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Electric vehicles, as a major strategy for climate change mitigation, uses lithium-ion batteries extensively as the power source. However, the operation, performance and lifetime of lithium-ion batteries depend on the battery temperature, which can have a wide range due to heat generation within the battery and significant variations in the ambient conditions due to changes in seasons and geographical locations where electric vehicles are operated. In the present study, thermal management methods/strategies on the capacity fade of lithium-ion batteries are assessed through a validated capacity fade model for lithium-ion batteries along with a thermal model for the heat generation in the battery and dissipation over battery surface, represented by various thermal management methods. The driving conditions are simulated through a constant and various standard drive cycles. It is shown that battery temperature has the predominant impact on the capacity fade, and it can be controlled through effective thermal management. A much more significant spread in battery capacity fade occurs with various thermal management methods for a lower initial battery temperature (20°C) compared to the higher temperatures (35°C and 50°C), hence, thermal management is much more effective in reducing capacity fade at battery temperatures close to 20°C, which is considered the optimum operating temperature for lithium-ion batteries. Further, the results indicate that using a lower charge voltage can result in slightly less capacity fade over cycling. Regenerative braking makes it more realistic to use lower charge voltages, since the battery can be recharged during operation, thereby increasing driving range, while preventing increased capacity fade. Effective thermal management is more imperative for realistic intense and aggressive driving behaviors. [ABSTRACT FROM AUTHOR]

It is still unknown how dissolved manganese ions affect the silicon anode's electrochemical performance in the lithium-ion batteries (LIBs). In this study, the damage mechanism of Mn2+ to silicon electrode in LIBs was studied by adding Mn2+ into electrolyte system to simulate the electrochemical environment. Through the comparison between full cell and half cell, the mechanism of the capacity fading of silicon electrode is revealed. In order to compare the amount of SEI growth of silicon anode during cycling, the heat flux of SEI was analyzed by DSC. Experiments shows that Mn2+ could make SEI more fragile, more easily break, and then accelerate the SEI thickening. So Mn2+ could reduce the Coulomb efficiency and electrochemical capacity of the silicon-based electrode. The galvanostatic cycle current is 300 mA/g. The half cell's Coulomb efficiency exceeds 97%, whereas the whole cell's Coulomb efficiency is only 32% after 100 cycles. In addition to the damage of the Mn2+ to silicon anode, the depletion of active lithium ion source in full cell is also an important reason for the rapid decline of electrochemical capacity. [ABSTRACT FROM AUTHOR]

In view of the problem of rapid estimation of capacity attenuation after multiple charge and discharge cycles of LIB(lithium-ion battery), a new method based on the capacity attenuation model of lithium-ion battery is proposed to rapidly estimate its internal capacity attenuation using the external expansion displacement of the battery. Based on the capacity attenuation model of LIB, the radial irreversible expansion model is derived, and the cylindrical LIB Sanyo UR18650E is modeled and solved by COMSOL Multiphysics, and the numerical results are compared with the experimental data, so as to verify the proposed model. Based on the above model, the causes of capacity attenuation and irreversible expansion of battery due to side reactions during the charge-discharge cycle are analyzed. The results show that the change of side reaction rate causes the gradient distribution of the concentration of side reaction products in the negative electrode, and the accumulation of side reaction products causes the expansion displacement of the battery to increase linearly with the cycle. The function formula of the cell capacity attenuation and its radial displacement is obtained by using the side reaction product as a bridge, which provides a new method for the rapid estimation of the capacity attenuation. [ABSTRACT FROM AUTHOR]

This work presents a follow up of previous case studies from the authors on the estimation of the in-vehicle battery capacity fade with performance-based models of Lithium-ion batteries applied to real-world vehicles’ activity data. The mobility pattern database is significantly enlarged compared to previous works from the authors, encompassing now six European regions, including 508,609 private vehicles which corresponds to 1.78 billion GPS records, 9.1 million trips and parking events and a total driven distance of 106.1 million kilometres. The capacity fade is derived at given years and mileage thresholds under different driving and recharging scenarios, supporting the concept of setting minimum performance requirements for traction batteries in future road transport policies. The vast majority of the considered scenarios do not lead to a battery capacity drop below 80% of its nominal value in less than five calendar years or 100,000 driven kilometres and below 70% in less than 8 years or 160,000 driven kilometres. The results of this work contributed to inform the new UN ECE Global Technical Regulation No. 22 on in-vehicle battery durability.

Lithium-ion (Li-ion) batteries are used in electric vehicles to reduce reliance on fossil fuels because of their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance decline when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principles of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Subsequently, the reliability assessments of Li-ion batteries are detailed using both qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in the early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, whereas quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Additionally, the quantitative techniques tend to be more cost-effective than their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation is highlighted to guide further research.

Efficient heat dissipation in lithium-ion battery packs is crucial for safety, necessitating a thorough assessment of thermal performance during the design phase. This study utilizes Newman’s Pseudo two-dimensional (P2D) model and three-dimensional computational fluid dynamics to depict heat generation and dissipation. The main objectives include evaluating heat dynamics, establishing optimal temperature limits, and assessing State of Charge (SOC) and State of Health (SOH) estimations. The battery pack, configured in 6s2p, features plastic-wrapped cylindrical modules with air-filled domains, connected in parallel pairs with aluminum wires. Active materials comprise positive electrodes like Lithium Cobalt Oxide (LCO), Lithium Nickel Manganese Cobalt Oxide (NMC), Lithium Iron Phosphate (LFP), and Lithium Manganese Oxide (LMO), with graphite as the negative electrode and a separator (Lithium Hexa Fluoro Phosphate). Results indicate that air-cooled NMC electrode chemistry achieves a peak heat dissipation of 102.48W, maintaining an optimal temperature range of 21.3deg C to 32.8deg C. After 1000 cycles, the optimal temperature limit of 32.8deg C with an 80% capacity loss minimizes negative impacts during charge/discharge cycles. SOC and SOH remain at 100% and 80%, utilizing an 8Ah battery with a nominal voltage of 4.2V, and a 3.9-year life expectancy, addressing charge/discharge cycle issues.

This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something current models often lack but is essential for system level simulations, relevant for electric vehicle manufacturers. The aging analysis includes both cycling and calendar effects across the lifetime of the cell and reversible and irreversible heat in a lumped-mass model to capture the temperature evolution of the cell in operation. The Thévenin equivalent circuit model with capacitance used to simulate the electrical behavior of the cell was experimentally validated, showing a high correlation with the proposed model during the charging and discharging phases. A maximum error of 3% on the voltage reading was identified during discharge with the complete model. This model was also designed to be used as a stepping stone for a comprehensive model at the module and vehicle levels that can later be used by designers.

The battery pack powers the electric motor in a battery-operated electric vehicle. To achieve the required power, the cells are connected in series and parallel combinations to form a battery pack. The battery pack is monitored using the battery management system. During the charging and discharging process, imbalance occurs in the cells due to intrinsic and extrinsic properties of the battery chemistry. This cell imbalance induces problems, such as an under-discharge, over-charge, increase in charging time and reduction in battery lifecycle. The passive and active balancing technique is employed to balance the individual cells in the battery pack. In this paper, the adaptive passive cell balancing is performed for a battery pack of six series-connected Li-ion cells of rating 3.6 V, 4 Ah under ideal, charging, discharging and drive cycle conditions using MATLAB/Simscape. In this proposed adaptive passive cell balancing methodology, a dynamic resistance is selected based on the threshold values to balance the individual cells in the battery pack. For this battery pack, the proposed design achieves 34% reduction in balancing time, 17% reduction in energy loss, and 14% reduction in power loss under ideal conditions. The experimental verification is also done and shows that the balancing time is about 2400 s. The capacity fade factor of the battery pack is also analyzed. [ABSTRACT FROM AUTHOR]

Silicon (Si) materials for use in Lithium ion batteries (LIBs) are of continued interest to battery manufacturers. With an increasing number of commercially available Si materials, evaluating their performance becomes a challenge. Here, we use an empirical fitting function presented earlier to aid in the analysis of galvanostatic charge‐discharge data of commercial Si half‐cells with relatively high loading. We find that the fitting procedure is capable of detecting dynamic changes in the cell, such as reversible capacity fade of the Si electrode. This fading is found to be due to the highly lithiated Li2Si↽⇀ ${ \mathbin{{\stackrel{\textstyle\rightharpoonup} { {\smash{\leftharpoondown}} } }} }$ Li3.5Si phase and that the behaviour is strongly dependent on the potential of this phase. EIS reveals that the Si electrode is responsible for the reversible behaviour due to progressive loss of Li+ leading to increasing resistance. SEM/EDX and XPS characterization are also employed to determine the origin of the irreversible resistance growth on the Si electrodes. [ABSTRACT FROM AUTHOR]

Abstract Silicon (Si) materials for use in Lithium ion batteries (LIBs) are of continued interest to battery manufacturers. With an increasing number of commercially available Si materials, evaluating their performance becomes a challenge. Here, we use an empirical fitting function presented earlier to aid in the analysis of galvanostatic charge‐discharge data of commercial Si half‐cells with relatively high loading. We find that the fitting procedure is capable of detecting dynamic changes in the cell, such as reversible capacity fade of the Si electrode. This fading is found to be due to the highly lithiated Li2Si ↽⇀ Li3.5Si phase and that the behaviour is strongly dependent on the potential of this phase. EIS reveals that the Si electrode is responsible for the reversible behaviour due to progressive loss of Li+ leading to increasing resistance. SEM/EDX and XPS characterization are also employed to determine the origin of the irreversible resistance growth on the Si electrodes.

Since lithium-ion batteries (LIBs) are essential to many different sectors, accurate estimates of their Remaining Useful Life (RUL) are necessary to maximize Battery Management Systems (BMS). In this study, we introduce an innovative approach that combines machine learning techniques to create a hybrid model, enhancing the precision and reliability of battery analysis. Our proposed model leverages the power of k-Nearest Neighbors (kNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms to capture complex relationships and patterns in battery data effectively. Our major objective is to precisely estimate the residual energy and RUL of LIBs, allowing for the efficient evaluation of battery health and deterioration over time. We meticulously curate a comprehensive dataset comprising essential battery parameters, including capacity, voltage, cycle, and temperature. The proposed hybrid model achieves impressive results with an R2 value of 0.996457, a minimal RMSE of 0.016861, and a low MAE of 0.008956. Our analysis provides valuable insights for optimizing battery performance, informed maintenance planning, and enhancing energy storage system efficiency.

With the development of electric vehicles, higher requirements for battery energy density are put forward. High nickel/SiOx-C batteries with high energy density have become the first choice for long-range electric vehicles. However, high nickel/SiOx-C batteries have the problem of rapid capacity decay in practical use. Non-destructive electrochemical analysis and post-mortem analysis were used to detect the changes in battery capacity and internal resistance during the cycle. The changes in the structure, material morphology and surface composition of positive and negative electrodes before and after the battery cycle were compared, revealing the mechanism of cycling failure of high nickel/SiOx-C batteries. The results show that the capacity decay of the battery presents three stages: the stationary period, the rapid decay period and the extreme decay period. After cycling, the polarization of the battery is more serious, and the polarization internal resistance of the battery, the surface film resistance of the negative electrode and the charge transfer resistance increase significantly. Through differential curve analysis combined with disassembly analysis, it is found that the high nickel cathode material has less attenuation, and the silica carbon anode material has more attenuation and active lithium-ion loss. The expansion and cracking of silicon oxide particles, the loss of negative electrode active material, and the continuous growth of solid electrolyte interface film on the negative electrode surface consuming too much active lithium are the main reasons for the rapid decline of battery capacity.

In this paper, capacity fade of LiFeYPO4/graphite commercial cells during 116 cycles under different temperatures is studied. The cells were discharged in two modes, during Drive Cycle (DrC) discharge cycles the cell was discharged with current waveform calculated for example battery electric vehicle (BEV) under WLTC 3b drive cycle conditions, whereas during Constant Current (CC) discharge cycles the cell was discharged with a constant current of the same root mean square of the current, as the WLTC 3b current waveform and with the same depth of discharge. All the cells were charged in constant current/constant voltage mode. Two fresh cells were used for each discharge mode at 25 °C and as the results were similar, only one cell per discharge mode was used at the other temperatures 5 °C and 45 °C. Furthermore, simulation P2D model of calendar and cycle life was calibrated based on experimental data. SoC floating was observed during cycling for both discharge modes, accompanied with slight increase in end discharge voltage and growth of energy efficiency. Concluding the results for 25 °C, not waveform character, but the amount of electric charge in combination with calendar aging has the most effect on the cycle life, which is also proved by the simulation. For 5 °C, the capacity fade is milder for DrC discharge cycles, but simulation results do not prove that, which would demand further investigation. The results for 45 °C are apparently dependent on a higher amount of discharged and charged electric charge and influenced by calendar life, simulated capacity fade corresponds quite well to the experiment. The best State of Health (SoH) simulation results are for temperature 45 °C, RMSE is 0.10% SoH, for the other temperatures RMSE is 0.20 and 0.93% SoH for 25 and 5 °C, respectively. [ABSTRACT FROM AUTHOR]

The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030—this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.

The exceptional advantages of vanadium redox flow batteries (VRFBs) have garnered significant attention, establishing them as the preferred choice for large-scale and long-term energy storage solutions. However, side reactions such as hydrogen evolution reaction (HER) lead to suboptimal performance of VRFB parameters, resulting in an overall decrease in VRFB performance. Therefore, it is imperative to investigate the mechanism by which HER affects VRFB performance and identify effective methods to mitigate its impact. In light of this, critical parameters such as charge-discharge curve, internal resistance, pressure-drop, pump power consumption, efficiency, and capacity retention rate are selected to evaluate the impact of HER on VRFB performance. Experimental results demonstrate that HER causes an increases in voltage during charging and a decrease during discharging stages leading to voltage loss; additionally, bubble generation significantly increases pressure drop and pump power consumption; furthermore, blockage of flow channels and electrodes due to bubbles leads to an increase in ohmic resistance. Finally, HER has a substantial impact on efficiency and capacity with an average energy efficiency of 2.92 %. After 60 cycles, the capacity retention rate decreased by nearly 7.69 %. [ABSTRACT FROM AUTHOR]

The capacity degradation of lithium-ion batteries occurs both during storage and operational usage. This paper investigates the capacity degradation of lithium-ion batteries during storage (calendar ageing) by analysing the interplay of storage temperature, state-of-charge (SOC), and time. Leveraging the machine learning techniques of Gaussian process regression and extreme gradient boosting (XGBoost), a predictive model is developed to characterize the degradation patterns. The study includes a sensitivity analysis of stress factors to identify their relative impact on degradation. The insights gained from this analysis are utilized to recommend optimal storage conditions, offering practical guidance for enhancing the durability and performance of lithium-ion batteries in real-world applications. • Analysis of influence of storage conditions on the capacity degradation in batteries. • Evaluation metric for correlation to account for variation in State of Charge (SOC), storage period, temperature and capacity. • The parametrization of temperature in the calendar ageing process of lithium-ion batteries is identified. • Optimal storage conditions for batteries are recommended. [ABSTRACT FROM AUTHOR]

Due to its numerous advantages, lithium-ion batteries have been widely used in various fields. However, as the application scenarios expand, batteries often encounter adverse environments such as high temperature and high humidity during storage and usage. Faced with such conditions, batteries not only experience performance degradation but also the risk of leakage, posing a serious threat to battery safety. In order to investigate the influencing factors of battery performance degradation and the failure modes of battery leakage under harsh conditions, we conducted a study using a commercial LiCoO 2 /graphite pouch cell as the experimental object. We focused on studying the capacity degradation mechanism and sealing failure modes during the storage process under high temperature-low humidity and high temperature-high humidity conditions. The results revealed that compared to the cell under normal temperature-low humidity conditions, the remaining capacity and recovery capacity of the cell under high temperature-low humidity and high temperature-high humidity conditions has shown a certain degree of attenuation. Subsequently, we investigated the underlying mechanisms behind the performance degradation through a series of characterizations. Additionally, regarding the issue of leakage, we analyzed the outer sealing materials of the cell to identify potential pathways for external moisture ingress, aiming to provide references and support for enhancing battery safety in harsh environments. [Display omitted] • Mechanistic study on the performance degradation of commercial Li-ion batteries. • Analysis of sealing failure modes and safety issues in pouch cell batteries. • Research on calendar aging of Li-ion batteries under multi-storage conditions. [ABSTRACT FROM AUTHOR]

In this review, the state of the art of modified membranes developed and applied for the improved performance of redox flow batteries (RFBs) is presented and critically discussed. The review begins with an introduction to the energy-storing chemical principles and the potential of using RFBs in the energy transition in industrial and transport-related sectors. Commonly used membrane modification techniques are briefly presented and compared next. The recent progress in applying modified membranes in different RFB chemistries is then critically discussed. The relationship between a given membrane modification strategy, corresponding ex situ properties and their impact on battery performance are outlined. It has been demonstrated that further dedicated studies are necessary in order to develop an optimal modification technique, since a modification generally reduces the crossover of redox-active species but, at the same time, leads to an increase in membrane electrical resistance. The feasibility of using alternative advanced modification methods, similar to those employed in water purification applications, needs yet to be evaluated. Additionally, the long-term stability and durability of the modified membranes during cycling in RFBs still must be investigated. The remaining challenges and potential solutions, as well as promising future perspectives, are finally highlighted.

Electric vehicles, as a major strategy for climate change mitigation, uses lithium-ion batteries extensively as the power source. However, the operation, performance and lifetime of lithium-ion batteries depend on the battery temperature, which can have a wide range due to heat generation within the battery and significant variations in the ambient conditions due to changes in seasons and geographical locations where electric vehicles are operated. In the present study, thermal management methods/strategies on the capacity fade of lithium-ion batteries are assessed through a validated capacity fade model for lithium-ion batteries along with a thermal model for the heat generation in the battery and dissipation over battery surface, represented by various thermal management methods. The driving conditions are simulated through a constant and various standard drive cycles. It is shown that battery temperature has the predominant impact on the capacity fade, and it can be controlled through effective thermalmanagement. Amuchmore significant spread in battery capacity fade occurs with various thermal management methods for a lower initial battery temperature (20℃) compared to the higher temperatures (35℃ and 50℃), hence, thermal management is much more effective in reducing capacity fade at battery temperatures close to 20℃, which is considered the optimum operating temperature for lithium-ion batteries. Further, the results indicate that using a lower charge voltage can result in slightly less capacity fade over cycling. Regenerative braking makes itmore realistictouse lowerchargevoltages, since the battery can be recharged during operation, thereby increasing driving range, while preventing increased capacity fade. Effective thermal management is more imperative for realistic intense and aggressive driving behaviors. [ABSTRACT FROM AUTHOR]

In this study, we present a modeling approach to estimate the combined effects of cyclable lithium loss and electrolyte depletion on the capacity and discharge power fades of lithium-ion batteries (LIBs). The LIB cell based on LiNi0.6Co0.2Mn0.2O2 (NCM622) was used to model the discharge behavior in the multiple degradation modes. The discharge voltages for nine different levels of cyclable lithium loss and electrolyte depletion were measured experimentally. When there was no cyclable lithium loss, the 50% of electrolyte depletion brought about 5% reduction in discharge capacity at 0.05 C discharge rate, while it resulted in 46% reduction when it was coupled with 30% of cyclable lithium loss. The 50% of electrolyte depletion with no cyclable lithium loss caused 1% reduction in discharge power during 0.5 C discharge at the state of charge (SOC) level of 0.8, while it resulted in 13% reduction when it was coupled with 30% of cyclable lithium loss. The modeling results obtained by using the one-dimensional finite element method were compared with the experimental data. The justification of the modeling methods is demonstrated by the high degree of concordance between the predicted and experimental values. Using the validated modeling methodology, the discharge capacity and usable discharge power can be estimated effectively under various combined degradation modes of cyclable lithium loss and electrolyte depletion in the LIB cell. [ABSTRACT FROM AUTHOR]

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Abstract Lithium‐ion technologies have become the most attractive and selected choice for battery electric vehicles. However, the understanding of battery aging is still a complex and nonlinear experience which is critical to the modeling methodologies. In this work, a comprehensive lifetime modeling twin framework following semi‐empirical methodology has been developed to predict the crucial degradation outputs accurately in terms of capacity fade and resistance increase. The constructed model considers all the relevant aging influential factors for commercial nickel manganese cobalt (NMC) Li‐ion cells based on long‐term laboratory‐level investigation and combines both the cycle life and the calendar life aspects. To demonstrate robustness, the model is validated with a real‐life worldwide harmonized light‐duty test cycle (WLTC). The model can precisely predict the capacity fade and the internal resistance growth with a root‐mean‐squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model can be used as an advanced online tool forecasting the lifetime based on dynamic profiles.

Electric vehicles, as a major strategy for climate change mitigation, uses lithium-ion batteries extensively as the power source. However, the operation, performance and lifetime of lithium-ion batteries depend on the battery temperature, which can have a wide range due to heat generation within the battery and significant variations in the ambient conditions due to changes in seasons and geographical locations where electric vehicles are operated. In the present study, thermal management methods/strategies on the capacity fade of lithium-ion batteries are assessed through a validated capacity fade model for lithium-ion batteries along with a thermal model for the heat generation in the battery and dissipation over battery surface, represented by various thermal management methods. The driving conditions are simulated through a constant and various standard drive cycles. It is shown that battery temperature has the predominant impact on the capacity fade, and it can be controlled through effective thermal management. A much more significant spread in battery capacity fade occurs with various thermal management methods for a lower initial battery temperature (20°C) compared to the higher temperatures (35°C and 50°C), hence, thermal management is much more effective in reducing capacity fade at battery temperatures close to 20°C, which is considered the optimum operating temperature for lithium-ion batteries. Further, the results indicate that using a lower charge voltage can result in slightly less capacity fade over cycling. Regenerative braking makes it more realistic to use lower charge voltages, since the battery can be recharged during operation, thereby increasing driving range, while preventing increased capacity fade. Effective thermal management is more imperative for realistic intense and aggressive driving behaviors.

Lithium-ion batteries are considered as a promising energy source due to the high energy density and long cycle life. In this paper, the authors propose a model of a lithium-ion battery capacity fade and the electrochemical reactions in the battery. To improve the properties of the electrolyte and the lithium/electrolyte interface, the additive tris-(2,2,2-trifluoroethyl)-borate (TTFEB) is added into the carbonatebased electrolyte. The authors also study the factors influencing the battery capacity, including the resistance of solid electrolyte interface (SEI), the active material particle radius, the discharging rate, and the ambient temperature. The simulation results show that the lithium-ion battery life is determined by the capacity decay rate, the electrochemical mechanism, and the physicochemical parameters. [ABSTRACT FROM AUTHOR]

The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030—this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade. [ABSTRACT FROM AUTHOR]

Metal‐based non‐aqueous redox flow batteries have the potential for long‐term energy storage if stability requirements can be achieved. One such stability issue in metal coordination complexes arises from ligand shedding in the anolyte upon reduction. Recognizing that the free ligands are relatively more stable than the metal coordination complex under highly reducing conditions, we evaluated a family of metal‐free bipyridinium materials as flow battery anolytes with the corresponding iron coordination complex as the catholyte. Bipyridinium compounds were functionalized for increased electrochemical stability, cycled in flow cells to understand efficiencies, and analyzed for degradation products. Methylation of the bipyridine nitrogens increased electrochemical stability yet left the reduced molecule susceptible to radical‐induced bond cleavage. Subsequent functionalization of bipyridine with carbomethoxy groups resulted in good battery performance with 96 % Coulombic and 90 % voltage efficiency, and improved cycling stability over a methoxy‐substituted anolyte with 14 % vs 36 % capacity fade in the first charge‐discharge cycle. [ABSTRACT FROM AUTHOR]

Summary: We develop an integrated framework of modeling and experiments to explain the capacity fade of the lithium‐ion battery containing CuCo2O4 as the anode. The electrical conductivity, diffusion coefficient, and open‐circuit voltage curves are measured from the fabricated electrodes, and the obtained properties are used for the input parameters of the side‐reaction coupled electrochemical model. During the simulation, two scenarios of the degradation are considered: only the solid electrolyte interphase (SEI) formation, and coupled degradation of the SEI formation and the Cu dissolution/deposition. Sensitivity analysis is carried out with different SEI molar mass, SEI conductivity, and dissolution parameters, to evaluate the effect on the cell capacity fade. The simulation shows that active material dissolution in ternary metal oxide electrodes plays a critical role in capacity fading, and the interactions of SEI formation and active material dissolution determine the battery life. The experiment and simulation integrated framework developed in this study can be used to predict the capacity fade of the battery systems with conversion‐based electrodes. [ABSTRACT FROM AUTHOR]

Battery capacity fade analysis was conducted to enhance both battery life and safety for different battery thermal managements based on natural convection, forced convection, finned-natural convection, PCM, and liquid cooling channels combined with PCM. The numerical model was developed according to Newman's pseudo two-dimensional (P2D) model, which included the generation of electrochemical heat. The capacity fade analysis showed that the temperature limit of 45 °C had the least negative impact on the battery life during charge/discharge cycles. In addition, the capacity fade was considerable as the battery temperature rose, showing 70% capacity loss after 500 cycles at 55 °C. The PCM provided a more uniform temperature distribution across the battery pack, however required extra heat dissipation mechanism. PCM cooling with liquid cooling channels could satisfy the temperature limits and significantly enhance the performance of battery heat management system.

In this paper, capacity fade of LiFeYPO4/graphite commercial cells during 116 cycles under different temperatures is studied. The cells were discharged in two modes, during Drive Cycle (DrC) discharge cycles the cell was discharged with current waveform calculated for example battery electric vehicle (BEV) under WLTC 3b drive cycle conditions, whereas during Constant Current (CC) discharge cycles the cell was discharged with a constant current of the same root mean square of the current, as the WLTC 3b current waveform and with the same depth of discharge. All the cells were charged in constant current/constant voltage mode. Two fresh cells were used for each discharge mode at 25 °C and as the results were similar, only one cell per discharge mode was used at the other temperatures 5 °C and 45 °C. Furthermore, simulation P2D model of calendar and cycle life was calibrated based on experimental data. SoC floating was observed during cycling for both discharge modes, accompanied with slight increase in end discharge voltage and growth of energy efficiency. Concluding the results for 25 °C, not waveform character, but the amount of electric charge in combination with calendar aging has the most effect on the cycle life, which is also proved by the simulation. For 5 °C, the capacity fade is milder for DrC discharge cycles, but simulation results do not prove that, which would demand further investigation. The results for 45 °C are apparently dependent on a higher amount of discharged and charged electric charge and influenced by calendar life, simulated capacity fade corresponds quite well to the experiment. The best State of Health (SoH) simulation results are for temperature 45 °C, RMSE is 0.10% SoH, for the other temperatures RMSE is 0.20 and 0.93% SoH for 25 and 5 °C, respectively.

The transition toward electric-powered devices is anticipated to play a pivotal role in advancing the global net-zero carbon emission agenda aimed at mitigating greenhouse effects. This shift necessitates a parallel focus on the development of energy storage materials capable of supporting intermittent renewable energy sources. While lithium-ion batteries, featuring inorganic electrode materials, exhibit desirable electrochemical characteristics for energy storage and transport, concerns about the toxicity and ethical implications associated with mining transition metals in their electrodes have prompted a search for environmentally safe alternatives. Organic electrodes have emerged as promising and sustainable alternatives for batteries. This review paper will delve into the recent advancements in nature-inspired electrode design aimed at addressing critical challenges such as capacity degradation due to dissolution, low operating voltages, and the intricate molecular-level processes governing macroscopic electrochemical properties., (© 2024 Wiley‐VCH GmbH.)

Lithium‐ion technologies have become the most attractive and selected choice for battery electric vehicles. However, the understanding of battery aging is still a complex and nonlinear experience which is critical to the modeling methodologies. In this work, a comprehensive lifetime modeling twin framework following semi‐empirical methodology has been developed to predict the crucial degradation outputs accurately in terms of capacity fade and resistance increase. The constructed model considers all the relevant aging influential factors for commercial nickel manganese cobalt (NMC) Li‐ion cells based on long‐term laboratory‐level investigation and combines both the cycle life and the calendar life aspects. To demonstrate robustness, the model is validated with a real‐life worldwide harmonized light‐duty test cycle (WLTC). The model can precisely predict the capacity fade and the internal resistance growth with a root‐mean‐squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model can be used as an advanced online tool forecasting the lifetime based on dynamic profiles. [ABSTRACT FROM AUTHOR]

In this study, we present a modeling approach to estimate the combined effects of cyclable lithium loss and electrolyte depletion on the capacity and discharge power fades of lithium-ion batteries (LIBs). The LIB cell based on LiNi0.6Co0.2Mn0.2O2 (NCM622) was used to model the discharge behavior in the multiple degradation modes. The discharge voltages for nine different levels of cyclable lithium loss and electrolyte depletion were measured experimentally. When there was no cyclable lithium loss, the 50% of electrolyte depletion brought about 5% reduction in discharge capacity at 0.05 C discharge rate, while it resulted in 46% reduction when it was coupled with 30% of cyclable lithium loss. The 50% of electrolyte depletion with no cyclable lithium loss caused 1% reduction in discharge power during 0.5 C discharge at the state of charge (SOC) level of 0.8, while it resulted in 13% reduction when it was coupled with 30% of cyclable lithium loss. The modeling results obtained by using the one-dimensional finite element method were compared with the experimental data. The justification of the modeling methods is demonstrated by the high degree of concordance between the predicted and experimental values. Using the validated modeling methodology, the discharge capacity and usable discharge power can be estimated effectively under various combined degradation modes of cyclable lithium loss and electrolyte depletion in the LIB cell.

In this work, we aimed to reveal two main contributors to the capacity fade of a vanadium redox flow battery (VRFB). These contributors are the oxidative imbalance caused by the hydrogen evolution reaction (HER) competing with V3+ reduction during charging, and crossover, particularly the net transfer of vanadium ions from negolyte to posolyte. To investigate this, we performed VRFB cycling under various operation conditions with sequential monitoring of the electrolytes composition. We discovered that shortly after cycling starts, the crossover makes the negolyte capacity-limiting. This leads to high polarization of the negative electrode inducing HER and increasing average oxidation state (AOS) of the electrolyte. Our examination shows that the magnitude of the oxidative imbalance correlates with that of the crossover, both being dependent on the state-of-charge (SoC). Therefore, by changing operation conditions, we can slightly influence the crossover, while dramatically affect the oxidative imbalance. We also found that the resulting magnitude of the capacity fade is a trade-off between these two side-processes. This necessitates careful optimization of the battery and electrolyte composition, along with its cycling regime. From the data obtained, we conclude that the only proper approach to achieve lower capacity fade is by ensuring that the negolyte is capacity-limiting over long-term cycling. We hope that the data on the capacity fade mechanisms presented here will facilitate development of more stable and cost-effective VRFBs. [ABSTRACT FROM AUTHOR]

Demand for a high energy density in electric vehicles is increasing, and thus, high-Ni-content cathodes have recently been increasingly used. However, cyclic degradation is an issue in high-Ni Li-ion batteries, especially in battery packs. The packing pattern plays a key role in the energy degradation and thermal behaviour of the final battery products. In this study, the effects of preloading, current rate, and environment temperature on the capacity fading of Li-ion batteries are experimentally investigated, and the corresponding mechanisms are discussed. The experimental uncertainty of capacity fading, resistance, and swelling force are 9.4%, 8.9%, and 2.8%, respectively. The results show that, when the cell is tested at 45 °C, the capacity loss is approximately ∼4.2 times that at 25 °C after 720 cycles. The current rate also plays a key role in the life of the cell; the estimated life is 860 and 1942 cycles for 1 C and 1/3 C, respectively. The most important finding is that an increase in the preloading can lengthen the life of lithium-ion cells. When the preloading is varied from 0 N to 1000 N at intervals of 250 N, the cell life span is estimated to be 1942, 1678, 2063, 2083, and 2421 cycles, respectively. In addition, our battery has certain advantages in terms of capacity degradation such that the required raw materials and hazardous waste of the battery can be reduced. Finally, the module is tested to estimate its capacity and constrained force evolution. Thus, the results of this study can guide the development and design of cell packing patterns and further investigations in this field. [Display omitted] • Capacity fading of NMC/Graphite cell was studied considering environmental parameters and preloads. • Optimal operating temperature range of lithium-ion batteries is 20–40 °C whether under constraint or not. • Sudden rise of constraint force under 1 C did not immediately lead to sudden drop of cell's capacity. • Complexity of interactions between cells when combined in a module was revealed. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic prediction model for electrolyte capacity fade in vanadium redox flow batteries (VRFBs) is proposed. The capacity fade characteristics of VRFBs were analyzed quantitatively from both microscopic ions crossover and macroscopic electrolyte volumetric change perspectives. The dynamic behavior of vanadium ions (V2+, V3+, VO2+, and VO+ 2), protons, and water molecules in VRFB half-cells and electrolyte tanks has been thoroughly explored. The parameters related to the capacity fade were obtained from a self-discharge test and formation charge processes. The accuracy of the proposed model was experimentally validated using a unit cell (25 cm2) in a long-term operation. The results reveal that after 200 continuous charge/discharge cycles (duration of 739,257 s) with a current density of 80 mA cm2, the model can accurately predict changes in electrolyte volume and capacity fade with errors of 0.632 mL and 0.0295 Ah, respectively. The electrolyte capacity fade under various current densities for long-term operations has also been predicted by the model and experimentally validated. Under all current densities applied to the VRFB, the discrepancies between simulation and experimental results for electrolyte volume change and capacity fade were <8.46% and 4.99%, respectively. The proposed model significantly improves the accessibility and reliability of accurate capacity fade prediction for VRFBs, enhancing the competitiveness of VRFBs in energy storage applications. • Dynamic model of VRFB which is capable of predicting capacity fade was developed. • Electrolyte volume change of VRFB was evaluated by considering mass transfer, migration, and self-discharge phenomena. • Proposed model was validated with the various long-term experiments, which confirmed the high fidelity of the model. [ABSTRACT FROM AUTHOR]

To resolve the long sequence time-series forecasting problem of a battery, this study introduces the Gaussian process regression (GPR) model combined with the Informer model, namely I-GPR model. The Informer model is used to predict the future features of the battery capacity fade according to the experimental features, which are extracted through a user-defined convolution kernel from the actual voltage, current, temperature, and electrolyte flowrate of the battery. In the GRP model, the capacity fade, which is considered to follow a Gaussian distribution, is characterized as a joint Gaussian distribution. By expressing the distribution under the training datasets from the experimental features, the test datasets are obtained from the predicted features as the distribution input to regress the capacity fade with high fidelity at different cycle lengths and current densities. Compared with the experimental result, the proposed model revealed prediction average errors of 0.66%, 1.93%, 1.71% with the initial state of health (SOH) of 100%, and 0.50%, 1.58%, 2.01% with the initial SOH of 85%, and average root-mean-square error of 0.85%, 2.51%, 2.04% with the initial SOH of 100%, and 0.60%, 1.96%, 2.37% with the initial SOH of 85% at current densities of 80, 120, and 160 mA cm−2, respectively. • Novel VRFB feature extraction method based on convolution kernel is proposed. • Informer model for LSTF problem is developed to predict the battery features. • I-GPR model for VRFB capacity fade prediction is proposed. • Experimental results are consistent with capacity fade predictions. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is a promising technique for battery health estimation and prediction. However, with more and more types of batteries entering the market, building an ML model from scratch for each new battery requires collecting a large amount of data, which is very expensive and time-consuming. This paper proposes a transfer learning approach to reduce the amount of data that needs to be recollected for a new battery. The key idea is to train an ML model for a new battery of interest (i.e., target battery) with a limited amount of data by transferring the knowledge contained in a well-studied battery (i.e., source battery) with sufficient data. We illustrate this approach using two types of batteries, i.e., the battery with Li 0. 86 Ni 0. 86 Co 0. 11 Al 0. 03 O 2 -based positive electrode (NCA battery, source battery) and the battery with Li 0. 84 Ni 0. 83 Co 0. 11 Mn 0. 07 O 2 -based positive electrode (NCM battery, target battery), which have similar degradation patterns but dramatically different cycle life. Specifically, we first pre-train a long short-term memory (LSTM) network, using cycling data of 20 NCA cells at 25 °C and at 45 °C, to predict the following capacity fade based on the previous capacity sequence. Then, to make the model applicable to NCM cells, we employ the transfer learning method to retrain the model, using cycling data of only 2 NCM cells at 25 °C, and propose a two-stage approach to further improve the model performance. The proposed two-stage model can predict the cycle life of NCM cells at 45 °C using the capacities of the first 13 cycles and obtain a cycle life root-mean-squared-error (RMSE) of 25.23 cycles and a capacity trajectory RMSE of 17.80 mAh (0.51 %). • Experimental data of NCA and NCM batteries cycled at 25 °C and 45 °C is used. • An LSTM network is trained to predict the capacity fade for NCA batteries. • Transfer learning is employed to make the model applicable to NCM batteries. • A two-stage model is proposed to improve the model performance. • High accuracy of capacity fade and cycle life prediction is obtained. [ABSTRACT FROM AUTHOR]

• Implication of anode particle configuration on capacity fading is studied. • Interplay of anode particle configuration and particle geometry is investigated. • Interplay of anode particle configuration and tortuosity is discussed. • Non-uniform anode particle distribution enhances battery capacity. Capacity fading is one major problem that hinders predicting the state of health and optimal use of the Li-ion battery in various applications, viz., electric vehicles and consumer electronic devices. In this work, the importance of non-uniform anode microstructure and its implications on capacity fading characteristics are examined. The numerical study considers the effect of anode particle size distribution, geometry, and tortuosity on capacity fading at different current densities. Our investigation reveals that the capacity fade mechanism is the least when the particle size is small near the anode-separator interface. The study on the effect of the geometry of particles shows that capacity fading is the least when spherical particles are employed. A separate study on the effect of tortuosity reveals that capacity fading decreases with the decrease in tortuosity. Further, a substantial interaction between particle size distribution and microstructural characteristics is reported for the cycle. Additionally, the role of anode microstructure is evident in charging compared to discharging. Moreover, the fading characteristics are prominent at higher current densities. These study insights establish that the anode non-uniform microstructure affects the cell performance and capacity fading characteristics that can ultimately improve the reliability aspect and increase the value of the battery. [ABSTRACT FROM AUTHOR]

In this review, the state of the art of modified membranes developed and applied for the improved performance of redox flow batteries (RFBs) is presented and critically discussed. The review begins with an introduction to the energy-storing chemical principles and the potential of using RFBs in the energy transition in industrial and transport-related sectors. Commonly used membrane modification techniques are briefly presented and compared next. The recent progress in applying modified membranes in different RFB chemistries is then critically discussed. The relationship between a given membrane modification strategy, corresponding ex situ properties and their impact on battery performance are outlined. It has been demonstrated that further dedicated studies are necessary in order to develop an optimal modification technique, since a modification generally reduces the crossover of redox-active species but, at the same time, leads to an increase in membrane electrical resistance. The feasibility of using alternative advanced modification methods, similar to those employed in water purification applications, needs yet to be evaluated. Additionally, the long-term stability and durability of the modified membranes during cycling in RFBs still must be investigated. The remaining challenges and potential solutions, as well as promising future perspectives, are finally highlighted.

One of the major challenges in vanadium redox flow batteries (VRFB) is a gradual decrease of available capacity over operation time. The VRFB capacity fade is a complex issue that affects volume, total content, and average valence of vanadium ions in posolyte and negolyte. Imbalances that occur due to crossover of vanadium ions, osmosis and electroosmosis of water can be dealt with by intermittent mixing of posolyte and negolyte, thereby prolonging VRFB operation at high capacity rates. However, the change in the average oxidation state (AOS) should be considered for stable VRFB operation. The imbalance of vanadium valence arises due to side reactions during charge–discharge processes. This work proposes a novel operando method for restoration of the initial VRFB capacity. Partial posolyte reduction on the cathode and oxygen evolution reaction on the RuO 2 /Ti electrode of the electrolysis cell, following which electrolytes mix and recharge, allow to recover initial performance parameters. Optimizing electrolysis conditions ensures high energy efficiency of the rebalancing procedure. Coulometric sensors data provide the charge required for posolyte reduction. The proposed approach for VRFB capacity recovery allows for complete restoration of performance parameters regardless of the magnitude of a capacity drop. • Operando method for capacity recovery method was proposed. • Battery SOC is measured by coulometric sensors. • Capacity recovery method is based on AOS reduction. • AOS of posolyte is reduced by electrolysis cell with RuO 2 /Ti electrode. • Proposed method is efficient regardless of capacity drop and ensures stable VRFB operation. [ABSTRACT FROM AUTHOR]

• Capacity fade due to electrolyte degradation and activated carbon transformation. • Passivating degradation products on electrode surface identified. • Surface area and pore property of electrode deteriorated by degradation products. • Defects increased in activated carbon electrode during cycling. The capacity fade mechanism of activated carbon (AC) electrode in Li-ion electrolyte was studied via electrochemical impedance spectroscopy (EIS) and post-mortem electrode characterizations at different stages of electrochemical cycling. Electrochemical cycling was conducted in half cells incorporating the AC working electrode, Li metal counter electrode, and 1 M LiPF 6 in EC:DMC (1:1) electrolyte. Three phases were identified during the ageing process that corresponded with transformation of the passivation layer at the electrode surface and charge transfer impedance derived from the EIS analysis. Surface area and morphology analysis showed that the AC surface was progressively transformed by degradation products that reduced the available surface area and accessibility of electrolyte moieties into the pores. X-ray photoelectron spectroscopy suggested that the degradation products are from the LiPF 6 salt decomposition and carbonate solvent decomposition, while Raman analysis demonstrated increased defects in the electrode as cycling progressed. The capacity fade was therefore caused by the synergistic effect of electrolyte degradation and active material transformation. [Display omitted] [ABSTRACT FROM AUTHOR]