent circuit modeling of electrochemical impedance spectroscopy (EIS) could be a challenge as the common circuit fitting method, complex nonlinear least-squares (CNLS), heavily depends on the initial guesses. To prevent the adjustment of the initial guess that demands extra time and experience, we propose employing a deep learning-based convolutional neural network (CNN) to perform the pre-fitting of the measured impedance spectrum. This approach not only facilitates the convergence dynamics of CNLS but also manifests a notable enhancement in parameter extraction fidelity, especially when benchmarked against conventional methodologies. The improvement of 25% in fitting success rate is demonstrated on an open-source impedance dataset by comparing to CNLS with random initials and the traditional stochastic methods including differential evolution and simulated annealing. Thus, we believe the proposed pre-fitting method can provide a useful tool for reliable parameter extraction with the uncertainty minimized to explore the underlying mechanism from EIS and automate this process for the analysis of a large amount of data

Montecarlo based quantitative Kramers-Kronig test for PEMFC impedance spectrum validation

Electrochemical Impedance Spectroscopy (EIS) is a very powerful tool to study the behaviour of electrochemical systems. At present, it is widely used in the fuel cell field in order to study challenging cutting edge issues as membrane drying or gas diffusion layer flooding amongst others. The proper analysis of impedance data requires the fulfilment of four fundamental conditions: causality, linearity, stability and finiteness. The non compliance with any of these conditions may lead to biased, or even misguided, conclusions. Therefore it is critical to verify the compliance of these conditions before accepting any analysis performed on an experimental spectrum. This is even more important in a fuel cell experimental spectrum analysis, since fuel cells are markedly non stationary systems. The aim of this work is to establish an impedance spectrum quantitative validation technique to validate the whole experimental spectrum and to identify the individual points within a spectrum that do not comply any of the four conditions, in order to remove these inconsistent points from the analysis. The designed validation method consists in a Kramers Kronig (KK) validation test, by equivalent electrical circuit fitting, coupled with a Montecarlo error propagation method. In a first step, the experimental spectrum is fitted to a particular electrical equivalent circuit, which satisfies the KK relations. Then, in a second step, a statistical Montecarlo method is used in order to propagate the model fitting parameter uncertainty through the model. Using this approach, a consistency region is built for a given confidence level: the experimental points inside this region are considered consistent for the given confidence level, whereas the outside points are rejected. The method was used on PEMFC experimental impedance spectra; and it successfully managed to identify inconsistent points, associated to no stationarities.The authors are very grateful to the Generalitat Valenciana for its economic support in form of Vali+d grant (Ref: ACIF-2013-268).Giner Sanz, JJ.; Ortega Navarro, EM.; Pérez-Herranz, V. (2015). Montecarlo based quantitative Kramers-Kronig test for PEMFC impedance spectrum validation. International Journal of Hydrogen Energy. 40(34):11279-11293. https://doi.org/10.1016/j.ijhydene.2015.03.135S1127911293403

Modeling of the Electrochemical Reactions at the Electrode-Electrolyte Interface of Nickel/Metal Hydride Batteries by an Equivalent Electrical Circuit

Based on the experimental impedance spectra, the electrochemical reactions that are deposed at the electrode-electrolyte interface can be modeled by equivalent electrical circuits. Each element used in the circuit must have a physical correspondence in the electrochemical system. In this work, a model has been proposed to a NiMH battery electrode to describe, in detail, the electrochemical process at the interface of this electrode. The theoretical impedance of a proposed circuit is a function of several variables. These adjusted variables to reach a good agreement between the theoretical spectra and the experimental spectra in the studied frequency. The Z-simplex software allows refining the experimental results. These results show a good superposition between the experimental spectra and the theoretical spectra corresponding to the proposed electric circuit. This leads to the conclusion that the proposed circuit describes the phenomena that take place at the interface of the hydride electrode

LITHIUM-ION BATTERY DEGRADATION EVALUATION THROUGH BAYESIAN NETWORK METHOD FOR RESIDENTIAL ENERGY STORAGE SYSTEMS

Batteries continue to infiltrate in innovative applications with the technological advancements led by Li-ion chemistry in the past decade. Residential energy storage is one such example, made possible by increasing efficiency and decreasing the cost of solar PV. Residential energy storage, charged by rooftop solar PV is tied to the grid, provides household loads. This multi-operation role has a significant effect on battery degradation. These contributing factors especially solar irradiation and weather conditions are highly variable and can only be explained with probabilistic analysis. However, the effect of such external factors on battery degradation is approached in recent literature with mostly deterministic and some limited stochastic processes. Thus, a probabilistic degradation analysis of Li-ion batteries in residential energy storage is required to evaluate aging and relate to the external causal factors. The literature review revealed modified Arrhenius degradation model for Li-ion battery cells. Though originating from an empirical deterministic method, the modified Arrhenius equation relates battery degradation with all the major properties, i.e. state of charge, C-rate, temperature, and total amp-hour throughput. These battery properties are correlated with external factors while evaluation of capacity fade of residential Li-ion battery using a proposed detailed hierarchical Bayesian Network (BN), a hierarchical probabilistic framework suitable to analyze battery degradation stochastically. The BN is developed considering all the uncertainties of the process including, solar irradiance, grid services, weather conditions, and EV schedule. It also includes hidden intermediate variables such as battery power and power generated by solar PV. Markov Chain Monte-Carlo analysis with Metropolis-Hastings algorithm is used to estimate capacity fade along with several other interesting posterior probability distributions from the BN. Various informative and promising results were obtained from multiple case scenarios that were developed to explore the effect of the aforementioned external factors on the battery. Furthermore, the methodologies involved to perform several characterizations and aging test that is essential to evaluate the estimation proposed by the hierarchical BN is explored. These experiments were conducted with conventional and low-cost hardware-in-the-loop systems that were developed and utilized to quantify the quality of estimation of degradation

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.Special thanks should also be expressed for the Torres Quevedo (PTQ) 2019 Aid from the State Research Agency, within the framework of the State Program for the Promotion of Talent and its Employability in R + D + i, Ref. PTQ2019-010787 /AEI/10.13039/501100011033

Nonlinear Stochastic Filtering for Online State of Charge and Remaining Useful Life Estimation of Lithium-ion Battery

Battery state monitoring is one of the key techniques in Battery Management System (BMS). Accurate estimation can help to improve the system performance and to prolong the battery lifetime. The main challenges for the state online estimation of Li-ion batteries are the flat characteristic of open circuit voltage (OCV) with the function of the state of charge. Hence, the focus of this thesis study is to estimation of the state of charge (SOC) of Li-ion with high accuracy, more robustness. A 2nd order RC equivalent circuit model is adapted to battery model for simulation, mathematical model analysis, and dynamics characteristic of battery study. Model parameters are identified with MATLAB battery model simulation. Although with more lumped RC loaders, the model is more accurate, high computation with a higher nonlinear function of output will be. So, a discrete state space model for the battery is developed. For a complex battery model with strong nonlinearity, Sequential Monte Carlo (SMC) method can be utilized to perform the on-line SOC estimation. An SMC integrates the Bayesian learning methods with sequential importance sampling. SMC approximate the posterior density function by a set of particles with associated weights, which is developed in MATLAB environment to estimate on-line SOC. A comparison is presented with Kalman Filtering and Extended Kalman Filtering to validated estimation results with SMC. Finally, the comparison results provide that SMC method is more accurate and robust then KF and EKF. Accurately prediction of Remaining Useful Life of Li-ion batteries is necessary to reliable system operation and monitoring the BMS. An empirical model for capacity degradation has been developed based on experimentally obtained capacity fade data. A nonlinear, non-Gaussian state space model is developed for empirical model. The obtained empirical model used in Sequential Monte Carlo (SMC) framework is to update the on-line state and model parameters to make a prediction of remaining useful life of a Li-ion battery at various lifecycle

Fuel Cell Impedance Model Parameters Optimization using a Genetic Algorithm

The objective of this paper is the PEM fuel cell impedance model parameters identification. This work is a part of a larger work which is the diagnosis of the fuel cell which deals with the optimization and the parameters identification of the impedance complex model of the Nexa Ballard 1200 W PEM fuel cell. The method used for the identification is a sample genetic algorithm and the proposed impedance model is based on electric parameters, which will be found from a sweeping of well determined frequency bands. In fact, the frequency spectrum is divided into bands according to the behavior of the fuel cell. So, this work is considered a first in the field of impedance spectroscopy So, this work is considered a first in the field of impedance spectroscopy. Indeed, the identification using genetic algorithm requires experimental measures of the fuel cell impedance to optimize and identify the impedance model parameters values. This method is characterized by a good precision compared to the numeric methods. The obtained results prove the effectiveness of this approach

A review on electric vehicle battery modelling: from lithium-ion toward lithium–sulphur

Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed

An experimental and modelling approach to study the performance and degradation of low temperature electrolyzers

L'abstract è presente nell'allegato / the abstract is in the attachmen

Integration Of Electrical Impedance Spectroscopy For Multichannel Cell Culture Measurement

ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY (EIS) has been widely used to study the electrical properties of biological material due to its non-invasive nature and experimental reliability. However, most of the precision impedance analyzers used in EIS only provide single- or two-channel measurements which are inadequate for larger-scale multiplexed measurements, such as those found in modern microfluidic cell culture experiments. The Biomedical Microsystems Laboratory has developed a 16-channel cell culture platform with integrated electrode arrays for monitoring cell growth and electrical properties (i.e., the so-called “electrical phenotype”). In this paper, a system consisting of a 16-channel solid-state analog multiplexer (MUX)paired with a low-cost, impedance analyzer is developed to replace high-cost physical relay MUX and impedance analyzer systems. System requirements and design constraints for monitoring biological systems are considered and a prototype device was fabricated. Initial testing was performed on a breadboard to verify the feasibility of the design idea. Results identified measurement errors due to parasitic elements in the system. Software compensation successfully corrected for parasitic capacitance in the analog MUX design. The accuracy of the measurement system was evaluated on a developed Printed Circuit Board Assembly (PCBA) by comparing theoretical values to MUX compensated data. Finally, an EIS experiment was carried out with tap water with the PCBA system, and measurement results were analyzed using an equivalent Circuit Model (ECM). These results successfully captured the dynamics of charge transport in the electrical double layer, consistent with a modified-Randlecell ECM