Batteries, Volume 10, Issue 6 (June 2024) – 36 articles

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring. Full article

Energy supply on high mountains remains an open issue since grid connection is not feasible. In the past, diesel generators with lead–acid battery energy storage systems (ESSs) were applied in most cases. Recently, photovoltaic (PV) systems with lithium-ion (Li-ion) battery ESSs have become suitable for solving this problem in a greener way. In 2016, an off-grid PV system with a Li-ion battery ESS was installed in Paiyun Lodge on Mt. Jade (the highest lodge in Taiwan). After operating for more than 7 years, the aging of the whole electric power system became a critical issue for its long-term usage. In this work, a method is established for analyzing the massive energy data (over 7 million rows), such as daily operation patterns, as well as the C-rate, temperature, and accumulated energy distributions, and estimating the health of the Li-ion battery system. A completed electric power improvement project dealing with power system aging is reported. Based on the long-term usage experience, a simple cost analysis model comparing lead–acid and Li-ion battery systems is built, revealing that expensive Li-ion batteries can compete with cheap lead–acid batteries for long-term usage on high mountains. This case study can provide engineers and researchers with a fundamental understanding of the long-term usage of off-grid PV ESSs and engineering on high mountains. Full article

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen. Full article

The rapid advancement of wearable devices and flexible electronics has spurred an increasing need for high-performance, thin, lightweight, and flexible energy storage devices. In particular, thin and lightweight zinc-ion batteries require battery materials that possess exceptional flexibility and mechanical stability to accommodate complex deformations often encountered in flexible device applications. Moreover, the development of compact and thin battery structures is essential to minimize the overall size and weight while maintaining excellent electrochemical performance, including high energy density, long cycle life, and stable charge/discharge characteristics, to ensure their versatility across various applications. Researchers have made significant strides in enhancing the battery’s performance by optimizing crucial components such as electrode materials, electrolytes, separators, and battery structure. This review provides a comprehensive analysis of the design principles essential for achieving thinness in zinc-ion batteries, along with a summary of the preparation methods and potential applications of these batteries. Moreover, it delves into the challenges associated with achieving thinness in zinc-ion batteries and proposes effective countermeasures to address these hurdles. This review concludes by offering insights into future developments in this field, underscoring the continual advancements and innovations that can be expected. Full article

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified systems, battery emulators are used with advanced mathematical models describing the expected voltage output with respect to current load. This paper conducted research into different modeling types: electrochemical, thermal, and electronic equivalent circuit models (EECMs). EECMs were identified as the most suitable to be used in conjunction with emulation techniques. A foundation EECM was created in conjunction with a thermal part to simulate thermal dependency. Hybrid Pulse Power Characterization (HPPC) tests were conducted on an NMC Li-ion cell across a range of temperatures from −20 °C to 25 °C. Using parameter optimization techniques, the HPPC test data were used to identify the resistance, capacitance, and the open-circuit voltage of the cell across a range of state of charge bounds and across a temperature range of 0 °C to 25 °C. The foundation model was assessed using identified parameters on two current profiles derived from drive cycles across a temperature range of 0 °C to 10 °C. The FMU (Functional Mockup Unit) model format was determined as the required interface for an AVL battery emulator. Full article

Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature. Full article

Polyoxometalates (POMs) with transition metals (Co, Cu, Fe, Mn, Ni) of Keggin structure and lamellar-stacked multi-layer morphology were synthesized. They were subsequently explored as bifunctional electrocatalysts for oxygen electrodes, i.e., oxygen reduction (ORR) and evolution (OER) reaction, for aqueous rechargeable metal-air batteries in alkaline media. The lowest Tafel slope (85 mV dec⁻¹) value and the highest OER current density of 93.8 mA cm⁻² were obtained for the Fe-POM electrocatalyst. Similar OER electrochemical catalytic activity was noticed for the Co-POM electrocatalyst. This behavior was confirmed by electrochemical impedance spectroscopy, where Fe-POM gave the lowest charge transfer resistance of 3.35 Ω, followed by Co-POM with R_ct of 15.04 Ω, during the OER. Additionally, Tafel slope values of 85 and 109 mV dec⁻¹ were calculated for Fe-POM and Co-POM, respectively, during the ORR. The ORR at Fe-POM proceeded by mixed two- and four-electron pathways, while ORR at Co-POM proceeded exclusively by the four-electron pathway. Finally, capacitance studies were conducted on the synthesized POMs. Full article

The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly. Full article

Graphene has a high specific surface area and high electrical conductivity, and its addition to activated carbon electrodes should theoretically significantly improve the energy storage performance of supercapacitors. Unfortunately, such an ideal outcome is seldom verified in practical commercial supercapacitor design and production. In this paper, the oxygen-containing functional groups in graphene/activated carbon hybrids, which are prone to induce side reactions, are removed in the material synthesis stage by a special process design, and electrodes with high densities and low internal resistances are prepared by a dry process. On this basis, a carbon-coated aluminum foil collector with a full tab structure is designed and assembled with graphene/activated carbon hybrid electrodes to form a commercial supercapacitor in cylindrical configuration. The experimental tests confirmed that such supercapacitors have high capacity density, power density, low internal resistance (about 0.06 mΩ), good high-current charging/discharging characteristics, and a long lifetime, with more than 80% capacity retention after 10 W cycles. Full article

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. Full article

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles to more dynamic profiles constructed to mimic real operation when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end of life. Using data from batteries aged in this fashion to create a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to train models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only one randomly sampled partial charge is needed to achieve errors smaller than 1%. Lastly, as the number of randomly sampled partial charges used to train the model increases, the dependence on particular partial charges tends to decrease. Full article

Potassium–oxygen batteries (KOBs) are a promising energy storage technology with high theoretical energy density, low overpotential and a long cycle life. The cathode microstructure plays a significant role in the electrochemical performance of KOB. In this article, hierarchical porosity was introduced to commercially available carbon paper cathodes by thermal pretreatment in air at different pretreatment times. This pretreatment modifies the properties, such as surface area, defects, oxygen functional groups, etc. The discharge performance was determined at three different current densities, i.e., 0.1 mA/cm², 0.5 mA/cm², and 1.0 mA/cm². It has been found that an increase in specific surface area with the introduction of micropores and mesopores is beneficial for the improvement in the discharge capacity by enabling homogeneous discharge product, KO₂ distribution and high degrees of pore filling over the volume of the cathode. A reduction in the discharge overpotentials was observed, which is attributed to the introduction of oxygenic functional groups and defects. Samples treated for the longest pretreatment time of 24 h showed the highest discharge capacity of 5 mAh/cm² and lowest discharge overpotential of 0.03 V. Full article

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry. Full article

The continuing increase in pollutant emissions requires the use of alternative power sources. This includes the use of electric or hybrid vehicles whose energy storage system is based on batteries of various types, including lithium-ion batteries. The optimum operating temperature is between 15 °C and 35 °C. Too high temperatures can lead to catastrophic phenomena such as thermal runaway. The thermal gradient within the system should not exceed 5 °C. An effective Battery Thermal Management System can mitigate this problem. This study analysed a lithium-ion battery with a bag structure. Temperature control was evaluated using a passive (low-cost) system with phase-change materials (PCMs). The material chosen was n-octadecane (paraffin) due to its thermophysical properties and market price. Four different cooling methods were analysed, including air, fins, pure PCM, and a mixed system of single cells and small battery packs. The results show that an undesirable temperature peak around 50 °C (323.15 K) can occur at hot spots. The best system for containing the temperature inside the battery pack is the PCM cooling system with fins. The optimum fin thickness is 1.5 mm. To contain the temperature inside the battery pack, the number of fins studied is 10, while the best temperature containment is achieved with n+ 1 plates, where n is the number of cells. Full article

Sluggish transfer kinetics caused by solid–solid contact at the lithium (Li)/solid-state electrolyte (SE) interface is an inherent drawback of all-solid-state Li metal batteries (ASSLMBs) that not only limits the cell power density but also induces uneven Li deposition as well as high levels of interfacial stress that deteriorates the internal structure and cycling stability of ASSLMBs. Herein, a fast Li⁺ transfer scaffold is proposed to overcome the sluggish kinetics at the Li/SE interface in ASSLMBs using an α-MnO₂-decorated carbon paper (CP) structure (α-MnO₂@CP). At an atomic scale, the tunnel structure of α-MnO₂ exhibits a great ability to facilitate Li⁺ adsorption and transportation across the inter-structure of α-MnO₂@CP, leading to a high critical current density of 3.95 mA cm⁻² at the Li/SE interface. Meanwhile, uniform Li deposition can be guided along the skeletons of α-MnO₂@CP with minimized volume expansion, significantly improving the structural stability of the Li/SE interface. Based on these advantages, the ASSLMBs using α-MnO₂@CP protected the Li anode and can stably cycle up to very high charge/discharge rates of 10C/10C, paving the way for developing high-power ASSLMBs. Full article

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica. Full article

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data. Full article

This paper presents an experimental comparison of two types of Li-ion battery stacks for low-voltage energy storage in small urban Electric or Hybrid Electric Vehicles (EVs/HEVs). These systems are a combination of lithium battery cells, a battery management system (BMS), and a central control circuit—a lithium energy storage and management system (LESMS). Li-Ion cells are assembled with two different active cathode materials, nickel–cobalt–aluminum (NCA) and lithium iron phosphate (LFP), both with an integrated decentralized BMS. Based on experiments conducted on the two assembled LESMSs, this paper suggests that although LFP batteries have inferior characteristics in terms of energy and power density, they have great capacity for improvement. Full article

Monitoring battery aging is crucial for maintaining reliability and performance. This study investigates Frequency Domain Reflectometry (FDR) as a tool for monitoring lithium-ion battery State-of-Health (SoH). While FDR has been applied in battery research, the existing literature fails to address SoH assessment and lacks studies on larger battery samples to provide more meaningful results. In this work, nineteen cells initially underwent Electrochemical Impedance Spectroscopy (EIS) to assess their degradation levels during cyclic aging. This work evaluates FDR’s effectiveness in monitoring battery health indicators, such as capacity and equivalent series resistance (ESR), by correlating these with FDR-measured impedance between 300 kHz and 1 GHz. Analytical comparison between impedance measured before and after de-embedding processes were presented. The results show FDR reactance within 300 kHz–40 MHz correlates with EIS-measured ESR, suggesting its potential as a SoH indicator. However, reduced sensitivity and accuracy, particularly after de-embedding, may limit practical applicability. Additionally, resonance-based analysis was conducted to explore the relationship between changes in circuit resonance and cell dielectric permittivity. Despite having the lowest sensitivity, the method showed that the resonance frequencies of cells remain relatively constant, mirroring behaviours associated with changes in resistive properties. Overall, this study provides insights into FDR’s potential for battery diagnostics while highlighting avenues for future research to enhance effectiveness in real-world scenarios. Full article

Batteries with an energy storage capacity of 280 Ah play a crucial role in promoting the development of smart grids. However, the inhomogeneity of their internal temperature cannot be accurately measured at different constant charge and discharge power, affecting the efficiency and safety of the battery. This work adopts finite element analysis to determine the typical internal temperature of a single-cell model, which can guide the measuring position of the battery. Before the manufacturing process, a slim pre-buried sensor is utilized to reduce the negative impacts of different constant charge and discharge powers. The maximum internal temperature of the battery is up to 77 °C at a constant charge and discharge power of 896 W. The temperature difference between the two poles and the battery surface is as high as 26.2 °C, which is beyond the safety temperature (55 °C). This phenomenon will result in the degradation of the positive electrode through dQ/dV curves. These measurements of battery internal temperature can improve battery heat control and facilitate the development of energy storage technology. Full article

All-solid-state battery (ASSB) technology is a new energy system that reduces the safety concerns and improves the battery performance of conventional lithium-ion batteries (LIB). The increasing demand for such new energy systems makes the transition from laboratory scale production of ASSB components to larger scale essential. Therefore, this study investigates the dry extrusion of poly(ethylene oxide):lithium bis (trifluoromethylsulfonyl)imide (PEO:LiTFSI) all-solid-state electrolytes at a ratio of 20:1 (EO:Li). We investigated the influence of different extruder setups on the product quality. For this purpose, different screw designs consisting of conveying, kneading and mixing elements are evaluated. To do so, a completely dry and solvent-free production of PEO:LiTFSI electrolytes using a co-rotating, intermeshing, twin-screw extruder under an inert condition was successfully carried out. The experiments showed that the screw design consisting of kneading elements gives the best results in terms of process stability and homogeneous mixing of the electrolyte components. Electrochemical impedance spectroscopy was used to determine the lithium-ion conductivity. All electrolytes produced had an ionic conductivity (σ_ionic) of (1.1–1.8) × 10⁻⁴ S cm⁻¹ at 80 °C. Full article

With the widespread adoption of battery technology in electric vehicles, there has been significant attention drawn to the increasing frequency of battery fire incidents. However, the jetting behavior and expansion force during the thermal runaway (TR) of batteries represent highly dynamic phenomena, which lack comprehensive quantitative description. This study addresses this gap by employing an enhanced experimental setup that synchronizes the video timing of cameras with a signal acquisition system, enabling the multidimensional quantification of signals, such as images, temperature, voltage, and pressure. It also provides a detailed description of the jetting behavior and expansion force characteristics over time for Li(Ni_0.8Co_0.1Mn_0.1)O₂ batteries undergoing thermal runaway in an open environment. The results from three experiments effectively identify key temporal features, including the timing of the initial jetting spark, maximum jetting velocity, jetting duration, explosion duration, and patterns of flame volume variation. This quantitative analytical approach proves effective across various battery types and conditions. The findings could offer scientific foundations and experimental strategies for parameter identification in fire prevention and thermal runaway model development. Full article

Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation. Full article

Asymmetric lithium salts, such as lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li⁺ conductivity and the capacity to generate a stable solid electrolyte interphase (SEI) while maintaining compatibility with an aluminum (Al⁰) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, detailed SEI reactions of LiDFTFSI–based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of the SEI. Our results show that asymmetric DFTFSI⁻ weakens the interaction between carbonate solvents and Li⁺, and substantially alters the solvation structure, exhibiting a well-balanced coordination capacity compared to bis(trifluoromethanesulfonyl)imide (TFSI⁻). Nanosecond hybrid molecular dynamics simulation further reveals that preferential decomposition of LiDFTFSI produces sufficient LiF and Li₂O to facilitate a robust SEI. Moreover, abundant F⁻ generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al³⁺ from the current collector to form AlF₃, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collectors. Full article

With the rapid development of new energy vehicles and energy storage industries, the demand for lithium-ion batteries has surged, and the number of spent LIBs has also increased. Therefore, a new method for lithium selective extraction from spent lithium-ion battery cathode materials is proposed, aiming at more efficient recovery of valuable metals. The acid + oxidant leaching system was proposed for spent ternary positive electrode materials, which can achieve the selective and efficient extraction of lithium. In this study, 0.1 mol L⁻¹ H₂SO₄ and 0.2 mol L⁻¹ (NH₄)₂S₂O₈ were used as leaching acid and oxidant. The leaching efficiencies of Li, Ni, Co, and Mn were 98.7, 30, 3.5, and 0.1%, respectively. The lithium solution was obtained by adjusting the pH of the solution. Thermodynamic and kinetic studies of the lithium leaching process revealed that the apparent activation energy of the lithium leaching process is 46 kJ mol⁻¹ and the rate step is the chemical reaction process. The leaching residue can be used as a ternary precursor to prepare regenerated positive electrode materials by solid-phase sintering. Electrochemical tests of the regenerated material proved that the material has good electrochemical properties. The highest discharge capacity exceeds 150 mAh g⁻¹ at 0.2 C, and the capacity retention rate after 100 cycles exceeds 90%. The proposed new method can extract lithium from the ternary material with high selectivity and high efficiency, reducing its loss in the lengthy process. Lithium replenishment of the delithiation material can also restore its activity and realize the comprehensive utilization of elements such as nickel, cobalt, and manganese. The method combines the lithium recovery process and the material preparation process, simplifying the process and saving costs, thus providing new ideas for future method development. Full article

Aqueous zinc metal batteries (AZMBs) are considered a promising candidate for grid-scale energy storage systems owing to their high capacity, high safety and low cost. However, Zn anodes suffer from notorious dendrite growth and undesirable surface corrosion, severely hindering the commercialization of AZMBs. Herein, a strategy for engineering a dense ZnO coating layer on Zn anodes using the atomic layer deposition (ALD) technique is developed, aiming to improve its long-term cycling stability with fewer Zn dendrites. The surface-modified Zn anode (ZnO@Zn) exhibits an excellent long-cycling life (680 h) and stable coulombic efficiency when being used in a symmetric cell. Moreover, the ZnO@Zn electrode shows a high stability with almost no capacity decay after 1100 cycles at 2C in a full cell using MnO₂ as the cathode. The ZnO coating is conducive to reducing corrosion and the generation of by-products, thus increasing the reversibility of Zn²⁺/Zn stripping/plating. Particularly, density functional theory (DFT) calculation results reveal that the ZnO coating layer could effectively lower the adsorption energy of the Zn(002) plane in ZnO@Zn, inducing the preferential deposition of Zn²⁺ towards the (002) crystal plane with fewer Zn dendrites. The surface ZnO coating protocol provides a promising approach to achieve a dendrite-free Zn anode for stable AZMBs. Full article

The concept of pseudocapacitance is explored as a rapid and universal method for the state of health (SOH) determination of batteries and supercapacitors. In contrast to this, the state of the art considers the degradation of a series of full charge/discharge cycles. Lithium-ion batteries, sodium-ion batteries and supercapacitors of different cell chemistries are studied by impedance spectroscopy during lifetime testing. Faradaic and capacitive charge storage are distinguished by the relationship between the stored electric charge and capacitance. Batteries with a flat voltage–charge curve are best suited for impedance spectroscopy. There is a slight loss in the linear correlation between the pseudocapacitance and Ah capacity in regions of overcharge and deep discharge. The correct calculation of quantities related to complex impedance and differential capacitance is outlined, which may also be useful as an introductory text and tutorial for newcomers to the field. Novel diagram types are proposed for the purpose of the instant performance and failure diagnosis of batteries and supercapacitors. Full article

The release of flammable gases during battery thermal runaway poses a risk of combustion and explosion, endangering personnel safety. The convective and diffusive properties of the gas make it challenging to accurately measure gas state, complicating the assessment of the battery pack exhaust design. In this paper, a thermal resistance network model is established, which is used to calculate the battery thermal runaway propagation. Gas accumulation after thermal runaway venting of a LiFeO₄ module is studied using ANSYS Fluent under different venting schemes. The results show that the scheme of battery inversion and simultaneous exhaust from the side and bottom of the module is optimal. The methods and results presented can guide the design of LiFeO₄ cell pack runners. Full article

Vanadium redox flow batteries are promising energy storage devices and are already ahead of lead–acid batteries in terms of installed capacity in energy systems due to their long service life and possibility of recycling. One of the crucial tasks today is the development of models for assessing battery performance and its residual resource based on the battery’s present state. A promising method for estimating battery capacity is based on analyzing present voltage and current values under various load conditions. This paper analyzes the discharge characteristics of a 10 kW all-vanadium redox flow battery at fixed load powers from 6 to 12 kW. A linear dependence of operating voltage and initial discharge voltage on load power is established. It is also determined that the slope of the discharge curve linear section does not increase linearly in absolute value, and the Box–Lucas model can be used to describe it. Models for predicting current VRFB capacity based on different curve fitting functions are proposed. These models can be used to roughly estimate battery capacity at different load powers. Full article