Electrochemical Model Research Articles

Research on aging mechanism and capacity attenuation of automotive lithium-ion batteries

In order to investigate the internal mechanism and the variation law of capacity attenuation of LIBs, a simplified electrochemical model of the LIBs was established using the nickel-cobalt-aluminum LIBs as the research object, and the aging model of solid electrolyte interface SEI growth and lithium evolution was added to simulate the electrochemical behavior of the batteries. The results showed that the porosity of the anode/diaphragm reduced continually at the start of the cycle, while the SEI film grew continuously. The loss of active materials accelerated as the number of cycles increased, while the side reaction of lithium development continued. Finally, the aging mechanism was confirmed by using microscopic morphology. The results show that the negative particles are obviously crushed and contain white deposited substances. The formation of these compounds increases the oxygen content, reduces the carbon content, reduces the porosity of the electrode, increases the overpotential of the electrolyte transport, and becomes the main reason for the attenuation of the battery life. Finally, the positive particles expand and break, which leads to the sudden reduction of the battery capacity. This study determined the evolution process of aging mechanism in the whole life cycle, and provided a theoretical basis for the establishment of mechanism aging model.

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

A multi-scale modeling approach for predicting and mitigating thermal runaway in electric vehicle batteries

This study presents a unique multi-scale modelling technique for electric vehicle (EV) battery thermal runaway prediction models using electrochemical, thermal, and machine learning approaches. This modelling framework has illuminated the thermal runaway’s physical process and the race between heat production and dissipation at the cell module, and pack levels.Experimental evaluation has demonstrated solid predictive performance for each component. The modified pseudo-two-dimensional (P2D) electrochemical model has achieved high voltage prediction accuracy (mean absolute error: 8.5 mV), and the 3D thermal model has captured battery module temperatures with an average error of ± 1.5 °C.The machine learning model has exhibited 96.8% accuracy, excellent classification precision, and an 18.3-minute early warning time. It was also demonstrated that the integrated multi-scale model outperformed standalone single-scale models with 15% higher prediction accuracy and 32% higher average early warning time.Based on these findings, adaptive cooling techniques, unique charge/discharge procedures, and early isolation methods were created. Simulations showed that these mitigation techniques decreased thermal runaway occurrence by 78% and severity by 93%. Despite its high computing cost, this technique might improve EV battery safety, guide battery pack design, and accelerate EV adoption, which would cut carbon emissions.

Unveiling graphite anodic expansion: Insights from electrochemical techniques, mass spectrometry, and kinetic modelling

Anodic expansion of graphite at different pH has been studied by coupling a mass spectrometer to an electrochemical cell. The mass spectrometry technique permits to follow the electrochemical gasification of graphite as well as the reaction products formed from the electrolyte. The current and mass spectrometry signals suggest that the neutral medium is the least aggressive for expansion. In addition, chronoamperometric expansion of graphite at different potentials has been carried out using the neutral medium. This experimental approach permits to get detailed information about the reaction mechanism that produces graphene-oxide based materials. The effect of potential and electrolyte concentration have been studied, and it has been observed that the degree of oxidation of the graphene oxide material obtained can be controlled. In addition, a kinetic model based on a phase-moving boundary has been used to describe the chronoamperometric experiments. Two types of graphene oxide materials have been obtained with the anodic synthesis and have been classified as few-layer graphene oxide (FLGO) and graphene oxide quantum dots (GOQDs). The physicochemical and electrochemical properties of both products have been studied.

Testing and Assessment of Various Catalysts for Uniquely Designed Cathodes for Hydrogen Evolution Reactions

This study reports the electrodeposition of unique catalysts for 3D-printed cathodes for alkaline water electrolysis. This paper explores hydrogen evolution reaction performance for various nickel, nickel-copper, nickel-iron, and nickel-molybdenum 3D-printed electrodes. In particular, this study reports a novel electrodeposition of nickel-iron and nickel-molybdenum on conductive PLA 3D-printed electrode surfaces. The performance of the electrodes is assessed through electrochemical models including cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). The results of the study show considerable differences, at a particular current density of 10 mA/cm2, nickel-iron, and nickel-copper coated 3D-printed electrodes are found to have an overpotential of 270 mV and 275 mV respectively. The nickel-copper and nickel-iron coated electrodes also showed to have a low resistance. The amount of metal deposited also showed to have an important role. At a potential of -2.5V, nickel coated electrode Ni4x with four times the nickel mass deposition (0.178 g/cm2) of another nickel coated electrode Ni1x (0.044 g/cm2) is found to have a current density of -106 mA/cm2 in comparison to -44 mA/cm2 respectively. These findings provide important insights for optimizing additive manufacturing for electrochemical systems.

Spatial‐Dependent Coupling of Electrochemistry, Mass Transport, and Stress in Silicon‐Graphite Composite Electrodes for Lithium‐Ion Batteries

AbstractThe commercialization of high‐capacity silicon materials in lithium‐ion batteries is hindered by significant volume changes. Composite anodes made from silicon and graphite, which increase battery capacity and maintain electrode structural stability, are receiving considerable attention. However, the spatial configuration of the active particles in the electrode is few investigated due to the complexity of experiments at the microscopic scale. Herein, this work focuses on electrochemical, mass transport, and stress coupling mechanisms by considering different spatial configurations of silicon and graphite. In situ electrochemical and stress measurements are first conducted to demonstrate the impact of the active material arrangement. Then, a 2D electrochemical‐mechanical model is developed considering the heterogeneity of electrochemical processes at the particle‐electrolyte interface. The results show that placing silicon in the upper active layer significantly reduces the ion transport resistance, while the graphite layer near the current collector provides a good conductive network for electron transport in the silicon layer, enhancing the performance of the composite electrode structure. By combining electrochemical and mechanical field models with experimental verification, this study deepens the understanding of composite electrode structure design, offering practical guidance for optimizing the spatial configuration of electrode materials to significantly improve battery performance.

The Effects of Polymerization on the Performance of Viologen-Based Electrochromic Devices

In this work, electrochromic devices were prepared using the redox couple ethyl viologen diperchlorate and 1,1′-diethyl ferrocene in propylene carbonate as an aprotic solvent to facilitate ions separation and diffusion inside the devices. Electrochromic devices were made using electrochromic gel mixtures at the concentrations of 55%, 60% and 65% with respect to the bisphenol A polymer. In particular, two sets of gels were made: one set contained the bisphenol A not-polymerized while and the second one contained the polymerized polymer. Different techniques, such as cyclic voltammetry, UV-vis-NIR, and Raman spectroscopy, were used to study such systems to understand the differences in terms of performances between the different sets of electrochromic devices. Cyclic voltammetry confirmed that the oxidation process of the 1,1′-diethyl ferrocene and the reduction of the ethyl viologen diperchlorate occurred at about 0.4 V. Interesting variations in the transmittances were found between the two groups of samples. The best values of CE were provided by the electrochromic devices based on the polymerized electrochromic gel mixture at a concentration of 60% (EM60). The EM60 device result was CE = 92.82 C/cm2 in the visible region and CE = 80.38 C/cm2 in the near–infrared region, confirming that these devices can be used for energy-saving applications. A structural characterization of the materials used in the two sets of electrochromic devices was made using Raman spectroscopy, and the analysis supports the electrochemical models used to explain the processes involved during operation of the electrochromic systems.

Optimization of electrode thickness of lithium-ion batteries for maximizing energy density

AbstractThe demand for high capacity and high energy density lithium-ion batteries (LIBs) has drastically increased nowadays. One way of meeting that rising demand is to design LIBs with thicker electrodes. Increasing electrode thickness can enhance the energy density of LIBs at the cell level by reducing the ratio of inactive materials in the cell. However, after a certain value of electrode thickness, the rate of energy density increase becomes slower. On the other hand, the impact of associated limitations becomes stronger, reducing the practical applicability of LIBs with thicker electrodes. Hence, an optimum value of thickness is of utmost importance for the practicability of thicker electrode design. In this paper, both the cathode thickness and the anode thickness of an NCM LIB cell were optimized by applying response surface methodology (RSM) with a Box-Behnken design (BBD) to maximize the energy density. Moreover, the influence of electrode porosity, together with the interaction of porosity with cathode and anode thickness, was incorporated into the optimization. A full factorial design of 3-level, 3-factor was used to generate 15 simulation conditions in accordance with the design of experiment (DoE) achieved through BBD. Then, those conditions were used to achieve 15 responses by simulating a reduced-order electrochemical model. Finally, the statistical technique analysis of variance (ANOVA) was used to analyze and validate the results of RSM. The results show that the RSM-BBD optimization method, coupled with ANOVA, has successfully optimized the thicknesses of both positive and negative electrodes for maximum energy density, despite the nonlinearity of the electrochemical system. The findings suggest an optimized cathode thickness of 401.56 µm and anode thickness of 186.36 µm for a maximum energy density of 292.22 of an NCM LIB cell, while electrode porosity is preferred to be 0.2.

Digital Twin Reveals the Impact of Carbon Binder Domain Distribution on Performance of Lithium‐Ion Battery Cathodes

The rising demand for high‐performance lithium‐ion batteries (LIBs) emphasizes the need for precise electrode design. The carbon binder domain (CBD) within electrodes, crucial for electron transport and structural integrity, can impede lithium‐ion transport and reduce electrochemically active sites. This study leverages digital twin technology to investigate the impact of CBD distribution along the electrode thickness on LIB cathode performance. Virtual LIB cathodes with varying CBD configurations are generated, and their performance using 3D heterogeneous electrochemical modeling is evaluated. The results reveal that a steep CBD gradient significantly diminishes charge capacity compared to a mild gradient, particularly at higher current rates. This reduction is attributed to an increased tortuosity factor and an uneven distribution of electrochemical activity, which adversely affect mass transport and electrochemical reaction dynamics. These effects are more pronounced at lower porosities and higher loading levels. These findings highlight that optimizing CBD distribution is critical for advancing electrode design and enhancing LIB cathode performance.

Modeling of Catalyst Degradation in Polymer Electrolyte Membrane Fuel Cells Applied to Three‐Dimensional Computational Fluid Dynamics Simulation

ABSTRACTIn a polymer electrolyte membrane (PEM) fuel cell, the following degradation mechanisms are associated with the catalyst particles and their support: carbon support corrosion triggered by carbon and platinum oxidation, platinum dissolution with redeposition, and particle detachment with agglomeration. In this work, an electrochemical model for those degradation effects is presented as well as its coupling with a three‐dimensional computational fluid dynamics PEM fuel cell performance model. The overall model is used to calculate polarization curves and current density distributions of a PEM fuel cell in a fresh and aged state as well as the degradation process during an accelerated stress test with 30 000 voltage cycles. The obtained simulation results are compared to measurements on a three‐serpentine channel PEM fuel cell with an active area of 25 cm2 under various temperatures and humidities. The experimental data are obtained with a segmented test cell using respective degradation protocols and test conditions proposed by the United States Department of Energy. In addition to the temperature and humidity changes, the influence of geometry and material parameters on the degree of degradation and the resulting fuel cell performance is explored in detail.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

An assessment of electroneutrality implementations for accurate electrochemical ion transport models

During the diffusion and migration of ions in electrolytes, the electrodynamic ion-ion interactions prevent charge separation despite different ionic mobilities, ultimately enforcing electroneutrality in the bulk electrolyte. To model ion transport accurately, a method to enforce electroneutrality must be implemented. In this study, four strategies to implement electroneutrality are discussed and evaluated. The ion distributions that result from a transport model with the different electroneutrality implementations are calculated, considering various electrolytes and sets of electrochemical parameters. The meaningfulness and applicability of each implementation are assessed through spatial charge accumulations, transference numbers, and experimental data from the literature. Combining the electrochemical ion transport models with the electroneutrality constraint for all ions is shown to result in an overdetermined system of equations if the driving forces are calculated under neglection of diffusion potentials. The often-reported model simplification of using the electroneutrality constraint to resolve the transport of one specific species explicitly results in non-physically correct mass transport. A practical approach to precisely describe the measured physicochemical ion movements is obtained by equilibrating spatial charges with the ion conduction for every time step in the ion transport model, which is reasonably applicable to multi-ion systems in three-dimensional frameworks. This comprehensive assessment aims to guide readers in selecting an appropriate electroneutrality implementation framework for ion transport models.

Construction and Analysis of Battery Equivalent Circuit Model Considering Liquid Phase Lithium Ion Diffusion Under High-Rate Conditions

In order to obtain the internal state of lithium-ion battery quickly and accurately, the problem that the internal state of the battery is difficult to estimate quickly and accurately is solved. The discharge performance of lithium ion is affected by the electrode current during the charge and discharge process. This paper analyzes the key factors of the electrochemical model at high rate, considers the change of lithium ion concentration in the liquid phase of the battery, integrates the electrochemical mechanism and the equivalent circuit, and establishes a mechanism equivalent circuit model with more computational advantages through circuit elements. The equivalent circuit model can describe the physical characteristics of the lithium battery. Under high rate constant current and dynamic conditions, the model shows good electrical characteristics. Compared with the traditional empirical equivalent circuit model, the accuracy of the model is improved, and the state quantity is reduced by 15 compared with the electrochemical model. The model is of great significance in practical application.

Towards electrochemical machining of powder superalloy René 88DT with high surface integrity in different solvent-based electrolytes

Electrochemical machining (ECM) presents numerous applications in the production of aero-engine components. The effects of current density on surface quality during ECM of René 88DT in NaCl-ethylene glycol and NaNO3-aqueous solutions was investigated here. The results indicated that the René 88DT surface has lower sensitivity to current density and more homogeneous electrochemical dissolution in NaCl-glycol solution than in NaNO3-aqueous solution, which results in the suppression of stray corrosion and consistently high surface quality regardless of current density. These strengths are attributed that C2H5O2- and Cl- ions replace OH– ions and combine with alloy ions to form solubles and complexes, which prevent the formation of obstructive passive film, oxide layer, and insoluble electrolytic products. The electrochemical dissolution model in two solutions was established. Furthermore, the high tensile strength and precision machinability of René 88DT in NaCl-glycol solution were also demonstrated.

Understanding intra-pore electrolyte resistances and distributed capacitances in carbon electrodes used in supercapacitors using a robust modeling process to separate the charge-storage occurring into macro-, meso-, and micro-pore structures

In this study, a Modified Generic Electrochemical (MGE) model is introduced for studying rough/porous carbon-based electrode materials employed in electrical double-layer capacitors (EDLC) which contain different types of surface defects. Instead of adopting the common theoretical approach involving the use of transmission line models or ad hoc equivalent circuits, a rational equivalent circuit model has been devised, which permitted us to derive the different equations for each electrochemical technique. The MGE representing the overall electrochemical behavior of EDLCs obtained using the different techniques (e.g., cyclic voltammetry, chronoamperometry, chronopotentiometry, and impedance) is formally derived based on a simple premise considering that the different inhomogeneous surface defects, which are geometrically arranged under equipotential conditions, are not electrochemically equivalent. In this sense, the presence of hierarchically distributed charge-storage processes with different time constants, accounting for the ionic accumulation at the electrode/solution interface, can be ascertained. We verified that is imperative to avoid the determination of the specific capacitances and resistances in EDLCs using a single electrochemical technique or several techniques whose model equations are based on different theoretical premises. The use of the MGE model unequivocally revealed that isolated measurements of the specific capacitance in the absence of the associated intra-pore resistances are not sufficient to ensure the presence of rapid charge storage allied with high-power characteristics. With the appropriate choice of carbon-based materials with very low intra-pore resistances, substantial progress can be made in ushering in the next generation of EDCLs with very high-power characteristics.

A Comprehensive Flow–Mass–Thermal–Electrochemical Coupling Model for a VRFB Stack and Its Application in a Stack Temperature Control Strategy

In this work, a comprehensive multi-physics electrochemical hybrid stack model is developed for a vanadium redox flow battery (VRFB) stack considering electrolyte flow, mass transport, electrochemical reactions, shunt currents, and as heat generation and transfer simultaneously. Compared with other VRFB stack models, this model is more comprehensive in considering the influence of multiple factors. Based on the established model, the electrolyte flow rate distribution across cells in the stack is investigated. The distribution and variation in shunt currents, single-cell current and single-cell voltage are analyzed. The distribution and variation in temperature and heat generation and heat transfer are also researched. It can be found that the VRFB stack temperature will exceed 40 °C when operating at 60 A and 100 mA cm−2 at an ambient temperature of 30 °C, which will lead to electrolyte ion precipitation, affecting the performance and safety of the battery. To control the stack temperature below 40 °C, a new tank cooling control strategy is proposed, and the suitable starting cooling point and the controlled temperature are specified. Compared with the common room cooling strategy, the new tank cooling strategy reduces energy consumption by 27.18% during 20 charge–discharge cycles.

Experimentally verified organic electrochemical transistor model

The Bernards–Malliaras model, published in 2007, is the primary reference for the operation of organic electrochemical transistors (OECTs). It assumes that, as in most transistors, the electronic transport is drift only. However, in other electrochemical devices, such as batteries, the charge neutrality is accompanied by diffusion-only transport. Using detailed 2D device simulations of the entire structure while accounting for ionic and electronic conduction, we show that high ion density (>1019 cm−3) results in Debye screening of the drain–source bias at the electrodes’ interface. Hence, unlike the drift-only current in standard FETs or low ion density OECTs, the current in high ion density OECTs is diffusion only. Also, we show that since in OECTs, the volumetric capacitor and the semiconductor are one, the threshold voltage has a different meaning than that in FETs, where the semiconductor and the gate-oxide capacitor are distinct entities. We use the above insights to derive a new model useful to experimentalists. Lastly, we fabricated PEDOT:PSS fiber-OECTs and used the results to verify the model.

A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison

Lithium-ion batteries (LIBs) are extensively utilized in power and energy storage systems. Accurately estimating state of charge (SOC) of LIBs using single methods is still challenging to fully address the problem associated with SOC. This paper proposes a novel generic fusion framework that integrates deep learning (DL) and Kalman filter (KF), characterized by an effective fusion of the results obtained by multiple-methods. Within the DL-based method, a spatial-temporal awareness model and an improved dung beetle optimizer are proposed to enhance the capture of spatial-temporal features and optimize hyperparameters. Additionally, three adaptive nonlinear KFs are employed for SOC estimation based on a simplified electrochemical model, with the adaptive cubature KF emerging as the most suitable following comparative analysis. Subsequently, the results from both approaches are integrated to derive the final result using various fusion methodologies. The effectiveness of the fusion framework is validated using experimental data, with kernel ridge regression emerging as the optimal fusion method. The best fusion results yield a mean absolute error of 0.2341% and a root mean square error of 0.3072%, demonstrating that proposed framework can effectively process and utilize multiple-source information, thereby enhancing accuracy and robustness compared to single methods and exhibiting adaptability to complex situations.

Advanced CFD simulation of two-phase anion exchange membrane water electrolysis

Anion exchange membrane (AEM) water electrolysis is a promising and inexpensive solution for hydrogen production and environmental problems. Owing to its low technical proficiency, AEM electrolysis necessitates high fidelity to improve its durability, reliability, and efficiency. Therefore, a CFD-based model for AEM electrolysis was developed to analyze the three-dimensional and two-phase phenomena inside the cell. The temperature, pressure, and gas generation profiles inside the cell were studied by combining electrochemical models with mass, momentum, and heat transfer models. Parameters were estimated using the experimental data from a high-accuracy model. The results showed that the applied voltage, which determined the exothermic/endothermic mode, significantly influenced the water electrolysis performance. Specifically, in the exothermic mode, with voltages exceeding the thermo-neutral voltage (1.48 V), the amount of hydrogen generated (15.53, 25.05, and 28.82 mol/m3 at 1.6, 1.8, and 2.0 V, respectively) was higher than that in the endothermic mode (4.78 mol/m3 at 1.45 V). However, the increased gas generation caused a rapid increase in the temperature and pressure drop inside the cell, which adversely affected durability. The model developed in this study can be used in experiments of various scales to optimize serpentine designs and commercial AEM electrolysis stack developments.