Detailed Electrochemical Model Research Articles

Thermal runaway evaluation and thermal performance enhancement of a lithium-ion battery coupling cooling system and battery sub-models

• Coupled thermal and electrochemical models to evaluate lithium-ion batteries. • Effect of C rate and colling flow on temperature distribution was assessed. • Onset and evolution of thermal runaway dependent on the reaction mechanism. • Early reactions dictates the occurrence of thermal runaway. This paper presents a novel simulation approach consisting of coupling fundamental and applicate aspects of Lithium-Ion battery simulations. A battery module representative of a complete battery pack is built using GT-AutoLion, consisting of a detailed electrochemical model and detailed cooling system modelled using the finite elements approach. The results show fresh and aged cylindrical cells submitted to different battery cooling flows. The cells are charged and discharged in high C rates to observe the performance of the proposed system in critical conditions. In addition, a battery thermal runaway code in Python is coupled to simulate the decomposition of the main components of the battery cell and their associated heat release during the battery operation. The concentration of the main species is tracked as well as the battery cells temperature distribution. The aged cells shown more probabilities of thermal runaway due to the increase of the internal resistance. However, it is possible to reduce the difference by increasing the cooling flow from 3 g/s to 50 g/s. When analysing the thermal runaway induced by a failure of a cell, the comparison shows that the mechanisms found in the bibliography shows a difference of 40 s in predicting the peak of heat release rate. Overall, the proposed framework confirms its capability of addressing the relevant phenomena during the battery operation, providing a way of improving the design phase from the battery cell to the battery pack.

A biogas-solar based hybrid off-grid power plant with multiple storages for United States commercial buildings

In this paper, a hybrid renewable power plant with a storage system is designed. The benefits of sizing and energy management are assessed for a commercial building under eight different climatic conditions in the United States. In the considered system, photovoltaic panels are coupled to a unitized regenerative solid oxide fuel cell. The use of biogas to feed unitized regenerative solid oxide fuel cell is investigated, employing a detailed electrochemical model of electrolyzer and fuel cell modes. A battery pack is included in the plant as a secondary storage system, together with a diesel engine operating in backup mode.Four scenarios where biogas amount is varied together with the initial state of charge of the battery were evaluated. Results demonstrate that the power plant can operate with 100 % renewable procurement if the digester produces from 6000 to 9500 stdm3/y and the battery is completely charged at the beginning of the year. By reducing the biogas availability or starting with a low state of charge, the use of the diesel generator is inevitable.The study confirms that the proposed hybrid renewable power plant is technically feasible and can be considered a reliable and clean energy source in other areas and buildings.

Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

Lithium-ion (Li-I) batteries have recently become pervasive and are used in many physical assets. For the effective management of the batteries, reliable predictions of the end-of-discharge (EOD) and end-of-life (EOL) are essential. Many detailed electrochemical models have been developed for the batteries. Their parameters are calibrated before they are taken into operation and are typically not re-calibrated during operation. However, the degradation of batteries increases the reality gap between the computational models and the physical systems and leads to inaccurate predictions of EOD/EOL. The current calibration approaches are either computationally expensive (model-based calibration) or require large amounts of ground truth data for degradation parameters (supervised data-driven calibration). This is often infeasible for many practical applications. In this paper, we introduce a reinforcement learning-based framework for reliably inferring calibration parameters of battery models in real time. Most importantly, the proposed methodology does not need any labeled data samples of observations and the ground truth parameters. The experimental results demonstrate that our framework is capable of inferring the model parameters in real time with better accuracy compared to approaches based on unscented Kalman filters. Furthermore, our results show better generalizability than supervised learning approaches even though our methodology does not rely on ground truth information during training.

Detailed Electrochemical Model of Microphotosynthetic Power Cells

A detailed and interdisciplinary explanation of the operation of microphotosynthetic cells ( μ -PSCs) from chemical reactions modeling to energy harvesting is provided in this article. The model includes the effect of photosynthesis and respiration of algae cells as well as the effect of irradiance, day and night cycle, temperature, and pH of the solution. It has an interdisciplinary explanation built on knowledge accumulated from different fields, such as chemical, biological, electrical sciences, and practical results. It covers the theory of the operation of μ -PSCs, the electrochemical model, the equivalent circuit representation of μ -PSCs, and energy harvesting from μ -PSCs. This article provides a simple but thorough insight into a better understanding and improvement of the μ -PSCs technology.

An Efficient Electrochemical Tanks-in-Series Model for Lithium Sulfur Batteries

This article applies and efficiently implements the Tanks-in-Series methodology (J. Electrochem. Soc., 167, 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium-Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, an exponential scaling method is introduced that enables efficient simulation of the model equations to address the wide range of time constants present for different reactions in the Lithium-Sulfur system. The Tank Model achieves acceptable voltage error even for transport-limited discharged conditions. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing, and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

Optimal Design of Experiments for a Lithium-Ion Cell: Parameters Identification of an Isothermal Single Particle Model with Electrolyte Dynamics

Advanced battery management systems rely on mathematical models to guarantee optimal functioning of Lithium-ion batteries. The Pseudo-Two Dimensional (P2D) model is a very detailed electrochemical model suitable for simulations. On the other side, its complexity prevents its usage in control and state estimation. Therefore, it is more appropriate the use of simplified electrochemical models such as the Single Particle Model with electrolyte dynamics (SPMe), which exhibits good adherence to real data when suitably calibrated. This work focuses on a Fisher-based optimal experimental design for identifying the SPMe parameters. The proposed approach relies on a nonlinear optimization to minimize the covariance parameters matrix. At first, the parameters are estimated by considering the SPMe as the real plant. Subsequently, a more realistic scenario is considered where the P2D model is used to reproduce a real battery behavior. Results show the effectiveness of the optimal experimental design when compared to standard strategies.

Model development and analysis of a novel high-temperature electrolyser for gas phase electrolysis of hydrogen chloride for hydrogen production

In this study, a high temperature electrolyser for the gas phase electrolysis of hydrogen chloride for hydrogen production is proposed and assessed. A detailed electrochemical model is developed to study the J-E characteristics for the proposed electrolyser (a solid oxide electrolyser based on a proton conducting electrolyte). The developed model accounts for all major losses, namely activation, concentration and ohmic. Energy and exergy analyses are carried out, and the energy and exergy efficiencies of the proposed electrolyser are determined to be 41.1% and 39.0%, respectively. The simulation results show that at T = 1073 K, P = 100.325 kPa and J = 1000 A/m2, 1.6 V is required to produce 1 mol of hydrogen. This is approximately 0.3 V less than the voltage required by a high temperature steam electrolyser (based on a proton conducting electrolyte) operating at same condition (T = 1073 K, P = 101.325 kPa and J = 1000 A/m2), suggesting that the proposed electrolyser offers a new option for high temperature electrolysis for hydrogen production, potentially with a low electrical energy requirement. The proposed electrolyser may be incorporated into thermochemical cycles for hydrogen production, like CuCl or chlorine cycles.

Cellular Obstruction Clearance in Proximal Ventricular Catheters Using Low-Voltage Joule Heating.

Proximal obstruction due to cellular material is a major cause of shunt failure in hydrocephalus management. The standard approach to treat such cases involves surgical intervention which unfortunately is accompanied by inherent surgical risks and a likelihood of future malfunction. We report a prototype design of a proximal ventricular catheter capable of noninvasively clearing cellular obstruction.Methods: In-vitro cell-culture methods show that low-intensity ac signals successfully destroy a cellular layer in a localized manner by means of Joule heating induced hyperthermia. A detailed electrochemical model for determining the temperature distribution and ionic current density for an implanted ventricular catheter supports our experimental observations. In-vitro experiments with cells cultured in a plate as well as cells seeded in mock ventricular catheters demonstrated that localized heating between 43°C and 48°C caused cell death. This temperature range is consistent with hyperthermia. The electrochemical model verified that Joule heating due to ionic motion is the primary contributor to heat generation. Hyperthermia induced by Joule heating can clear cellular material in a localized manner. This approach is feasible to design a noninvasive self-clearing ventricular catheter system. A shunt system capable of clearing cellular obstruction could significantly reduce the need for future surgical interventions, lower the cost of disease management, and improve the quality of life for patients suffering from hydrocephalus.

Analysis and performance assessment of NH3 and H2 fed SOFC with proton-conducting electrolyte

The present paper investigates the performance of a solid oxide fuel cell based on proton-conducting electrolyte (SOFC-H+) using one-dimensional steady-state model. The analysis covers a detailed electro-chemical model for H2 and NH3 fuels. The direct internal reforming of NH3 is examined, and the effects of some operating parameters (e.g. temperature, pressure, fuel utilization and oxidant utilization) on the reversible cell potential are investigated. In addition, the overpotentials (including activation, ohmic and concentration) are calculated to study the irreversible behavior of the SOFC-H+ with some actual data operating conditions and material properties taken from the literature. In addition, effects of some operation and structural parameters on cell performance were examined. The present results indicate that the activation and the ohmic losses are considerable. The concentration overpotential at the anode side is negligible due to the fact that H2O is produced at the cathode side. The maximum power density is calculated as 3212 and 3113 W/m2 at 1073 K and 1 atm for the fuels of H2 and NH3. The results further show that H2 provides better performance than NH3 at the same partial pressure. Moreover, NH3 is an excellent hydrogen carrier which is a potential candidate for SOFC-H+ due to its high hydrogen content and considerable cell performance.

Optimal design of hybrid wind/photovoltaic electrolyzer for maximum hydrogen production using imperialist competitive algorithm

The rising demand for high-density power storage systems such as hydrogen, combined with renewable power production systems, has led to the design of optimal power production and storage systems. In this study, a wind and photovoltaic (PV) hybrid electrolyzer system, which maximizes the hydrogen production for a diurnal operation of the system, is designed and simulated. The operation of the system is optimized using imperialist competitive algorithm (ICA). The objective of this optimization is to combine the PV array and wind turbine (WT) in a way that, for minimized average excess power generation, maximum hydrogen would be produced. Actual meteorological data of Miami is used for simulations. A framework of the advanced alkaline electrolyzer with the detailed electrochemical model is used. This optimal system comprises a PV module with a power of 7.9 kW and a WT module with a power of 11 kW. The rate of hydrogen production is 0.0192 mol/s; an average Faraday efficiency of 86.9 percent. The electrolyzer works with 53.7 percent of its nominal power. The availability of the wind for longer periods of time reflects the greater contribution of WT in comparison with PV towards the overall throughput of the system.

Modeling and optimization of proton-conducting solid oxide electrolysis cell: Conversion of CO2 into value-added products

Abstract Proton-conducting solid oxide electrolysis cells (SOEC-H + ) are a promising technology that can utilize carbon dioxide to produce syngas. In this work, a detailed electrochemical model was developed to predict the behavior of SOEC-H + and to prove the assumption that the syngas is produced through a reversible water gas-shift (RWGS) reaction. The simulation results obtained from the model, which took into account all of the cell voltage losses (i.e., ohmic, activation, and concentration losses), were validated using experimental data to evaluate the unknown parameters. The developed model was employed to examine the structural and operational parameters. It is found that the cathode-supported SOEC-H + is the best configuration because it requires the lowest cell potential. SOEC-H + operated favorably at high temperatures and low pressures. Furthermore, the simulation results revealed that the optimal S/C molar ratio for syngas production, which can be used for methanol synthesis, is approximately 3.9 (at a constant temperature and pressure). The SOEC-H + was optimized using a response surface methodology, which was used to determine the optimal operating conditions to minimize the cell potential and maximize the carbon dioxide flow rate.

An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

Modeling of batteries in photovoltaic systems has been a major issue related to the random dynamic regime imposed by the changes of solar irradiation and ambient temperature added to the complexity of battery electrochemical and electrical behaviors. However, various approaches have been proposed to model the battery behavior by predicting from detailed electrochemical, electrical or analytical models to high-level stochastic models. In this paper, an improvement of dynamic electrical battery model is proposed by automatic parameter extraction using genetic algorithm in order to give usefulness and future implementation for practical application. It is highlighted that the enhancement of 21 values of the parameters of CEIMAT model presents a good agreement with real measurements for different modes like charge or discharge and various conditions.

A Semi-Empirical Capacity Fade Model for Lithium Ion Cells with Nickel Based Composite Cathode

High energy density and low cost of nickel based composite cathode has drawn significant attention for vehicular application of Li-ion battery. Experimental observations confirm that the capacity fade of such cells are governed by the surface degradation of the cathode particles. Although, there are many experimental studies on this phenomenon, no numerical model exists. In the present investigation a semi-empirical and semi-electrochemical model is proposed based on the experimental results. The cathode degradation based hypothesis is found to be valid on experimental data obtained at varied operating temperature and discharge rates. A physics based model has been derived from the hypothesis. As the cathode degradation reaction is kinetically driven, the capacity-fade model has been decoupled from the standard electrochemistry based battery model, making it computationally economical. Due to this ease of computation, the present approach can be used to estimate the capacity loss due to cathode degradation without detailed electrochemical models and cyclic simulations. The model has shown excellent predictive capability when compared with experimental data for a wide range of operating conditions. Therefore, the model is ideal for an onboard battery management system as well as for a prognostic tool for battery life prediction along with other algorithms.

Thermodynamic analysis of solid oxide fuel cell system using different ethanol reforming processes

In this study, a performance of a solid oxide fuel cell (SOFC) system integrated with different ethanol reforming processes (i.e., steam reforming (SR), partial oxidation (POX) and autothermal reforming (ATR)) is investigated with the aim to determine a suitable ethanol reforming process for the SOFC system. The thermodynamic analysis of the SOFC system operated under steady state conditions was performed using flowsheet simulator. A detailed electrochemical model incorporating all voltage losses (i.e., activation, ohmic and concentration losses) was considered. The simulation results showed that increases in reformer and SOFC temperatures can improve the electrical performance of the SOFC system. The electrical performance of the SOFC-SR is maximized because this reforming process provides the highest hydrogen yield. However, because the SOFC included an internal methane reformation, electrical performances of SOFC systems with different reforming systems are slightly different. When the thermal efficiency was determined, it was revealed that the SOFC-POX system had a higher thermal efficiency with an increasing O/E and decreasing reformer and SOFC temperatures.

Efficient Computational Strategy for Coupled Multi-Physics Battery Pack Simulations

Today, most of the battery pack simulations are conducted for design and analysis of the cooling channels to address non-uniform temperature distribution that lead to loss of performance and life. This non-uniformity could lead to different ageing of the cells depending on their location within the pack. This could subsequently trigger undesirable behavior like undercharge or overcharge conditions. In all these simulations, an equivalent circuit model is used to represent electro-chemistry as the detailed model would become computationally expensive. Incorporating degradation and aging mechanisms into these equivalent circuit models are effective for on-board diagnostic devices where time is critical, but a detailed electro-chemical model is necessary to understand balancing of cells in a Li-Ion battery pack. In this paper, we will present an efficient way to simulate battery packs that utilizes high performance computing to demonstrate the importance of detailed electro-chemical model on temperature and potential gradients in a battery pack that lead to cell imbalance. The 3D multi-physics model for the individual Li-Ion pouch cell used to construct the battery pack has been validated earlier with the experimental data at various discharge rates [1]. The battery pack under consideration has modules connected in series. Each module has four cells connected in series and parallel. The Voltage and Temperature profiles of the battery pack with two modules at the end of discharge are shown in Figure 1. Convective cooling boundary condition is imposed on the exterior of the battery pack.

Temperature Effect due to Internal Reforming in Pressurized SOFC

For the integration in a hybrid power plant the behavior of pressurized SOFC stacks was examined. A detailed electrochemical model was validated allowing for a thorough interpretation of experimental data. This paper focuses on the use of reformate gases and the influence of internal reforming. Experimental and simulated current-voltage characteristics and electrochemical impedance spectra were used to examine the temperature differences due to internal reforming within the stack which were found to have a major impact on performance. It is shown that similar performance can be obtained for different fuel gas compositions if temperature within the stack is controlled.

Temperature Effect Due to Internal Reforming in Pressurized SOFC

For the integration in a hybrid power plant the behavior of pressurized SOFC stacks was examined. A detailed electrochemical model was validated allowing for a thorough interpretation of experimental data. This contribution focuses on the use of reformate gases and the influence of internal reforming. Experimental and simulated current-voltage characteristics and electrochemical impedance spectra were used to examine the temperature differences due to internal reforming which were found to have a major impact on performance. It is shown that similar performance can be obtained for different fuel gas compositions if temperature within the stack is controlled.

Investigation of a proton-conducting SOFC with internal autothermal reforming of methane

This study presents a performance analysis of a proton-conducting SOFC (SOFC-H+) with internal reforming of methane. The autothermal reforming within the SOFC-H+ stack is considered to be a potential solution of the carbon formation problem facing in operation of internal steam reforming SOFC-H+. A one-dimensional, steady-state model of the SOFC-H+ coupled with a detailed electrochemical model is employed to investigate its performance in terms of power density and fuel cell efficiency. The simulation results show that when SOFC-H+ is operated under an autothermal reforming environment, the presence of carbon monoxide, which is a major cause of carbon formation, in the fuel cell stack decreases. Effect of key operating parameters, such as temperature, steam-to-carbon and oxygen-to-carbon feed ratios, current density and fuel utilization, on the SOFC-H+ performance in terms of electrical efficiencies and energy demand is also investigated. The results indicate that operating temperatures have strong influence on SOFC-H+ performance, carbon monoxide production and heat generation.

Electrochemical Model Based Observer Design for a Lithium-Ion Battery

Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the internal temperature, the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. Based on physical insight, we propose an output error injection observer based on a reduced set of partial differential-algebraic equations. This reduced model has a less complex structure, while it still captures the main dynamics. The observer is extensively studied in simulations and validated in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.