Batteries, Volume 10, Issue 3 (March 2024) – 45 articles

Effectively extracting a lithium-ion battery’s impedance is of great importance for various onboard applications, which requires consideration of both the time consumption and accuracy of the measurement process. Although the pseudorandom binary sequence (PRBS) excitation signal can inject the superposition frequencies with high time efficiency and an easily implementable device, processing the data of the battery’s impedance measurement is still a challenge at present. This study proposes a fast impedance spectrum construction method for lithium-ion batteries, where a multi-density clustering algorithm was designed to effectively extract the useful impedance after PRBS injection. According to the distribution properties of the measurement points by PRBS, a density-based spatial clustering of applications with noise (DBSCAN) was used for processing the data of the lithium-ion battery’s impedance. The two key parameters of the DBSCAN were adjusted by a delicate workflow according to the frequency range. The validation of the proposed method was proved on a 3 Ah lithium-ion battery under nine different test conditions, considering both the SOC and temperature variations. Full article

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications. Full article

To stabilize the Earth’s climate, large-scale transition is needed to non-carbon-emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency makes them unable to supply a constant load without storage. To address these challenges, rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance, 3D printing technology has emerged as a promising solution. This literature review provides state-of-the-art enhancements of battery properties with 3D printing, including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation, and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing battery components as well as full batteries offer design flexibility, geometric freedom, and material flexibility, reduce pack weight, minimize material waste, increase the range of applications, and have the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising. Full article

The improvement of fast-charging capabilities for lithium-ion batteries significantly influences the widespread application of electric vehicles. Fast-charging performance depends not only on materials but also on the battery’s inherent structure and the heterogeneity of the electrode reaction. Herein, we utilized advanced imaging techniques to explore how the internal structure of cylindrical batteries impacts macroscopic electrochemical performance. Our research unveiled the natural 3D structural non-uniformity of the electrodes, causing heterogeneity of electrode reaction. This non-uniformity of reaction exhibited a macro–meso–micro-scale feature in four dimensions: the exterior versus the interior of the electrode, the middle versus the sides of the cell, the inside versus the outside of the cell, and the surface versus the body of the electrode. Furthermore, the single-coated side of the anode demonstrated notably faster reaction than the double-coated sides, leading to the deposition of island-like lithium during fast charging. These discoveries offer novel insights into multi-scale fast-charging mechanisms for commercial batteries, inspiring innovative approaches to battery design. Full article

In vanadium redox flow batteries, the flow field geometry plays a dramatic role on the distribution of the electrolyte and its design results from the trade-off between high battery performance and low pressure drops. In the literature, it was demonstrated that electrolyte permeation through the porous electrode is mainly regulated by pressure difference between adjacent channels, leading to the presence of under-the-rib fluxes. With the support of a 3D computational fluid dynamic model, this work presents two novel flow field geometries that are designed to tune the direction of the pressure gradients between channels in order to promote the under-the-rib fluxes mechanism. The first geometry is named Two Outlets and exploits the splitting of the electrolyte flow into two adjacent interdigitated layouts with the aim to give to the pressure gradient a more transverse direction with respect to the channels, raising the intensity of under-the-rib fluxes and making their distribution more uniform throughout the electrode area. The second geometry is named Four Inlets and presents four inlets located at the corners of the distributor, with an interdigitated-like layout radially oriented from each inlet to one single central outlet, with the concept of reducing the heterogeneity of the flow velocity within the electrode. Subsequently, flow fields performance is verified experimentally adopting a segmented hardware in symmetric cell configuration with positive electrolyte, which permits the measurement of local current distribution and local electrochemical impedance spectroscopy. Compared to a conventional interdigitated geometry, both the developed configurations permit a significant decrease in the pressure drops without any reduction in battery performance. In the Four Inlets flow field the pressure drop reduction is more evident (up to 50%) due to the lower electrolyte velocities in the feeding channels, while the Two Outlets configuration guarantees a more homogeneous current density distribution. Full article

Typical usage scenarios for energy storage and electric vehicles (EVs) require lithium-ion batteries (LIBs) to operate under extreme conditions, including varying temperatures, high charge/discharge rates, and various depths of charge and discharge, while also fulfilling vehicle-to-grid (V2G) interaction requirements. This study empirically investigates the impact of ambient temperature, charge/discharge rate, and charge/discharge cut-off voltage on the capacity degradation rate and internal resistance growth of 18,650 commercial LIBs. The charge/discharge rate was found to have the most significant influence on these parameters, particularly the charging rate. These insights contribute to a better understanding of the risks associated with low-temperature aging and can aid in the prevention or mitigation of safety incidents. Full article

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%. Full article

The automotive industry aims for the highest possible driving range (highest energy density) in combination with a fast charge ability (highest power density) of electric vehicles. With both targets being intrinsically contradictory, it is important to understand and optimize resistances within lithium-ion battery (LIB) electrodes. In this study, the properties and magnitude of electronic resistance contributions in LiMn_0.7Fe_0.3PO₄ (LMFP)- and LiNi_xCo_yMn_zO₂ (NCM, x = 0.88~0.90, x + y + z = 1)-based electrodes are comprehensively investigated through the use of different measurement methods. Contact resistance properties are characterized via electrochemical impedance spectroscopy (EIS) on the example of LMFP cathodes. The EIS results are compared to a two-point probe as well as to the results obtained using a novel commercial 46-point probe system. The magnitude and ratio of contact resistance and compound electronic resistance for LMFP- and NCM-based cathodes are discussed on the basis of the 46-point probe measurement results. The results show that the 46-point probe yields significantly lower resistance values than those in EIS studies. Further results show that electronic resistance values in cathodes can vary over several orders of magnitude. Various influence parameters such as electrode porosity, type of current collector and the impact of solvent soaking on electronic resistance are investigated. Full article

The ubiquitous deployment of Li-ion batteries (LIBs) in more demanding applications has reinforced the need to understand the root causes of thermal runaway. Herein, we perform a forensic simulation of a real-case failure scenario, using localised heating of Li(Ni_0.5Mn_0.3Co_0.2)O₂ versus graphite 18650 cylindrical cells. This study determined the localised temperatures that would lead to venting and thermal runaway of these cells, as well as correlating the gases produced as a function of the degradation pathway. Catastrophic failure, involving melting (with internal cell temperatures exceeding 1085 °C), deformation and ejection of the cell componentry, was induced by locally applying 200 °C and 250 °C to a fully charged cell. Conversely, catastrophic failure was not observed when the same temperatures were applied to the cells at a lower state of charge (SOC). This work highlights the importance of SOC, chemistry and heat in driving the thermal failure mode of Ni-rich LIB cells, allowing for a better understanding of battery safety and the associated design improvements. Full article

As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements. Full article

The zinc anode mainly faces technical problems such as short circuits caused by the growth of dendrite, low coulomb efficiency, and a short cycle life caused by side reactions, which impedes the rapid development of aqueous zinc-ion batteries (AZIBs). Herein, a common ionic liquid, 1,1-Spirobipyrrolidinium tetrafluoroborate ([SBP]BF₄), is selected as a new additive for pure ZnSO₄ electrolyte. It is found that this additive could regulate the solvation sheath of hydrated Zn²⁺ ions, promote the ionic mobility of Zn²⁺, homogenize the flux of Zn²⁺, avoid side reactions between the electrolyte and electrode, and inhibit the production of zinc dendrites by facilitating the establishment of an inorganic solid electrolyte interphase layer. With the 1% [SBP]BF₄-modified electrolyte, the Zn||Zn symmetric cell delivers an extended plating/stripping cycling life of 2000 h at 1 mA cm⁻², which is much higher than that of the cell without additives (330 h). As a proof of concept, the Zn‖V₂O₅ battery using the [SBP]BF₄ additive shows excellent cycling stability, maintaining its specific capacity at 97 mAh g⁻¹ after 2000 cycles at 5 A g⁻¹, which is much greater than the 46 mAh g⁻¹ capacity of the non-additive battery. This study offers zinc anode stabilization through high-efficiency electrolyte engineering. Full article

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as “Start & Stop” systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. Full article

The aqueous processing of cathode materials for lithium-ion batteries (LIBs) has both environmental and cost benefits. However, high-loading, water-based electrodes from the layered oxides (e.g., NMC) typically exhibit worse electrochemical performance than NMP-based electrodes. In this work, primary, binary, and ternary binder mixtures of aqueous binders such as CMC, PAA, PEO, SBR, and Na alginate, in combination with bare and C-coated Al current collectors, were explored, aiming to improve the rate capability performance of NMC811 electrodes with high areal capacity (≥4 mAh cm⁻²) and low binder content (3 wt.%). Electrodes with a ternary binder composition (CMC:PAA:SBR) have the best performance with bare Al current collectors, attaining a specific capacity of 150 mAh g⁻¹ at 1C. Using carbon-coated Al current collectors results in improved performance for both water- and NMP-based electrodes. This is further accentuated for Na-Alg and CMC:PAA binder compositions. These electrodes show specific capacities of 170 and 80 mAh g⁻¹ at 1C and 2C, respectively. Although the specific capacities at 1C are comparable to those for NMP-PVDF electrodes, they are approximately 50% higher at the 2C rate. This study aims to contribute to the development of sustainably processed NMC electrodes for high energy density LIBs using water as solvent. Full article

In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts. Full article

Predicting the operating temperature of lithium-ion battery during different cycles is important when it comes to the safety and efficiency of electric vehicles. In this regard, it is vital to adopt a suitable modeling approach to analyze the thermal performance of a battery. In this paper, the temperature of lithium-ion NMC pouch battery has been investigated. A new formulation of lumped model based on the thermal resistance network is proposed. Unlike previous models that treated the battery as a single entity, the proposed model introduces a more detailed analysis by incorporating thermal interactions between individual cells and tabs within a single cell scenario, while also considering interactions between cells and insulators or gaps, located between the cells, within the module case. This enhancement allows for the precise prediction of temperature variations across different cells implemented within the battery module. In order to evaluate the accuracy of the prediction, a three-dimensional finite element model was adopted as a reference. The study was performed first on a single cell, then on modules composed of several cells connected in series, during different operating conditions. A comprehensive comparison between both models was conducted. The analysis focused on two main aspects, the accuracy of temperature predictions and the computational time required. Notably, the developed lumped model showed a significant capability to estimate cell temperatures within the modules. The thermal results revealed close agreement with the values predicted by the finite element model, while needing significantly lower computational time. For instance, while the finite element model took almost 21 h to predict the battery temperature during consecutive charge/discharge cycles of a 10-cell module, the developed lumped model predicted the temperature within seconds, with a maximum difference of 0.42 °C. Full article

Despite features of cost-effectiveness, high safety, and superior capacity, aqueous zinc-ion batteries (ZIBs) have issues of uncontrolled dendritic cell failure and poor Zn utilization, resulting in inferior cycling reversibility. Herein, the environmentally friendly and naturally abundant sodium citrate (SC) was adopted as a dual-functional additive for ZnSO₄-based (ZSO) electrolytes. Owing to the abundant hydrogen-bond donors and hydrogen-bond acceptors of SC, the Zn²⁺-solvation shell is interrupted to facilitate Zn desolvation, resulting in inhibited corrosion reactions. Additionally, sodium ions (Na⁺) from the SC additive with a lower effective reduction potential than that of zinc ions (Zn²⁺) form an electrostatic shield inhibiting the formation of initial surface protuberances and subsequent Zn dendrite growth. This assists in the Zn three-dimensional (3D) diffusion and deposition, thereby effectively enhancing cycling stability. Specifically, a long cycling lifespan (more than 760 h) of the Zn//Zn symmetric cell is achieved with a 2 M ZSO-1.0 SC electrolyte at a current density of 1 mA cm⁻². When coupled with the NaV₃O₈·1.5 H₂O (NVO) cathode, the full battery containing SC additive exhibited a capacity retention rate (40.0%) and a cycling life of 400 cycles at a current density of 1 A g⁻¹ compared with that of pure ZnSO₄ electrolyte (23.8%). This work provides a protocol for selecting an environmentally friendly and naturally abundant dual-functional electrolyte additive to achieve solvation shell regulation and Zn anode protection for the practical large-scale application of ZIBs. Full article

High-entropy materials (HEMs) constitute a revolutionary class of materials that have garnered significant attention in the field of materials science, exhibiting extraordinary properties in the realm of energy storage. These equimolar multielemental compounds have demonstrated increased charge capacities, enhanced ionic conductivities, and a prolonged cycle life, attributed to their structural stability. In the anode, transitioning from the traditional graphite (372 mAh g⁻¹) to an HEM anode can increase capacity and enhance cycling stability. For cathodes, lithium iron phosphate (LFP) and nickel manganese cobalt (NMC) can be replaced with new cathodes made from HEMs, leading to greater energy storage. HEMs play a significant role in electrolytes, where they can be utilized as solid electrolytes, such as in ceramics and polymers, or as new high-entropy liquid electrolytes, resulting in longer cycling life, higher ionic conductivities, and stability over wide temperature ranges. The incorporation of HEMs in metal–air batteries offers methods to mitigate the formation of unwanted byproducts, such as Zn(OH)₄ and Li₂CO₃, when used with atmospheric air, resulting in improved cycling life and electrochemical stability. This review examines the basic characteristics of HEMs, with a focus on the various applications of HEMs for use as different components in lithium-ion batteries. The electrochemical performance of these materials is examined, highlighting improvements such as specific capacity, stability, and a longer cycle life. The utilization of HEMs in new anodes, cathodes, separators, and electrolytes offers a promising path towards future energy storage solutions with higher energy densities, improved safety, and a longer cycling life. Full article

Despite the variety of solid electrolytes available, no single solid electrolyte has been found that meets all the requirements of the successor technology of lithium-ion batteries in an optimum way. However, composite hybrid electrolytes that combine the desired properties such as high ionic conductivity or stability against lithium are promising. The addition of conductive oxide fillers to sulfide solid electrolytes has been reported to increase ionic conductivity and improve stability relative to the individual electrolytes, but the influence of the mixing process to create composite electrolytes has not been investigated. Here, we investigate Li₃PS₄ (LPS) and Li₇La₃Zr₂O₁₂ (LLZO) composite electrolytes using electrochemical impedance spectroscopy and distribution of relaxation times. The distinction between sulfide bulk and grain boundary polarization processes is possible with the methods used at temperatures below 10 °C. We propose lithium transport through the space-charge layer within the sulfide electrolyte, which increases the conductivity. With increasing mixing intensities in a high-energy ball mill, we show an overlay of the enhanced lithium-ion transport with the structural change of the sulfide matrix component, which increases the ionic conductivity of LPS from 4.1 × 10⁻⁵ S cm⁻¹ to 1.7 × 10⁻⁴ S cm⁻¹. Full article

The impact of global warming has required the development of efficient new types of batteries. One of the most promising is Zn-O₂ batteries because they provide the second biggest theoretical energy density, with relevant safety and a cycle of life long enough to be fitted for massive use. However, their industrial use is hindered by a series of obstacles, such as a fast reduction in the energy density after the initial charge and discharge cycles and a limited cathode efficiency or an elevated overpotential between discharge and charge. This work is focused on the synthesis of titanium compounds as catalyzers for the cathode of a Zn-O₂ aqueous battery and their characterization. The results have shown a surface area of 350 m²/g after the elimination of the organic templates during heat treatment at 500 °C in air. Different thermal treatments were performed, tuning different parameters, such as intermediate treatment at 500 °C or the atmosphere used and the final temperature. Surface areas remain high for samples without an intermediate temperature step of 500 °C. Raman spectroscopy studies confirmed the nitridation of samples. SEM and XRD showed macro–meso-porosity and the presence of nitrogen, and the electrochemical evaluation confirmed the catalytic properties of this material in oxygen reaction reduction (ORR)/oxygen evolution reaction (OER) analysis and Zn-O₂ battery tests. Full article

Layered oxides exhibit interesting performance as positive electrodes for commercial sodium-ion batteries. Nevertheless, the replacement of low-sustainable nickel with more abundant iron would be desirable. Although it can be achieved in P2-Na_2/3Ni_2/9Fe_2/9Mn_5/9O₂, its performance still requires further improvement. Many imaginative strategies such as surface modification have been proposed to minimize undesirable interactions at the cathode–electrolyte interface while facilitating sodium insertion in different materials. Here, we examine four different approaches based on the use of the electron-conductive polymer poly(3,4-ethylene dioxythiophene) (PEDOT) as an additive: (i) electrochemical in situ polymerization of the monomer, (ii) manual mixing with the active material, (iii) coating the current collector, and (iv) a combination of the latter two methods. As compared with pristine layered oxide, the electrochemical performance shows a particularly effective way of increasing cycling stability by using electropolymerization. Contrarily, the mixtures show less improvement, probably due to the heterogeneous distribution of oxide and polymer in the samples. In contrast with less conductive polyanionic cathode materials such as phosphates, the beneficial effects of PEDOT on oxide cathodes are not as much in rate performance as in inhibiting cycling degradation, due to the compactness of the electrodes without loss of electrical contact between active particles. Full article

The increasing adoption of lithium-ion battery cells in contemporary energy storage applications has raised concerns regarding their potential hazards. Ensuring the safety of compact and modern energy storage systems over their operational lifespans necessitates precise and dependable monitoring techniques. This research introduces a novel method for the cell-specific surveillance of prismatic lithium-ion cells, with a focus on detecting pressure increases through the surface application of a fiber Bragg grating (FBG) sensor on a rupture disc. Commercially available prismatic cells, commonly used in the automotive sector, are employed as test specimens and equipped with proven pressure and innovative FBG sensors. Encompassing the analysis capacity, internal resistance, and pressure (under elevated ambient temperatures of up to 120 °C), this investigation explores the thermal degradation effects. The applied FBG sensor on the rupture disc exhibits reversible and irreversible state changes in the cells, offering a highly sensitive and reliable monitoring solution for the early detection of abuse and post-abuse cell condition analysis. This innovative approach represents a practical implementation of fiber optic sensor technology that is designed for strain-based monitoring of prismatic lithium-ion cells, thereby enabling customized solutions through which to address safety challenges in prismatic cell applications. In alignment with the ongoing exploration of lithium-ion batteries, this research offers a customizable addition to battery monitoring and fault detection. Full article

This study explores the feasibility of integrating battery technology into electric buses, addressing the imperative to reduce carbon emissions within the transport sector. A comprehensive review and analysis of diverse literature sources establish the present and prospective landscape of battery electric buses within the public transportation domain. Existing battery technology and infrastructure constraints hinder the comprehensive deployment of electric buses across all routes currently served by internal combustion engine counterparts. However, forward-looking insights indicate a promising trajectory with the potential for substantial advancements in battery technology coupled with significant investments in charging infrastructure. Such developments hold promise for electric buses to fulfill a considerable portion of a nation’s public transit requirements. Significant findings emphasize that electric buses showcase considerably lower emissions than fossil-fuel-driven counterparts, especially when operated with zero-carbon electricity sources, thereby significantly mitigating the perils of climate change. Full article

This review analyzed the literature data about the global warming potential (GWP) of the lithium-ion battery (LIB) lifecycle, e.g., raw material mining, production, use, and end of life. The literature data were associated with three macro-areas—Asia, Europe, and the USA—considering common LIBs (nickel manganese cobalt (NMC) and lithium iron phosphate (LFP)). The GWP (kgCO_2eq/kg) values were higher for use compared to raw material mining, production, and end of life management for hydrometallurgy or pyrometallurgy. Considering the significant values associated with the use phase and the frequent application of secondary data, this study also calculated the GWP of LIBs applied in public urban buses in Turin, Italy. The 2021 fleet (53% diesel, 36% natural gas, and 11% electric buses) was compared to scenarios with increasing shares of hybrid/electric. The largest reduction in CO_2eq emissions (−41%) corresponded to a fleet with 64% electric buses. In conclusion, this review highlighted the bottlenecks of the existing literature on the GWP of the LIB lifecycle, a lack of data for specific macro-areas for production and use, and the key role of public transportation in decarbonizing urban areas. Full article

Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision. Full article

Currently, the electrification of transport networks is one of the initiatives being performed to reduce greenhouse gas emissions. Despite the rapid advancement of power electronic systems for electrified transportation systems, their integration into the AC power grid generates a variety of quality issues in the electrical distribution system. Among the possible solutions to this challenge is the inclusion of continuous storage systems, which can be located either onboard or offboard. The rapid development of energy storage devices has enabled the creation of numerous solutions that are leading to ever-increasing energy consumption efficiency, particularly when two or more of these storage systems are linked in a cascade and a hybrid mode. The various energy storage systems that can be integrated into vehicle charging systems (cars, buses, and trains) are investigated in this study, as are their electrical models and the various hybrid storage systems that are available. Full article

A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based on fennec fox optimization algorithm–mixed extreme learning machine (FFA-MELM). Firstly, health indicators are extracted from lithium-battery-charging data, and grey relational analysis (GRA) is employed to identify highly correlated features with the state-of-health of the battery. Subsequently, a state-of-health estimation model based on mixed extreme learning machine is constructed, and the hyperparameters of the model are optimized using the fennec fox optimization algorithm to improve estimation accuracy and convergence speed. The experimental results demonstrate that the proposed method has significantly improved the accuracy of the state-of-health estimation for lithium batteries compared to the extreme learning machine. Furthermore, it can achieve precise state-of-health estimation results for multiple batteries, even under complex operating conditions and with limited charge/discharge cycle data. Full article

Thermal runaway (TR) of lithium-ion batteries has always been a topic of concern, and the safety of batteries is closely related to the operating temperature. An overheated battery can significantly impact the surrounding batteries, increasing the risk of fire and explosion. To improve the safety of battery modules and prevent TR, we focus on the characteristics of temperature distribution and thermal spread of battery modules under overheating conditions. The heat transfer characteristics of battery modules under different battery thermal management systems (BTMSs) are assessed. In addition, the effects of abnormal heat generation rate, abnormal heat generation location, and ambient temperature on the temperature distribution and thermal spread of battery modules are also studied. The results indicate that the BTMS consisting of flat heat pipes (FHPs) and bottom and side liquid cooling plates can effectively suppress thermal spread and improve the safety of the battery module. Full article

Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability. Full article

Layered ternary materials with high nickel content are regarded as the most promising cathode materials for high-energy-density lithium-ion batteries, owing to their advantages of high capacity, low cost, and relatively good safety. However, as the nickel content increases in ternary layered materials, their thermal stability noticeably decreases. It is of paramount importance to explore the characteristics of thermal runaway for lithium-ion batteries. In this study, two high-nickel LiNi_0.9Co_0.05Mn_0.05O₂ batteries were laterally heated to thermal runaway in a sealed chamber filled with nitrogen to investigate the thermal characteristics and gas compositions. The temperature of the battery tabs was measured, revealing that both batteries were in a critical state of thermal runaway near 120 degrees Celsius. A quantitative analysis method was employed during the eruption process, dividing it into three stages: ultra-fast, fast, and slow; the corresponding durations for the two batteries were 3, 2, 27 s and 3, 3, 26 s. By comparing the changes in chamber pressure, it was observed that both batteries exhibited a similar continuous venting duration of 32 s. However, the pressure fluctuation ranges of the two samples were 99.5 and 68.2 kPa·m·s⁻¹. Compared to the other sample, the 211 Ah sample exhibited larger chamber pressure fluctuations and reached higher peak pressures, indicating a higher risk of explosion. In the experimental phenomenon captured by a high-speed camera, it took only 1 s for the sample to transition from the opening of the safety valve to filling the experimental chamber with smoke. The battery with higher energy density exhibited more intense eruption during thermal runaway, resulting in more severe mass loss. The mass loss of the two samples is 73% and 64.87%. The electrolyte also reacted more completely, resulting in a reduced number of measured exhaust components. The main components of gaseous ejections are CO, CO₂, H₂, C₂H₄, and CH₄. For the 211 Ah battery, the vented gases were mainly composed of CO (41.3%), CO₂ (24.8%), H₂ (21%), C₂H₄ (7.4%) and CH₄ (3.9%), while those for the other 256 Ah battery were mainly CO (30.6%), CO₂ (28.5%), H₂ (21.7%), C₂H₄ (12.4%) and CH₄ (5.8%). Comparatively, the higher-capacity battery produced more gases. The gas volumes, converted to standard conditions (0 °C, 101 kPa) and normalized, resulted in 1.985 L/Ah and 2.182 L/Ah, respectively. The results provide valuable guidance for the protection of large-capacity, high-energy-density battery systems. The quantitative analysis of the eruption process has provided assistance to fire alarm systems and firefighting strategies. Full article

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources. Full article