On Open Circuit Voltage Research Articles

Numerical study of gas crossover effect on hydrogen-oxygen proton exchange membrane fuel cell

Hydrogen-oxygen proton exchange membrane fuel cell (H2/O2 PEMFC) is a typical fuel cell for the applications of air-free environments, such as aerospace and underwater. Gas crossover has an adverse effect on the cell performance especially on open-circuit voltage (OCV), and this phenomenon would be severe for H2/O2 PEMFC. In this study, we develop a H2/O2 PEMFC model considering hydrogen and oxygen crossover, and investigate the gas crossover effect on H2/O2 PEMFC, and effects of temperature, relative humidity, inlet pressure and membrane thickness. The numerical results show that the gas crossover effect on the cell performance is obvious in the low current density region, and this effect becomes weak with current density. Although the crossover flux rate of hydrogen is generally greater than that of oxygen, the crossover current density of oxygen could be greater than that of hydrogen. Increasing temperature and relative humidity will decrease the OCV, and increase hydrogen and oxygen crossover current density. The temperature effect on OCV is more obvious at high relative humidity conditions. Increasing inlet pressure only slightly increases the OCV with considering the gas crossover effect. The OCV increases with increasing membrane thickness, and this positive effect becomes weak when the membrane thickness is greater than 75 μm in the investigated range. The work illustrates the basic influence rules and some quantified results of the critical parameters on the gas crossover effect and OCV of H2/O2 PEMFC.

Research on SOP Estimation Based on SOC Constraints under Static Operating Conditions

In a battery management system (BMS), accurate estimation of system operating power (SOP) is essential to ensure battery performance and extend service life. In this paper, an SOP estimation method based on open circuit voltage (OCV), state of charge (SOC) and battery current is studied. By analyzing the battery data under dynamic stress test (DST) conditions, the estimated value of continuous peak charge and discharge current is calculated, and the corresponding continuous peak charge and discharge power is estimated accordingly. Comparing the estimated charging and discharging power with the experimental value, the results show that the new algorithm can significantly improve the estimation accuracy of SOP. It not only improves the accuracy of SOP estimates, but also helps to optimize BMS, thereby improving the overall performance and service life of the power battery.

On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focuses on establishing a correlation between the OCV values, the energy stored in the battery, and the battery SOH. The experimental campaign was conducted at the Hyundai Motor Europe Technical Center GmbH (Germany), and the data collection process took advantage of the ETAS Integrated Calibration and Application Tool (INCA) and the ETAS Measure Data Analyzer (MDA) software. Six machine learning algorithms are evaluated and compared, namely linear regression, k-nearest neighbors, support vector machine, random forest, classification and regression tree, and neural network. Among the evaluated algorithms, random forest (RF) exhibits the best performance in predicting the state of health of high-voltage batteries, both for the OCV and the capacity (C) estimation. Specifically, if compared to the worst algorithm (i.e., linear regression), RF achieves a remarkable improvement with a reduction of 96% and 97% in the mean absolute error for the OCV and the C estimation, respectively. Furthermore, the comparison highlighted the main differences in the performance, complexity, interpretability, and specific features of the six algorithms. The findings of the present study will contribute to the development of efficient maintenance strategies, thus reducing the risk of unexpected battery failures.

Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation

Battery management systems depend on open circuit voltage (OCV) characterization for state of charge (SOC) estimation in real time. The traditional approach to OCV-SOC characterization involves collecting OCV-SOC data from sample battery cells and then fitting a polynomial model to this data. The parameters of these polynomial models are known as the OCV-parameters, or OCV-SOC parameters, in battery management systems and are used for real-time SOC estimation. Even though traditional OCV-SOC characterization approaches are able to abstract the OCV-SOC behavior of a battery in a few parameters, these parameters are only applicable in high precision computing systems. However, many practical battery applications do not have access to high-precision computing resources. The typical approach in a low-precision system is to round the OCV-parameters. This paper highlights the perils of rounding the OCV parameters and proposes an alternative OCV-SOC table. First, several existing OCV-SOC parameters are compared in terms of their expected system requirements and accuracy losses due to rounding. Then, a systematic optimization-based approach is introduced to create an OCV-SOC table that is robust to rounding. A formal performance evaluation metric is introduced to measure the robustness of the resulting OCV-SOC table. It is shown that the OCV-SOC table obtained through the proposed optimization approach outperforms the traditional parametric OCV-SOC models with respect to rounding.

Visualization, Understanding, and Mitigation of Membrane Irregularities in Gas Diffusion Electrode-Based Polymer Electrolyte Membrane Fuel Cells

Polymer electrolyte membrane fuel cells (PEMFCs) show great promise for their applications in electric vehicles. Most studies on PEMFCs typically use catalyst-coated membranes (CCMs); however, gas-diffusion-electrodes (GDEs) offer advantages for large-scale manufacturing with roll-to-roll coating systems. Additional procedures including hot-pressing and coating a thin ionomer overlayer are necessary in the fabrication process of GDE-based membrane electrode assemblies (MEAs) to improve the otherwise poor catalyst layer/membrane interface.1,2 However, these procedures may also introduce potential irregularities and/or defects, especially when thin membranes are used. Limited understanding exists regarding if and to what extent such irregularities impact PEMFC performance and lifetime.In this study, NREL’s custom fuel cell hardware that enables quasi in-situ infrared (IR) thermography studies3 was utilized to visualize spatial hydrogen crossover in GDE-based MEAs to identify and locate Nafion membrane irregularities. The MEA cross-sectional structure of the membrane irregularity was investigated by scanning electron microscopy (SEM) imaging. The impact of membrane irregularities on open-circuit voltage (OCV), hydrogen crossover and PEMFC initial H2/air performance was systematically studied. The effect of micro-porous layer (MPL) surface roughness of gas diffusion media (GDM) on the membrane irregularity formation mechanism was investigated. By employing GDM with a smoother MPL surface and fine tuning the MEA fabrication process, including compression force and hot-pressing temperature, membrane irregularities were successfully mitigated. Furthermore, IR assisted accelerated stress testing (IR-AST) of MEAs containing different amounts of beginning-of-test membrane irregularities was investigated. These membrane irregularities were demonstrated to be seeds of failure points and dramatically shorten the MEA lifetime. Reference: (1) Wang, M.; Medina, S.; Pfeilsticker, J. R.; Pylypenko, S.; Ulsh, M.; Mauger, S. A. Impact of Microporous Layer Roughness on Gas-Diffusion-Electrode-Based Polymer Electrolyte Membrane Fuel Cell Performance. ACS Appl. Energy Mater. 2019, 2 (11), 7757–7761. https://doi.org/10.1021/acsaem.9b01871.(2) Mauger, S. A.; Pfeilsticker, J. R.; Wang, M.; Medina, S.; Yang-Neyerlin, A. C.; Neyerlin, K. C.; Stetson, C.; Pylypenko, S.; Ulsh, M. Fabrication of High-Performance Gas-Diffusion-Electrode Based Membrane-Electrode Assemblies. J. Power Sources 2020, 450, 227581. https://doi.org/https://doi.org/10.1016/j.jpowsour.2019.227581.(3) Phillips, A.; Ulsh, M.; Neyerlin, K. C.; Porter, J.; Bender, G. Impacts of Electrode Coating Irregularities on Polymer Electrolyte Membrane Fuel Cell Lifetime Using Quasi In-Situ Infrared Thermography and Accelerated Stress Testing. Int. J. Hydrogen Energy 2018, 43 (12), 6390–6399. https://doi.org/https://doi.org/10.1016/j.ijhydene.2018.02.050. Figure 1

Aging mechanism diagnosis of lithium ion battery by open circuit voltage analysis

The whole life aging behavior and degradation mechanism of lithium ion battery (LIB) are critical to ensure the stability and reliability during practical operation. In this work, a new LIB aging modelling and diagnosing method is proposed based on open circuit voltage (OCV) analysis, through a two-stage segmented nonlinear regression algorithm to smooth incremental capacity (IC) curves for the reconstruction of universal OCV curves. Such algorithm can well reserve the chemical features of the IC curves, which enables the quantification of the loss of active materials and lithium inventory in a nondestructive manner with this OCV model-based diagnostic method. A case study of a commercial LiFePO4 (LFP) battery shows a satisfied accuracy of the model, realizing a quick identification of aging behavior and capacity degradation modes, as well as the parameter recognition of the battery internal resistance components.

Development of status detection method of lithium-ion rechargeable battery for hybrid electric vehicles

In order to maximize the performance of lithium ion batteries, we have developed a new method for detecting the status of hybrid electric vehicle (HEV) batteries. The method obtains an accurate state of charge (SOC) by combining the SOC based on open circuit voltage (OCV) and the SOC based on current integration. In addition to that, our method has an auto-tuning function, which is realized by relatively simple processing, for the characteristic parameter of batteries. Our method can also obtain the state of health (SOH) by watching the tuned characteristic parameter. We perform simulation evaluations of our method with a battery model, and we confirm that the SOC error is less than 5% even if SOH set in the battery model and in our method are under mismatched condition. We also confirm that the SOH gradually converges to the true value and SOC error is improved by performing simulation evaluation repeatedly.

A fast capacity estimation method based on open circuit voltage estimation for LiNixCoyMn1-x-y battery assessing in electric vehicles

Abstract With the widespread popularity of electric vehicles (EV), effective assessment for retired EVs has become increasingly critical. Unlike traditional internal combustion vehicles, for EV, batteries account for a large proportion of the entire vehicle cost. Therefore, a fast battery capacity estimation method based on open-circuit voltage (OCV) estimation is forthwith proposed. The method calculates capacity using the ratio of the change in electric quantity to the corresponding change in state-of-charge (SOC), and the SOC is estimated via a fast OCV estimation method proposed in this paper. The fast test procedure includes a charging/discharging test and a short rest, which take less than 30 minutes in total and provide the data for the battery capacity estimation. For estimation, a weighted voltage relaxation model, containing two parallel resistor–capacitor (RC) components, is established. Its parameters are then optimized using the beetle antenna search algorithm with the approximate OCV range obtained in the test and an early voltage relaxation curve. The results of the experiments show that the proposed model and algorithm can accurately estimate the OCV, and the capacity estimation can be quickly realized in half an hour while limiting inaccuracy to less than 3%.

A Study on the Influence of Lithium Plating on Battery Degradation

Within Li-ion batteries, lithium plating is considered as one of the main reasons behind the capacity fade that occurs during low temperature and fast charging conditions. Previous studies indicate that plating is influenced by the levels of loss of lithium inventory (LLI) and the loss of active material (LAM) present in a battery. However, it is not clear from the literature on how lithium plating influences battery degradation in terms of LAM and LLI. Quantifying the undesirable impacts of lithium plating can help in understanding its impact on battery degradation and feedback effects of previous lithium plating on the formation of present plating. This study aims to quantify the degradation modes of lithium plating: LLI, LAM at the electrode level. A commercial Li-ion cell was first, aged using two different cases: with and without lithium plating. Second, a degradation diagnostic method is developed to quantify the degradation modes based on their measurable effects on open-circuit voltage (OCV) and cell capacity. The results highlight that LAMNE and LLI levels under the fast charge profile are increased by 10% and 12%, respectively, compared to those under the less aggressive charge profile. Further, limitations of the degradation analysis methods are discussed.

Experimental research on temperature rise and electric characteristics of aluminum air battery under open-circuit condition for new energy vehicle

Due to lack of systematic research on open-circuit voltage (OCV) and electrolyte temperature rise characteristics of aluminum air battery, in order to explore the influential factors on the OCV and electrolyte temperature rise of aluminum air battery, in this paper, for the first time, we studied the effects of different ambient temperature conditions, different concentrations of NaOH and KOH electrolyte, and pure aluminum and aluminum alloy on the OCV and electrolyte temperature rise of aluminum air battery. Results show that the OCV of aluminum air battery is obviously affected by ambient temperature conditions, electrolyte concentration, and different anode materials. The OCV range is 1.5 to 1.8 V at 0°C under different KOH-electrolyte concentrations when aluminum alloy is used as anode material; with the increase of ambient temperature, the OCV will rise, and the range is 1.8 to 1.95 V. The working process of aluminum air battery is accompanied by the phenomenon of heat release, and the temperature rise range of electrolyte will not exceed 7°C when aluminum alloy is used as the anode material; however, the highest temperature of the electrolyte can reach 100°C when pure aluminum is used as the negative electrode material. The results of this study will provide theoretical guidance for designing aluminum air batteries and identifying their optimal operating conditions.

Practical Guidelines for Reliable Electrochemical Characterization of Solid Oxide Fuel Cells

Electrochemical measurements on solid oxide fuel cells (SOFC) often uncover unusual readings. These are then ascribed to unknown material properties or newly discovered physical effects. However, a closer look shows that these are simply errors − mostly test artefacts. Our group has isolated many causes of error and developed reliable techniques to avoid them, focusing on open circuit voltage (OCV) measurement, current/voltage (i/v) curves and electrochemical impedance spectroscopy (EIS). This paper aims to help researchers avoid errors through balanced choice of apparatus and test procedure. Simple to implement test-sequences are set out to avoid errors. First, test-bench wiring, hardware limitations and apparatus selection are detailed to avoid error sources. Secondly, measurement settings and how they impact measurement results are discussed. Our developed procedures avoid the majority of errors; this is exhaustively tested at operationally relevant conditions.

Investigation of Impedance-Based Parameters in Metal-O2 Batteries for Next Generation of Battery Management Systems

The need for increasing driving range in electric vehicles (EV) has led to research in high capacity batteries beyond Li-ion. Metal-O2 batteries, like Li-O2 and Na-O2, have high theoretical energy densities and thus a possibility to increase the capacity of automotive batteries significantly compared to current Li-ion batteries1,2. A battery management system (BMS) typically uses a combination of coulomb counting and calibration based on open circuit voltage (OCV) measurements that depend on the state of charge (SOC). Calibration is needed due to the accumulation of errors in the coulomb counting3. Common for most metal-O2 batteries is that the chemistry is unchanged during discharge and charge (assuming no degradation). This means that the OCV does not change as function of SOC. Furthermore, constant current measurements show a flat discharge plateau in large parts of the discharge period. This has been shown for Li-O2 and Na-O2 batteries2,4 and in some cases also for Mg-O2 and Al-O2 5,6. Taking the well studied Li-O2 battery as an example, the dominating process during discharge is reduction of oxygen to produce Li2O2 on top of an existing Li2O2 layer. As this process continues during the entire discharge, both OCV and discharge potential is constant until the end of discharge, where other processes become limiting, as shown in figure 1. New methods have to be developed to overcome the constant OCV and flat discharge plateau that otherwise would complicate both battery management and accurate online prediction of available capacity in metal-O2 batteries. Furthermore, the increased capacity predicted for the metal-O2 batteries will result in longer discharge periods without charging, thus increasing the need for accurate calibration of the capacity in the range between ~10% and ~90% SOC. In the following we propose a method to accurately predict the capacity of metal-O2 batteries using impedance spectroscopy for calibration of the SOC tracking algorithm as well as gauging the degradation of the battery materials. This method can easily be implemented in an automotive BMS with only a few extra components and preliminary experiments have shown that the impedance measurements can be performed both during rest periods and under load conditions. This makes the method applicable not only for EVs but for batteries in a large range of electrical devices as the measurements can be performed when needed, thus maintaining a high level of accuracy for the SOC estimation and state of degradation.[1] Christensen, J., Albertus, P., Sánchez-Carrera, R. S., Lohmann, T., Kozinsky, B., Liedtke, R., et al. (2012). A Critical Review of Li∕Air Batteries. Journal of the Electrochemical Society, 159(2), R1. doi:10.1149/2.086202jes [2] Hartmann, P. (2012). A rechargeable room-temperature sodium superoxide (NaO2) battery. Nature Materials, 12(3), 228–232. doi:10.1038/nmat3486 [3] Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86(9), 1506–1511. doi:10.1016/j.apenergy.2008.11.021 [4] McCloskey, B. D., Garcia, J. M., & Luntz, A. C. (2014). Chemical and Electrochemical Differences in Nonaqueous Li–O 2and Na–O2 Batteries. The Journal of Physical Chemistry Letters, 5(7), 1230–1235. doi:10.1021/jz500494s [5] Shiga, T., Hase, Y., Kato, Y., Inoue, M., & Takechi, K. (2013). A rechargeable non-aqueous Mg–O2 battery. Chemical Communications, 49(80), 9152–9154. doi:10.1039/c3cc43477j [6] Revel, R., Audichon, T., & Gonzalez, S. (2014). Non-aqueous aluminium-air battery based on ionic liquid electrolyte. Journal of Power Sources, 272(c), 415–421. doi:10.1016/j.jpowsour.2014.08.056 Figure 1

A comprehensive yet comprehensible analytical model for the direct methanol fuel cell

The direct methanol fuel cell (DMFC) has a significant potential in consumer electronics and in backup and portable power. Its progress is, however, hindered, in part, by a lack of an adequate fundamental understanding of the effect of various operating and design variables on its performance. While detailed computational models are available, an analytical model is attractive for ease of comprehension and ready utility. Therefore, we have developed a comprehensive yet tractable one-dimensional, isothermal, explicit analytical model based largely on a priori parameters, and in terms of quantities with tangible meaning.The model correctly predicts the extent of methanol crossover and its effect on open-circuit voltage (OCV) as well as on polarization of the anode, cathode, and the fuel cell. It also accurately describes the effects of methanol feed concentration and temperature on DMFC performance. It aptly predicts the significant power losses from the large anode and cathode overpotentials as well as from methanol crossover, and the resulting low DMFC efficiency, except over a narrow range of operating conditions. The insightful model can be used, e.g., in real-time control of DMFC to operate in the narrow region of high efficiency and power density.

On Chip Kalman Filtering Strategy-Based for VRLA Battery Control Unit

Based on existing understanding of residual capacity, the paper adopts Kalman filtering method on SOC estimation. The battery control unit(BCU) includes voltage, current and temperature sampling module. Due to Lead-Acid battery charging and discharging are a complex electrochemical process, we suggest a non-linear least squares regression method (NLLSRM) on open circuit voltage (OCV) method is proposed to aim at Lead-Acid battery. At the same time, a detailed hardware circuit schemes transfer module and on chip embedded Kalman filter method are employed. Moreover, A Local Interconnect Network (LIN) Bus Communication Technology is applied to communicate to Electronic Control Unit (ECU). Validation experimental results show that the proposed Lead-Acid BCU is high credible and the state of charge (SOC) estimation average relative error is about 3%.

Electrochemical testing of suspension plasma sprayed solid oxide fuel cell electrolytes

Electrochemical performance of metal-supported plasma sprayed (PS) solid oxide fuel cells (SOFCs) was tested for three nominal electrolyte thicknesses and three electrolyte fabrication conditions to determine the effects of electrolyte thickness and microstructure on open circuit voltage (OCV) and series resistance ( R s). The measured OCV values were approximately 90% of the Nernst voltages, and electrolyte area specific resistances below 0.1 Ω cm 2 were obtained at 750 °C for electrolyte thicknesses below 20 μm. Least-squares fitting was used to estimate the contributions to R s of the YSZ bulk material, its microstructure, and the contact resistance between the current collectors and the cells. It was found that the 96% dense electrolyte layers produced from high plasma gas flow rate conditions had the lowest permeation rates, the highest OCV values, and the smallest electrolyte-related voltage losses. Optimal electrolyte thicknesses were determined for each electrolyte microstructure that would result in the lowest combination of OCV loss and voltage loss due to series resistance for operating voltages of 0.8 V and 0.7 V.

Lithium insertion into M 2+-V 2O 5 bronzes ( M = Ni, Mn, Co, Cu)

Electrochemical lithium insertion into α-type vanadium oxide bronze M 2+-V 2O 5 (M = Ni, Mn, Co, Cu) of orthorhombic system was studied, by comparing with V 2O 5. The amount of lithium inserted into M 2+-V 2O 5 bronzes up to the second plateau was smaller by about 0.15 mol than the case of V 2O 5 on open circuit voltage (OCV) curves and discharge curves. These results prove that M 2+ cations in the bronze are placed in the same sites as lithium between the layer of V-O polyhedra and that the number of foreign ions which can be inserted is governed by the number of sites.

Lithium insertion into vanadium oxide bronze containing nickel

Electrochemical insertion of lithium ions into V 2O 5 bronze containing nickel (NiV 2O 5, Ni V = 0.075 ) was studied by comparing with V 2O 5. The difference in the amount of lithium inserted up to the second plateau was about 0.15 between NiV 2O 5 and V 2O 5 on open circuit voltage (OCV) curves. The broadened reduction and oxidation peaks in cyclic voltammogram, indistinct plateaux in discharge curve, and wide gap between OCV and discharge potential observed on NiV 2O 5 suggested that nickel ions in the bronze are placed in the same sites as lithium and disturb lithium diffusion along the channels of V 2O 5 structure. The present results implied that two factors, charge balance and lattice sites, governed the insertion of foreign ions, such as Li + and Ni 2+, into V 2O 5 lattice.