Battery Behavior Research Articles

Understanding ultrafast rechargeable Al/graphite battery by visualizing phase separation

Al/graphite batteries (ABs) using ionic liquid electrolytes exhibit exceptionally fast charging and cycling stability. However, the mechanisms underlying their high rate capabilities remains elusive. In this study, in situ optical microscopy is employed to investigate the intercalation dynamics of single-flake graphite in ABs. Observations reveal that surface reaction limitations, rather than AlCl4− mass transfer, primarily govern performance in the graphite cathode. During charging under varying current densities, the ABs display distinct phase separation behaviour with an intercalation wave morphology, indicating that surface reactions restrict the intercalation process. This finding explains the ultrafast recharge capability of ABs, where active sites in graphite become nearly fully intercalated with AlCl4− at high current densities. Additionally, slight rate performance loss occurs due to increasing ohmic and charge transfer polarisation (ηohm and ηct) at higher current densities. To address this limitation, we propose increasing the cut-off voltage as a straightforward and effective method to mitigate these polarization effects. This study offers valuable insights into the electrochemical behaviour of rechargeable secondary ion batteries by visualising their phase separation.

Mitigating Intraphase Catalytic‐Domain Transfer via CO2 Laser for Enhanced Nitrate‐to‐Ammonia Electroconversion and Zn‐Nitrate Battery Behavior

AbstractDeveloping sustainable energy solutions is critical for addressing the dual challenges of energy demand and environmental impact. In this study, a zinc‐nitrate (Zn−NO3−) battery system was designed for the simultaneous production of ammonia (NH3) via the electrocatalytic NO3− reduction reaction (NO3RR) and electricity generation. Continuous wave CO2 laser irradiation yielded precisely controlled CoFe2O4@nitrogen‐doped carbon (CoFe2O4@NC) hollow nanocubes from CoFe Prussian blue analogs (CoFe‐PBA) as the integral electrocatalyst for NO3RR in 1.0 M KOH, achieving a remarkable NH4+ production rate of 10.9 mg h−1 cm−2 at −0.47 V versus Reversible Hydrogen Electrode with exceptional stability. In situ and ex situ methods revealed that the CoFe2O4@NC surface transformed into high‐valent Fe/CoOOH active species, optimizing the adsorption energy of NO3RR (*NO2 and *NO species) intermediates. Furthermore, density functional theory calculations validated the possible NO3RR pathway on CoFe2O4@NC starting with NO3− conversion to *NO2 intermediates, followed by reduction to *NO. Subsequent protonation forms the *NH and *NH2 species, leading to NH3 formation via final protonation. The Zn−NO3− battery utilizing the CoFe2O4@NC cathode exhibits dual functionality by generating electricity with a stable open‐circuit voltage of 1.38 V versus Zn/Zn2+ and producing NH3. This study highlights the innovative use of CO2 laser irradiation to transform Prussian blue analogs into cost‐effective catalysts with hierarchical structures for NO3RR‐to‐NH3 conversion, positioning the Zn−NO3− battery as a promising technology for industrial applications.

Mitigating Intraphase Catalytic-Domain Transfer via CO2 Laser for Enhanced Nitrate-to-Ammonia Electroconversion and Zn-Nitrate Battery Behavior.

Developing sustainable energy solutions is critical for addressing the dual challenges of energy demand and environmental impact. In this study, a zinc-nitrate (Zn-NO3 -) battery system was designed for the simultaneous production of ammonia (NH3) via the electrocatalytic NO3 - reduction reaction (NO3RR) and electricity generation. Continuous wave CO2 laser irradiation yielded precisely controlled CoFe2O4@nitrogen-doped carbon (CoFe2O4@NC) hollow nanocubes from CoFe Prussian blue analogs (CoFe-PBA) as the integral electrocatalyst for NO3RR in 1.0 M KOH, achieving a remarkable NH4 + production rate of 10.9 mg h-1 cm-2 at -0.47 V versus Reversible Hydrogen Electrode with exceptional stability. In situ and ex situ methods revealed that the CoFe2O4@NC surface transformed into high-valent Fe/CoOOH active species, optimizing the adsorption energy of NO3RR (*NO2 and *NO species) intermediates. Furthermore, density functional theory calculations validated the possible NO3RR pathway on CoFe2O4@NC starting with NO3 - conversion to *NO2 intermediates, followed by reduction to *NO. Subsequent protonation forms the *NH and *NH2 species, leading to NH3 formation via final protonation. The Zn-NO3 - battery utilizing the CoFe2O4@NC cathode exhibits dual functionality by generating electricity with a stable open-circuit voltage of 1.38 V versus Zn/Zn2+ and producing NH3. This study highlights the innovative use of CO2 laser irradiation to transform Prussian blue analogs into cost-effective catalysts with hierarchical structures for NO3RR-to-NH3 conversion, positioning the Zn-NO3 - battery as a promising technology for industrial applications.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Numerical study on air-cooled battery thermal management system considering the sheer altitude effect

Due to energy shortage and environmental pollution, vehicles and aircraft powered by Li-ion batteries have now received widespread attention. Among various types of battery thermal management systems (BTMSs), the air-cooled BTMS is still the preferred choice due to its affordability, longevity, and simplicity. To ensure the reliable operation of electric vehicles and aircraft at different altitudes, it is extremely meaningful and significant to study the thermal behavior of batteries at different altitudes. Therefore, for the investigation of altitude impact, this paper firstly proposes an indirect decoupling method to address the limitations of using ambient temperature as a single variable. Based on this, several different configurations of air-cooled BTMS have been investigated through numerical simulation. Then, for the tapering-type BTMS with the best thermal performance, the battery behavior at different altitudes is investigated and the influence law of sheer altitude factor is summarized. Subsequently, to address the thermal performance issues at higher altitudes, this research proposes three optimization measures, which include increasing inlet velocity, decreasing inlet temperature, and incorporating phase change material (PCM) layers, respectively. Ultimately, the entropy weight-TOPSIS method is adopted to seek the optimal measure at different parameters. The results indicate that as altitude increases from the standard altitude to 4000 m, the maximum temperature rises significantly, exceeding the permissible temperature range of Li-ion batteries. Besides, in order to effectively confine the maximum temperature within the permissible temperature range at an altitude of 4000 m, the inlet velocity should be increased by at least 2 m/s or the inlet temperature should be reduced by at least 3 °C. Among all optimized solutions, the solution with the addition of 0.4 mm PCM layers is the best based on the comprehensive evaluation of multiple indicators.

Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage

Minor deformation damage poses a concealed threat to battery performance and safety. This study delves into the progressive degradation behavior and mechanisms of lithium-ion batteries under minor deformation damage induced by out-of-plane compression. The effects of varying initial states of charge and loading speed on battery degradation are also analyzed. It has been observed that a deformation damage degree as low as 3.1 % can cause a significant transient capacity reduction up to 6.3 %. More transient capacity reduction can occur at higher initial state of charges and loading speed. Additionally, the investigation reveals that the capacity decay rate between the normal cell and cells experiencing deformation damage degree of <4.7 % is almost similar, with a maximum difference of merely 1.887 mAh/cycle and a minimum difference of 0.001 mAh/cycle, both observed over 3000 cycles. Non-destructive and destructive analysis methods are used to investigate degradation behaviors of the cells experiencing minor deformation dagame. It is found that such progressive degradation is primarily driven by the loss of active lithium ions, followed by the loss of cathode and anode active materials. It is anticipated that the findings will offer theoretical underpinnings for advancements in battery damage assessment, early failure prediction, and facilitation of loss adjustment and compensation by insurance agencies.

Tailoring Li-ion transport in hybrid biopolymer electrolyte from Maydis stigma for supercapattery applications

Corn Silk Biopolymer (CSBP), a potent biomaterial shed light on offering a sustainable solution to agrowaste and achieve waste valorization. Hybrid Biopolymer Electrolyte (HBPE) was synthesized using corn silk extract by simple solution casting strategy. Freshly synthesized [30wt % CSBP+70 wt % PVDF-Co-HFP] system exemplifies conductivity of 2.35 × 10−8 Scm−1 and while loading 3.2 v/v % LiClO4 salt, the conductivity increased to 4.05 × 10−4 Scm−1 at 25 °C. Structural studies indicate complex formation between the Li+ and polar groups within polymer-salt system. From FTIR studies, prominent peaks noticed at 1651 and 1628 cm−1 are due to the interaction of –C=O group with Li+ and 625 cm−1 for unbound ClO4−.Our best conducting system reveal Li+ ions (tion = 0.98) are the major charge carriers. Electrochemical behaviour from CV demonstrates a good reversibility with a specific capacity (Qs = 25.6 Cg-1) and specific capacitance (Csp = 10.67 Fg-1) at a scan rate of 1 mV s−1. Also, the relation between the peak current with different scan rates for CPL 2 system is estimated from the slope as b = 0.7 suggesting the extraordinary behavior of a hybrid battery and super capacitor offering a unique platform for the sustainable energy storage technology.

Development of a Non-Linear Battery Management System Model for Lithium-Ion Batteries Using MATLAB Simulation

Abstract: This research focuses on developing a comprehensive battery model tailored for Battery Management Systems (BMS) using lithium-ion batteries. Given the increasing reliance on lithium-ion batteries in electric vehicles and portable electronic devices, this study aims to construct a non-linear battery model that accurately reflects the charging and discharging characteristics under various load conditions. The research identifies suitable characteristics of lithium-ion batteries essential for effective battery model construction. A MATLAB-Simulink approach is employed to build the model, which integrates both charging and discharging processes into a single simulation framework. This merged model enhances the existing non-linear models by offering improved accuracy in simulating battery behavior under fixed parameters. The outcome is expected to provide a reference model for future studies and applications in power management systems. Additionally, this research underscores the critical role of BMS in extending battery life and ensuring safety, making it a significant contribution to the field of energy management. The study's findings highlight the potential of the proposed model to serve as a foundation for further research and development in battery technologies.

Modeling state-of-charge dependent mechanical response of lithium-ion batteries with volume expansion

The behavior of lithium-ion batteries under mechanical abuse conditions has been studied extensively in recent years. Typically, characterization is performed by conducting experiments on discharged batteries to minimize the chances of thermal runaway and fire. However, studies have shown that material models calibrated at discharged state may not be as accurate at higher states of charge. In this study, a combined experimental-numerical method is proposed to simulate the stiffening of mechanical response in electrode stacks/jellyrolls of lithium-ion batteries at various states of charge and levels of confinement. Our study indicates that the mechanical properties of the jellyroll do not inherently change as a function of the state of charge, rather, the primary cause of the higher stiffness in charged batteries is the volume expansion of the jellyroll during charging. Therefore, mechanical characterization tests are conducted at the discharged state and the expansion of the jellyroll is induced in the finite element models to match the equivalent volume at the desired state of charge. In the second phase of the simulation, mechanical abuse loading is applied to validate the accuracy of simulations using the test data. This method was applied and validated for both pouch and cylindrical batteries with high accuracy.

Adaptive optimal observer for real-time state of charge estimation of lithium-ion batteries in robotic systems

Purpose This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan. Design/methodology/approach The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation. Findings The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates. Originality/value This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

Inside Front Cover: Mitigating Intraphase Catalytic‐Domain Transfer via CO2 Laser for Enhanced Nitrate‐to‐Ammonia Electroconversion and Zn‐Nitrate Battery Behavior

Inside Front Cover: Mitigating Intraphase Catalytic‐Domain Transfer via CO₂ Laser for Enhanced Nitrate‐to‐Ammonia Electroconversion and Zn‐Nitrate Battery Behavior

Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development

Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.

Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion (MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and extraction. Tested on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and reliability. Its robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

Study on the influence of high rate charge and discharge on thermal runaway behavior of lithium-ion battery

With the development of the new energy industry, battery life and rapid charge-discharge capacity have attracted much attention. At the same time, the high temperature inside the cell during high-rate charging and discharging may increase the probability of the battery thermal runaway. This paper studied the thermal runaway reaction of Li-ion batteries under different state of charge (SOC) and charge rates using a self-made experimental platform. The experimental phenomena and the changes in the temperature field were recorded. The key parameters, such as trigger temperature (T1, Lithium battery back thermal runaway triggers temperature), maximum temperature (Tmax),voltage, and mass loss (ML) of thermal runaway, were measured. The morphology changes of electrode materials, the battery remains, and the dynamics of thermal runaway reaction after high rate charge and discharge were further analyzed. The results show that for the 4 C-100 % battery, the T1 and Ea are reduced by 22.6 ℃ and 82.2 %, and the Tmax and maximum mass loss rate (MLRmax) are increased by 218.14 ℃ and five times, compared with the 1 C-50 % battery. With the increase of charge-discharge rate, the thermal stability of the battery decreases, and the gravity degree of accident increases.

A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941.

Parameterization and heat generation investigation of cylindrical lithium batteries based on a reconstructed electrochemical-thermal coupling model

To comprehensively investigate the electrochemical and thermal behaviors of cylindrical lithium-ion batteries (LIBs), an appropriate reconstructed electrochemical-thermal coupling model (RETM) is first established to parameterize the LIBs, and the simulation differences of different geometric configurations are quantitatively studied from two perspectives as temperature and voltage. Based on this RETM, a comprehensive method to obtain battery information by scanning electron microscopy, actual measurement and parameter estimation algorithm is further proposed and analyzed. Additionally, the heat generation rate (HGR) and the heat distributions of the cylindrical battery are carefully explored and a novel HGR verification method of the RETM is proposed and illustrated. Finally, the simulation and experimental results validate the superiority and effectiveness of the proposed RETM and HGR analysis method. This work could help other researchers predict the electrochemical and thermal behavior of batteries under different applicable conditions more effectively.

Failure mechanism and thermal runaway behavior of lithium-ion battery induced by arc faults

As the widespread of lithium-ion battery systems such as electric vehicles and energy storage systems, the number of safety incidents due to electrical faults are increasing. Many accident reports have demonstrated that arc faults have become one of the main triggers of LIB system accidents, however, the related studies are inadequate. In this study, an arc imitation system is employed to investigate the influence of different arc energies on battery safety valve, as well as the electrochemical characteristics of faulty batteries. The results show that the minimum arc power to breach the safety valve ranges from 110 to 441 W. The maximum temperature rise rate on the battery surface can exceed 15 °C/s with arc power of around 1000 W. Further, the testing of in-situ and ex-situ indicate the faulty batteries undergo degradation and failure due to that moisture in the air enters the battery interior, resulting in increased internal resistance, loss of active materials and cyclable lithium. Finally, the faulty battery has no valve opening during thermal runaway, and the ignition time is four hundred seconds earlier than that of the normal battery, indicating more severe fire dangers. The results are valuable for safety design of battery systems in relation to arc faults, as well as the characteristic for fault detection and early warning.

Fluorinated ester additive to regulate nucleation behavior and interfacial chemistry of room temperature sodium-sulfur batteries

Room temperature sodium-sulfur (RT Na-S) batteries are considered as advanced energy storage technology due to their low cost and high theoretical energy density. However, challenges such as the growth of sodium dendrite and dissolution of sodium polysulfides significantly hinder the electrochemical performance. Herein, we developed a propylene carbonate (PC)-based electrolyte with Methyl 2-Fluoroisobutyrate (MFB) as an additive. The ester group in the MFB additive is capable of participating in and reconfiguring the coordination of their Na+ solvated structures, thereby lowering the desolvation barrier and regulating the Na anode’s interfacial reaction and nucleation behavior. The polar C−F bond at the other end helps to reduce the lowest unoccupied molecular orbital (LUMO) energy of the MFB additive, enabling the preferential decomposition of MFB to form the F-rich inorganic phase strong polar solid electrolyte interphase (SEI), contributing to the inhibition of Na dendrite growth, the accumulation of dead Na. In addition, NaF-riched cathode electrolyte interphase (CEI) was also observed on sulfur-based cathode, which can effectively inhibited the shuttle effect. Consequently, the developed RT Na-S battery exhibit excellent electrochemical performance.

An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles

Predicting the state of power batteries is crucial for ensuring the safe and reliable operation of electric vehicles. However, accurately forecasting multiple battery parameters under full operating conditions remains challenging. Traditional prediction models struggle to accurately represent the actual battery behavior due to irregular vehicle driving patterns. Furthermore, long-term prediction of battery states, essential for potential fault diagnosis, poses significant difficulties for conventional neural networks. To address the issue of error accumulation in recurrent neural networks (RNNs) during multi-parameter battery prediction across various operating conditions, we propose a combined model featuring multi-level feature extraction. This model integrates Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks, where the LSTM layer employs forget gates to filter out unnecessary data while preserving valuable information. The bidirectional update gate in the BiGRU layer effectively combines historical and future data to enhance time series modeling. This comprehensive approach improves sequence modeling efficiency, thereby increasing reliability. However, the multi-level feature extraction structure introduces higher-dimensional hyperparameters, complicating the optimization process. To maintain prediction accuracy across multiple battery parameters, we propose an improved moth-flame optimization (IMFO) algorithm to optimize the complex hyperparameters of the LSTM_BiGRU model. Extensive experiments conducted on a real-world vehicle dataset demonstrate that the IMFO-LSTM_BiGRU combined network surpasses existing state-of-the-art methods in terms of accuracy and stability.

Growth of Ni-Ga oxalate nanoparticles on 3D current collector as a supercapacitive electrode

Metal oxalate materials have been explored for electrochemical energy storage (EES) applications. Herein, Ni-incorporated gallium oxalate (Ni-Ga-C2O4) based binder-free electrode was fabricated through a facile process. The electrode gathers the benefit of Ni foam contributed as a conductive substrate and also acting as a Ni source with binder-free approach. The Ni-Ga-C2O4 crystalline phase formation was confirmed through X-ray diffraction spectroscopy (XRD). The morphological properties and elemnetal quantification were studied via scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and transmission electron spectroscopy (TEM). Importantly, electrochemical energy storage investigation revealed the Ni-Ga-C2O4 electrode exhibits battery behavior with capacity retention of 85.5 % and possessed the highest specific capacity of 319 C g−1 at 1 A g−1. Overall, the additional contribution of Ni during the synthesis has boosted the physicochemical properties and electrochemical energy storage performance of the Ni-Ga-C2O4 electrode.