Open-circuit Voltage Hysteresis Research Articles

Explaining the Voltage Hysteresis and Slow Relaxation of Silicon Nanoparticles with a Chemo-Mechanical Particle-SEI Model

Silicon is widely considered to be a promising next-generation anode material, primarily due to its remarkably high theoretical capacity. Furthermore, silicon is an abundant, cheap, and widely spread material. However, a major challenge for the commercialization of silicon anodes is the significant voltage hysteresis reducing efficiency and leading to detrimental heat generation during fast-charging. Additionally, the hysteresis causes an unclear state-of-charge (SOC) to voltage relation impeding precise SOC estimation.The voltage hysteresis behavior of silicon anodes is addressed in literature with three different arguments: phase transformations, plastic flow of silicon, and slow diffusion. These approaches can interpret the voltage hysteresis of crystalline silicon, thin films, and large anode particles, respectively. Nevertheless, we show that they cannot explain the hysteresis observed for silicon anodes consisting of amorphous nanoparticles, the relevant material for next-generation lithium-ion batteries.Our investigation highlights the chemo-mechanical interplay between the silicon nanoparticle and the covering Solid-Electrolyte Interphase (SEI) as the underlying cause for the significant voltage hysteresis. The SEI, a thin passivating layer, forms on negative electrode particles due to electrolyte decomposition [1]. In the case of silicon particles, the volume changes during lithiation and delithiation result in massive strains and plastic deformation occurring within the SEI [2]. Our chemo-mechanical particle-SEI description successfully replicates the observed open-circuit voltage hysteresis in experiments and aligns with the Plett model [3]. Moreover, our visco-elastoplastic SEI model reproduces the voltage difference between slow cycling and the relaxed open-circuit voltage. In our recent work, we show that a sophisticated mechanical model explains the slow voltage relaxation observed for silicon nanoparticles as well as the observed C rate dependence [4].To conclude, we explain the voltage hysteresis and the slow voltage relaxation of silicon nanoparticles with a visco-elastoplastic particle-SEI model and discuss options to mitigate the size of the voltage hysteresis. This detailed physical understanding can improve the performance of all-silicon anodes and contribute to their commercialization.[1] Köbbing, L.; Latz, A.; Horstmann, B. J. Power Sources 2023, DOI: 10.1016/j.jpowsour.2023.232651.[2] Kolzenberg, L.; Latz, A.; Horstmann, B. Batter. Supercaps 2022, DOI: 10.1002/batt.202100216.[3] Köbbing, L.; Latz, A.; Horstmann, B. Adv. Funct. Mater. 2024, DOI: 10.1002/adfm.202308818.[4] Köbbing, L.; Latz, A.; Horstmann, B. (in preparation). Figure 1

Experimental Investigation on the Temperature Dependence of Open-Circuit Voltage Hysteresis in LiFePO4 Lithium-Ion Batteries

A commercial LiFePO4 Lithium-ion (LFP-LIB) cell was adopted to investigate hysteresis of open circuit voltage (OCV) in Lithium-ion batteries. Experiments were conducted within the typical operating temperature range of 0℃, 10℃, 20℃, 30℃, 40℃ and 45℃, covering state of charge (SOC) from 0% to 100%. The results showed that (i) OCV hysteresis is dependence on temperature and increases as the temperature decreases, and (ii) bimodality appears in certain SOCs. Comparing the impedances confirmed by electrochemical impedance spectroscopy (EIS), we found that charge-transfer resistance (Rct) increases significantly as the temperature decreases. This means that the LFP-LIB operating at lower temperatures suffers a higher polarization. This result indicates that identification of OCV hysteresis is essential for accurate SOC estimation. Figure 1

State of charge estimation with hysteresis-prone open circuit voltage in lithium-ion batteries using the trajectory correction hysteresis (TCH) model

State of charge estimation with hysteresis-prone open circuit voltage in lithium-ion batteries using the trajectory correction hysteresis (TCH) model

A physically motivated voltage hysteresis model for lithium-ion batteries using a probability distributed equivalent circuit

The open circuit voltage hysteresis of lithium-ion batteries is a phenomenon that, despite intensive research, is still not fully understood. However, it must be taken into account for accurate state-of-charge estimation in battery management systems. Mechanistic models of the open circuit voltage hysteresis previously published are not suitable for deployment in a battery management system. Phenomenological models on the other hand can only superficially represent the processes taking place. To address this limitation, we propose a probability distributed equivalent circuit model motivated by the physical insights into hysteresis. The model incorporates hysteresis effects that are often disregarded for state estimation, while keeping the computational cost low. Although the parameterization is more demanding, the model has the advantage of providing insight into the internal state of the battery and intrinsically incorporating the effect of path-dependent rate capability.

Building a P2D Model for the NiMH Battery

For understanding a battery when working under realistic conditions, a dynamic battery model is a useful tool. One of the most known electrochemical model types for batteries is the Newman P2D model.[1] This model has a good balance between complexity and computational efficiency. Due to the level of complexity, using a P2D model allows us to make simulations to optimize battery design by adjusting parameters such as electrode porosity and tortuosity, electrode thickness, and overall cell thickness. But it can also be used to optimize battery management system (BMS) functionality. There are many studies made on different Li-ion type batteries when it comes to P2D modelling, but to our knowledge there is no dynamic NiMH P2D model reported in literature. In this study we have created a dynamic P2D model through combining it with two previously developed models focused on the specific difficulties on NiMH modelling: Hysteresis estimation and gas behavior. [2,3]Two phenomena are paramount to include in dynamic NiMH battery models: the open circuit voltage (OCV) hysteresis; and the oxygen evolution/recombination reactions in the cell. The OCV hysteresis manifests as an OCV dependence on battery charge/discharge history. In the NiMH battery, the OCV hysteresis is of such a magnitude that one OCV value can be indicative of an SOC ranging between 20% and 90%, making any type of SOC determination algorithm unusable without taking the hysteresis behavior into account.[2] The hysteresis can predominantly be traced to the phase changes that takes place in the positive electrode as it is charged and discharged. This suggests that models of other battery electrodes with significant phase changes, such as LiS and Si, will have to take this behavior into account as well. While the hysteresis effect affects the energy efficiency of the battery, the gas reactions affect the coulombic efficiency of the battery. Since the NiMH battery has a water-based electrolyte, once the positive electrode voltage reaches above the oxygen evolution potential oxygen is produced.[3] This parasitic reaction diverts current from charging the battery, decreasing the coulombic efficiency. Once the oxygen reaches the negative electrode it is recombined and releasing the lost energy as heat. A model that fails to take the gas behavior into account will have difficulties to properly simulate the temperature behavior in the cell. In addition, the decreased coulombic efficiency affects any coulomb counting done as a part of SOC tracking in the cell.A dynamic model that takes both these behaviors into account can be used for many different purposes. Algorithms for use in battery management systems (BMS), for example end of charge criteria and state of charge estimation, will have higher accuracy when optimized using the model. A digital twin based on such a model can be used to monitor battery performance and deviations. System dimensioning can be optimized to achieve the best cost-to-life ratio. It can also be used to develop better usage patterns to get the most out of the battery. Through using the model to develop more efficient usage patterns, the influence of hysteresis and the gas evolution can be minimized, thereby increasing the efficiency of the system.[1] K.E. Thomas, J. Newman, R.M. Darling, Mathematical Modeling of Lithium Batteries, in: Adv. Lithium-Ion Batter., Springer US, Boston, MA, 2002: pp. 345–392. https://doi.org/10.1007/0-306-47508-1_13.[2] J.B. Axén, H. Ekström, E.W. Zetterström, G. Lindbergh, Evaluation of hysteresis expressions in a lumped voltage prediction model of a NiMH battery system in stationary storage applications, J. Energy Storage. 48 (2022). https://doi.org/10.1016/j.est.2022.103985.[3] J. Börjesson Axén, H. Ekström, E. Widenkvist Zetterström, G. Lindbergh, A NiMH dynamic pressure composition model for on-line applications, Manuscript

Quantifying the Open-Circuit Voltage (OCV) Hysteresis in Ternary Li-Rich Mn-Ni Layered Oxides

To achieve the high energy densities needed for automotive applications while keeping the cathode active material (CAM) cost as low as possible, lithium- and manganese-rich NCMs (LMR-NCMs), e. g., Li1.14(Ni0.26Co0.14Mn0.60)0.86O2, are considered as possible candidates. Furthermore, in the past years there was a tendency for replacing expensive and supply-limited cobalt from cathode active materials, resulting in cobalt-free LMR-NMs with the general composition x Li2MnO3 · (1-x) LiMO2 (M = Ni, Mn; 0.33 ≤ x ≤ 0.5) or alternatively expressed as Li1+δ[NiyMnz]1-δO2(y+z= 1, 0.14 ≤ δ ≤ 0.20).1 Even though these materials are able to reversibly provide capacities of ≈ 250 mAh/g, there are numerous challenges to overcome, like extensive gas evolution, capacity and voltage fading over extended charge/discharge cycling, and the substantial open-circuit voltage (OCV) hysteresis, which hamper the implementation of LMR-NCMs in the automotive sector.2–5 It was shown in previous publications that the OCV hysteresis, an intrinsic property of LMR-NCMs, significantly lowers the charge/discharge energy efficiency and thus increases heat generation.2, 6, 7 Furthermore, it was qualitatively shown that the voltage fading as well as the OCV hysteresis is dependent on x in x Li2MnO3 · (1-x) LiMO2 (M = Ni, Mn, Co).5, 6 In order to investigate the relation between composition and OCV hysteresis for Co-free overlithiated layered oxides, we synthesized CAMs with various compositions with the general formula x Li2MnO3 · (1-x) LiMO2 (M = Ni, Mn; 0.33 ≤ x ≤ 0.5), proving the presence of the layered structure of the materials with Rietveld refinements on the collected X-ray diffraction data. The OCV hysteresis of the synthesized and electrochemically activated LMR-NMs is determined from slow charge/discharge cycles with intermittent OCV holds. Based on these data, the OCV hysteresis of LMR-NMs can be correlated with their specific composition. References D. Andre, S.-J. Kim, P. Lamp, S. F. Lux, F. Maglia, O. Paschos and B. Stiaszny, J. Mater. Chem. A, 3(13), 6709–6732 (2015). L. Kraft, T. Zünd, D. Schreiner, R. Wilhelm, F. J. Günter, G. Reinhart, H. A. Gasteiger and A. Jossen, J. Electrochem. Soc., 168(2), 20537 (2021). J. R. Croy, M. Balasubramanian, K. G. Gallagher and A. K. Burrell, Accounts of chemical research, 48(11), 2813–2821 (2015). T. Teufl, B. Strehle, P. Müller, H. A. Gasteiger and M. A. Mendez, J. Electrochem. Soc., 165(11), A2718-A2731 (2018). J. R. Croy, K. G. Gallagher, M. Balasubramanian, B. R. Long and M. M. Thackeray, J. Electrochem. Soc., 161(3), A318-A325 (2014). F. Friedrich, S. Pieper and H. A. Gasteiger, J. Electrochem. Soc., 168(12), 120502 (2021). B. Strehle, T. Zünd, S. Sicolo, A. Kiessling, V. Baran and H. A. Gasteiger, J. Electrochem. Soc., 169(2), 20554 (2022). Acknowledgement This work is financially supported by the BASF SE Network on Electrochemistry and Battery Research.

Analysis of heat generation due to open-circuit voltage hysteresis in lithium-ion cells

For the application of lithium-ion batteries, the thermal management is an important aspect, as the temperature is one of the main parameters for the behavior of Li-ion batteries. Several mechanisms influence the thermal behavior of Li-ion batteries, which can be split into reversible and irreversible losses. In literature, mostly Joule heat and reversible heat due to the entropy is discussed. With upcoming new active materials like silicon, which exhibits a significant charge-direction dependent voltage hysteresis, the losses due to the hysteresis become a more prominent factor. In this paper, we show the influence of the hysteresis losses for the most common active materials, which exhibit a voltage hysteresis. With a detailed analysis of calorimeter measurements, a true equilibrium potential can be calculated. The results show, that the equilibrium potential is not in the middle between both open-circuit voltage (OCV) curves as most of the heat due to hysteresis is generated during charging, which can have a significant influence on the thermal behavior of Li-ion batteries. For pure graphite cells 63 % and for silicon-graphite composite cells even 75 % of the hysteresis heat is generated while charging.

Modelling Nickel Metal Hydride Batteries for System Integrated Applications

The NiMH battery is resilient against mistreatment and can be charged in a large temperature window. It also has a high power-capability, which has made it popular in for instance power tools. Today, the battery is often found in large scale stationary energy storage, as well as applications which require high power capability.In large scale energy storage, small differences in run cycles can have large total effects on power efficiency and safety. It is therefore desirable to develop better methods of battery management. In recent years, development in system hardware has made it possible to implement on-line models in battery management systems. Such an implementation requires a model capable of working in dynamic conditions simulating voltage, temperature, and pressure. In this study, a model for this purpose is developed and verified.There are two major features that present a challenge when modelling a NiMH-system dynamically: Open Circuit Voltage (OCV) hysteresis and pressure build-up. Handling the hysteresis effect is important to accurately predict voltage and OCV.[1] OCV is especially important, as OCV is used to estimate the battery state of charge (SOC). Pressure build-up can be an important tool for charge termination, as it is the build-up of oxygen pressure and subsequent temperature increase that signal end of charge, Figure 1. In addition, the hydrogen pressure behavior changes with battery age and can be used to estimate end-of-life.OCV hysteresis is when the open circuit voltage at a certain SOC depends on the path taken to reach that charge state. This mean that a battery that has been charged to 50% will have a drastically different OCV than when the same battery has been discharged to 50%. This effect is believed to arise primarily from structural changes in the positive electrode, where the potential of the electrode surface is dependent on the material structure of the surface. An example of open circuit voltage hysteresis in a NiMH cell is found in Figure 1.The gas phase in the battery consists of four gases: nitrogen, hydrogen, oxygen and water. Nitrogen is an inert remnant from the battery manufacture.[2] Water is in equilibrium with the aqueous electrolyte. Hydrogen is in equilibrium with intercalated hydrogen in the negative metal hydride (MH) electrode. Finally, oxygen is the product of a side reaction that occurs at high potentials on the positive electrode. The production of oxygen at the positive electrode and ensuing oxygen recombination on the negative electrode produces a great deal of heat, which heats the battery and produces the temperature response that can be used to set a temperature derivative determined end of charge criteria. However, the presence of oxygen and high temperatures also drives the major aging mechanism: oxidation of the negative electrode. As the negative electrode is consumed, the capacity decreases. This leads to a difference in the hydrogen equilibrium pressure, as reaching a higher intercalation fraction increases the equilibrium pressure. [3]An implementation of a model for system battery management could help improve the following functions for a NiMH system: SOC estimation, SOH estimation, charge termination, fault detection and aging prevention. Therefore, development of a fully dynamic NiMH model has great value in improving over all system function for large scale energy storage applications. This study presents a fully dynamic and experimentally verified model for the NiMH battery that takes both pressure and OCV hysteresis into consideration. References Axén, J. B.; Ekström, H.; Zetterström, E. W.; Lindbergh, G. Evaluation of Hysteresis Expressions in a Lumped Voltage Prediction Model of a NiMH Battery System in Stationary Storage Applications. J. Energy Storage 2022, 48 (January). https://doi.org/10.1016/j.est.2022.103985.Mank, A. J. G.; Belfadhel-Ayeb, A.; Krüsemann, P. V. E.; Notten, P. H. L. In Situ Raman Analysis of Gas Formation in NiMH Batteries. Appl. Spectrosc. 2005, 59 (1), 109–114. https://doi.org/10.1366/0003702052940503.Tserolas, V.; Katagiri, M.; Onodera, H.; Ogawa, H. Thermodynamical Modeling of P-C Isotherms for Metal Hydride Materials. Trans. Mater. Res. Soc. Japan 2010, 35 (2), 221–226. https://doi.org/10.14723/tmrsj.35.221. Figure 1 Left: Open Circuit Voltage of a NiMH cell with exhibited hysteresis behavior. Right: Pressure and Temperature behavior of a NiMH module during a charge discharge cycle. Figure 1

Classification of Heat Evolution Terms in Li-Ion Batteries Regarding the OCV Hysteresis in a Li- and Mn-Rich NCM Cathode Material in Comparison to NCA

We investigate the heat release of Li- and Mn-rich NCM (LMR-NCM) and NCA half-cells during cycling at different C-rates and quantify the individual contributions to the overall heat flow using a combination of isothermal micro-calorimetry and electrochemical methods. The paper focuses in particular on the open-circuit voltage (OCV) hysteresis of the LMR-NCM material, which results in a significant reduction in energy round-trip efficiency (≈90% for LMR-NCM/Li cells vs ≈99% for NCA/Li cells at C/10) and therefore in an additional source of heat that has to be considered for the thermal management of the cell. The total heat release of the LMR-NCM/Li cells is found to be nine times higher than that of the corresponding NCA/Li cells (at C/10). In the case of the LMR-NCM cathode, the heat due to OCV hysteresis is responsible for up to 55% of the total energy loss. Using the applied approach, the OCV hysteresis heat is separated into its share during charge and discharge and is furthermore presented as a function of SOC. Additional sources of heat, such as reversible entropic heat, parasitic effects, and measurement limitations, are discussed in terms of their contribution to the overall energy balance of the two cell chemistries.

Evaluation of hysteresis expressions in a lumped voltage prediction model of a NiMH battery system in stationary storage applications

As a part of battery system operation, battery models are often used to determine battery characteristics such as the state of charge (SOC) and the state of health (SOH). A phenomenon that has a large impact on battery model accuracy for NiMH batteries is open circuit voltage (OCV) hysteresis, which causes the OCV to differ not only with the SOC of the battery but also with the charge-discharge history. This characteristic is especially influential when running the system in applications with dynamic current patterns. A model including a way to emulate battery hysteresis behavior would improve the battery management system function. In this study a lumped battery model for cell voltage prediction was expanded to include an OCV hysteresis model. Different expressions to describe the hysteresis behavior were explored. The different models were then evaluated using both synthetic and real-life application experimental data. In all cases the error was reduced by adding a hysteresis component to the model. Using this type of model in the battery management system of stationary energy storage systems based on NiMH batteries could help make the state prediction more accurate. This, in turn, would allow for better optimization of the system operation, something that could help increase system efficiency and lifetime.

Entropy Measurements of Li-Ion Battery Cells with Li- and Mn-Rich Layered Transition Metal Oxides via Linear Temperature Variation

Changes in the partial molar entropy of lithium- and manganese-rich layered transition metal oxides (LMR-NCM) are investigated using a recently established electrochemical measuring protocol, in which the open-circuit voltage (OCV) of a cell is recorded during linear variation of the cell temperature. With this method, the entropy changes of LMR-NCM in half-cells were precisely determined, revealing a path dependence of the entropy during charge and discharge as a function of state of charge, which vanished as a function of OCV. This observation is in line with other hysteresis phenomena observed for LMR-NCM, of which the OCV hysteresis is the most striking one. For a systematic investigation of the entropy changes in LMR-NCM, measurements were conducted during the first activation cycle and in a subsequent cycle. In addition, two LMR-NCM materials with different degrees of overlithiation were contrasted. Contributions from configurational and vibrational entropy are discussed. Our results suggest that the entropy profile during activation exhibits features from the configurational entropy, while during subsequent cycling the vibrational entropy dominates the entropy curve.

Nimh Gas Model for Dynamic Behaviour Study

The NiMH battery is an intercalation battery with a positive NiOH electrode and a negative metal hydride electrode. The electrolyte used is a highly alkaline aqueous solution, typically 6:1 KOH:LiOH. As a consequence of this and the reaction potentials of the electrodes, splitting of water into its constituting elements is a part of normal battery operation. When charging at high SOC, as well as when overcharging, oxygen is formed as a side-reaction. It then diffuses through the separator and recombines at the negative electrode. This recombination cycle causes the temperature in the battery to rise, something that is commonly used to terminate battery charging, either through ΔT or -ΔV criteria. In addition to oxygen production at high state of charge, overdischarging of the battery can produce hydrogen at the positive electrode, with a pressure increase at end of discharge indicating a harmful overdischarge of the battery [1]. Hydrogen is also present in the battery gas composition as a consequence of the negative hydrogen intercalation electrode material which is in equilibrium with gaseous hydrogen, an equilibrium which shifts with electrode hydrogen content [2]. Figure Left: A schematic showing the main gas reactions in the metal NiMH battery. Right: An example cycle of a NiMH battery, showing cell voltage (blue), cell gas pressure (green) and cell surface temperature (teal).The internal gas reactions of the NiMH battery have a great impact on battery function, both in regard to energy efficiency and battery aging. While measurements of battery internal total gas pressure occur in some battery systems, measurements of individual constituents would be impractical in the field, as such measurements require laboratory equipment. Therefore, a gas model that can estimate the internal pressure distribution under dynamic conditions would be a valuable tool to study the effects of different application drive cycles, as well as a possibility to further enhance BMS function for battery stability and longevity.There have been many attempts at modeling the gaseous side reactions in the NiMH battery. Some of these models are part of comprehensive battery models, which combines a voltage response model with added side reactions [3,4]. Other models look only at specific gas related processes [5]. However, a limitation with these models is that they are not able to model the battery during dynamic current conditions. The main reason for this is the presence of a strong open circuit voltage hysteresis, something that these models do not capture well.This study presents a model of the gas reactions and heat generation in the NiMH battery. The model is not coupled to a voltage model, instead it simulates internal temperature and pressure when supplied with experimental current, voltage and surface temperature data. This removes the complication of the open circuit voltage hysteresis effect and lets us model dynamic battery behavior. The model is in zero dimensions and based on physical principles. There are several parameters that are optimized by fitting to experimental data. The model is then validated by simulating pressure for a second set of data, based on the fitted values of the parameters. The simplicity of the model not only allows for it to be used in battery monitoring hardware, it can also be coupled to voltage and multidimensional models to build a more comprehensive predictive model.References Notten, P. H. L.; Latroche, M. Nickel-Metal Hydride: Metal Hydrides. In Encyclopedia of Electrochemical Power Sources; Garche, J., Dyer, C., Moseley, P., Ogumi, Z., Rand, D., Scrosati, B., Eds.; Elsevier B.V., 2009; Vol. 50, pp 502–521.Tserolas, V.; Katagiri, M.; Onodera, H.; Ogawa, H. Thermodynamical Modeling of P-C Isotherms for Metal Hydride Materials. Trans. Mater. Res. Soc. Japan 2010, 35 (2), 221–226. https://doi.org/10.14723/tmrsj.35.221.Albertus, P.; Christensen, J.; Newman, J. Modeling Side Reactions and Nonisothermal Effects in Nickel Metal-Hydride Batteries. J. Electrochem. Soc. 2008, 155 (1), A48. https://doi.org/10.1149/1.2801381.Ledovskikh, A.; Verbitskiy, E.; Ayeb, A.; Notten, P. H. L. Modelling of Rechargeable NiMH Batteries. J. Alloys Compd. 2003, 356–357, 742–745. https://doi.org/10.1016/S0925-8388(03)00082-3.Notten, P. H. L.; Ouwerkerk, M.; Ledovskikh, A.; Senoh, H.; Iwakura, C. Hydride-Forming Electrode Materials Seen from a Kinetic Perspective. J. Alloys Compd. 2003, 356–357, 759–763. https://doi.org/10.1016/S0925-8388(03)00085-9. Figure 1

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

Effects of cycling on lithium-ion battery hysteresis and overvoltage

Currently, lithium-ion batteries are widely used as energy storage systems for mobile applications. However, a better understanding of their nature is still required to improve battery management systems (BMS). Overvoltages and open-circuit voltage (OCV) hysteresis provide valuable information regarding battery performance, but estimations of these parameters are generally inaccurate, leading to errors in BMS. Studies on hysteresis are commonly avoided because the hysteresis depends on the state of charge and degradation level and requires time-consuming measurements. We have investigated hysteresis and overvoltages in Li(NiMnCo)O2/graphite and LiFePO4/graphite commercial cells. Here we report a direct relationship between an increase in OCV hysteresis and an increase in charge overvoltage when the cells are degraded by cycling. We find that the hysteresis is related to diffusion and increases with the formation of pure phases, being primarily related to the graphite electrode. These findings indicate that the graphite electrode is a determining factor for cell efficiency.

Electrical Characterization and Micro X-ray Computed Tomography Analysis of Next-Generation Silicon Alloy Lithium-Ion Cells

This study analyzed a prototype of a pouch cell containing silicon alloy anodes with the potential to significantly increase the energy density, resulting in improved autonomy for electric vehicles. An electrical characterization campaign was performed, resulting in three main observations. Firstly, measurements showed a high energy density, although a high lower cutoff voltage (3.0 V) was used due to the prototypical nature of the cells. Further optimization would allow a decrease of the lower cutoff voltage, resulting in an even higher energy density. Secondly, a large open-circuit voltage hysteresis was observed, increasing the complexity for equivalent circuit models. Thirdly, ballooning of the pouch cell was observed, most likely caused by gas formation. This leads to a loss of active surface area, significantly reducing the cell’s capacity. This third observation was more thoroughly investigated by 3D computed tomography, which showed mechanical deformation of the layers. An extensive literature review revealed that the addition of fluoroethylene carbonate (FEC) to the electrolyte enhances the cycling stability of silicon alloy batteries but leads to the production of CO 2 as a side reaction. Furthermore, the usage of external pressure was proposed and validated as a methodology to reduce the production of CO 2 while improving the cells’ performance.

Covariance controlled state-of-charge estimator of LiFePO4 cells using a simplified hysteresis model

Equivalent circuit model (ECM)-based state-of-charge (SOC) estimation has been considered as one of the most important aspects in battery management system (BMS). However, in case of a lithium iron phosphate (LiFePO4) cell, because of the flatness and hysteresis effect of the open-circuit voltage (OCV) curve, there are inevitable drawbacks directly related to both erroneous SOC information and the slow SOC convergence speed caused by incorrect OCV characteristics. Therefore, this approach gives insight to the design and implementation of the ECM-based SOC estimator that is suitable for an actual LiFePO4 cell. Two approaches for settlement in current OCV issues are as follows. Firstly, through linearization between coordinated charging and discharging OCVs, an OCV hysteresis model can be easily implemented. This model incorporating OCV measurement data is adequately applied to the model-based SOC estimator using the extended Kalman filter (EKF). Secondly, a well-adjusted measurement error covariance controlled in the EKF is used to alleviate an undesired SOC fluctuation that surely results in low BMS performance. This measurement error covariance additionally enables us to provide the fast SOC convergence speed against an inaccurate initial SOC value. This approach has been sufficiently validated by extensive experimental results conducted on LiFePO4 cells that had a rated capacity of 14 Ah by EIG. Consequently, our validation showed the clearness of the proposed work for a reliable SOC estimator of a LiFePO4 cell.

Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides

Lithium-rich layered transition metal oxide positive electrodes offer access to anion redox at high potentials, thereby promising high energy densities for lithium-ion batteries. However, anion redox is also associated with several unfavorable electrochemical properties, such as open-circuit voltage hysteresis. Here we reveal that in Li1.17–xNi0.21Co0.08Mn0.54O2, these properties arise from a strong coupling between anion redox and cation migration. We combine various X-ray spectroscopic, microscopic, and structural probes to show that partially reversible transition metal migration decreases the potential of the bulk oxygen redox couple by > 1 V, leading to a reordering in the anionic and cationic redox potentials during cycling. First principles calculations show that this is due to the drastic change in the local oxygen coordination environments associated with the transition metal migration. We propose that this mechanism is involved in stabilizing the oxygen redox couple, which we observe spectroscopically to persist for 500 charge/discharge cycles.

Modeling of LiFePO4 battery open circuit voltage hysteresis based on recursive discrete Preisach model**Project supported by the National Natural Science Foundation of China (Grant No. 51107052).

This paper focuses on the modeling of LiFePO4 battery open circuit voltage (OCV) hysteresis. There exists obvious hysteresis in LiFePO4 battery OCV, which makes it complicated to model the LiFePO4 battery. In this paper, the recursive discrete Preisach model (RDPM) is applied to the modeling of LiFePO4 battery OCV hysteresis. The theory of RDPM is illustrated in detail and the RDPM on LiFePO4 battery OCV hysteresis modeling is verified in experiment. The robust of RDPM under different working conditions are also demonstrated in simulation and experiment. The simulation and experimental results show that the proposed method can significantly improve the accuracy of LiFePO4 battery OCV hysteresis modeling when the battery OCV characteristic changes, which conduces to the online state estimation of LiFePO4 battery.

A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries

Abstract The knowledge of nonlinear monotonic correlation between State-of-Charge (SoC) and open-circuit voltage (OCV) is necessary for an accurate battery state estimation in battery management systems. Among the main factors influencing the OCV behavior of lithium-ion batteries (LIBs) are aging, temperature and previous history of the battery. In order to develop an accurate OCV-based SoC estimator, it is necessary that the OCV behavior of the LIBs is sufficiently investigated and understood. In this study, the impact of the mentioned factors on OCV of LIBs at different aging states using various active materials (C/NMC, C/LFP, LTO/NMC) is investigated over a wide temperature range (from −20 °C to +45 °C) comprehensively. It is shown that temperature and aging of the battery influence the battery's relaxation behavior significantly where a linear dependence between the required relaxation time and the temperature can be assumed. Moreover, the required relaxation time increases with decreasing SoC and temperature. Furthermore, we state that for individual LIB, the OCV and the OCV hysteresis change over the battery lifetime. Based on the obtained results a simplified OCV model considering temperature correction term and aging of the battery is proposed.

Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

Abstract The key technology of a battery management system is to online estimate the battery states accurately and robustly. For lithium iron phosphate battery, the relationship between state of charge and open circuit voltage has a plateau region which limits the estimation accuracy of voltage-based algorithms. The open circuit voltage hysteresis requires advanced online identification algorithms to cope with the strong nonlinear battery model. The available capacity, as a crucial parameter, contributes to the state of charge and state of health estimation of battery, but it is difficult to predict due to comprehensive influence by temperature, aging and current rates. Aim at above problems, the ampere-hour counting with current correction and the dual adaptive extended Kalman filter algorithms are combined to estimate model parameters and state of charge. This combination presents the advantages of less computation burden and more robustness. Considering the influence of temperature and degradation, the data-driven algorithm namely least squares support vector machine is implemented to predict the available capacity. The state estimation and capacity prediction methods are coupled to improve the estimation accuracy at different temperatures among the lifetime of battery. The experiment results verify the proposed methods have excellent state and available capacity estimation accuracy.