Search Results (376)

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

With the increasing deployment of lithium iron phosphate (LiFePO₄) batteries in electric vehicles and energy storage systems, the recycling of these materials has become an urgent necessity. Specifically, the reclamation of lithium iron phosphate cathode materials presents a significant challenge in the recycling process. In this study, we proposed an efficient low-temperature hydrothermal direct regeneration method aimed at repairing lithium vacancies and Fe/Li inversion defects in spent lithium iron phosphate resulting from prolonged cycling. By using this method, spent lithium iron phosphate was successfully regenerated through a hydrothermal process conducted at 80 °C for 6 h, utilizing hydrazine hydrate (N₂H₄·H₂O) as a potent reducing agent and lithium hydroxide (LiOH·H₂O) as the lithium source. X-ray diffraction (XRD) analysis, coupled with Rietveld refinement, revealed a substantial reduction in the concentration of Fe/Li anti-site defects in the spent material, decreasing from 8.8% to 3.3% following regeneration. Consequently, the electrochemical performance was significantly restored. The initial specific discharge capacity increased from 118.0 mAh·g⁻¹ to 150.3 mAh·g⁻¹, and the capacity retention after 100 cycles (at 1 C) improved from 67.5% to 90.7%. The hydrothermal regeneration process introduced in this work effectively repairs the material structure and restores the active valence state of iron, thereby significantly enhancing lithium-ion diffusion and electron transport capabilities. This approach constitutes a technically viable solution for the efficient, environmentally friendly, and cost-effective recycling of spent lithium-ion batteries. Full article

Standalone photovoltaic systems play an important role in providing reliable renewable energy in remote areas. These systems depend heavily on battery energy storage, especially lithium iron phosphate batteries, which are known for their safety and long cycle life. However, battery degradation remains a major challenge, as high charging currents, temperature variations, and wide state-of-charge fluctuations introduce electro-thermal stress that reduces the useful lifetime of the storage system. To address this issue, this paper presents a Flexible Power Point Tracking (FPPT) strategy supported by a fuzzy-logic-based controller. In this context, battery stress refers to the combined electrochemical and thermal stress induced by high charging currents, elevated operating temperatures, and large state-of-charge (SOC) excursions, which are known to accelerate ageing mechanisms and capacity fade. Based on a review of the existing literature, most FPPT and lifetime-oriented control studies have focused on lithium-ion batteries such as NMC or LCO chemistries, while limited attention has been given to lithium iron phosphate (LiFePO₄) batteries. The goal is to limit battery stress by reducing current peaks, mitigating temperature rise, and smoothing state-of-charge variations, thereby improving battery lifetime without compromising the stability of the standalone PV system. A complete PV–battery model is developed in PLECS and tested using one-year irradiance, temperature, and load data from Perth, Australia. The results show that the FPPT–Fuzzy controller reduces current peaks, stabilises the state of charge, and lowers the thermal impact on the battery when compared with traditional MPPT. As a result, overall degradation decreases and the battery lifetime is extended by approximately 7%. These findings demonstrate that FPPT is a promising method for improving the long-term performance of renewable energy systems based on lithium iron phosphate battery storage. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

Given the importance of nickel in lithium-ion batteries and the expectation of the growth in electric vehicles and electrical devices, the demand for nickel in battery production is expected to increase dramatically. Nickel is primarily sourced from laterite saprolite ore, and there is now substantial interest in moving from ferronickel smelting technology to nickel matte technology in its processing to produce high-grade nickel. This research involved a thermodynamic modelling and lab–scale experiment on the smelting of nickel matte. Nickel concentrate from laterite saprolite was used, and CaS, produced from commercially available gypsum, was employed as a sulfurizing agent. The matte smelting experiment was conducted at 1500 °C to optimize CaS and C consumption. During smelting with CaS, matte particles form, although sufficient reduction of nickel and iron from the concentrate is not achieved. By consuming carbon, the reduction potential of iron is increased, and this process, along with enriching the matte with iron, aids in the transportation of nickel. At a nickel grade in the matte with a Ni/Fe ratio of approximately 1, the nickel recovery only reached 63%. Upon achieving a nickel recovery exceeding 93%, the Ni/Fe ratio reached 0.44, corresponding to a nickel grade reduction to 22.78%. By employing analytical techniques and thermodynamic modelling, we have successfully determined the sulfidizing of nickel, identified the ideal CaS and C additions, and characterized the structure and quality of the slag produced during nickel matte smelting, supplying vital technological data necessary for practical application. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

Lithium iron phosphate (LiFePO₄, LFP) batteries dominate grid-scale energy storage, yet their cycle life is capped by its capacity fade issues. Conventional failure workflows suffer from redundant tests, high cost, and long turnaround time because the underlying mechanisms remain unclear. Herein, multi-scale characterization coupled with electrochemical tests have been quantitatively established to reveal four synergistic fade modes of LFP: active-Li loss, FePO₄ secondary-phase formation, SEI rupture, and particle fracture. A two-tier “screen–validate” protocol is proposed to accurately and efficiently disclose its mechanism. In the screening tier, capacity, cyclic voltammetry, electrochemical impedance spectroscopy, low-magnification scanning electron microscopy, and snapshot X-ray diffraction (XRD) rapidly flag the most probable failure cause. The validation tier then deploys mechanism-matched in situ/ex situ tools (operando XRD, TEM, XPS, ToF-SIMS, etc.) to build a comprehensive evidence chain of dynamic structural evolution, materials loss tracking, and quantitative proof. The streamlined workflow preserves scientific rigor and reproducibility while cutting analysis time and cost, offering a closed-loop route for fast failure diagnosis and targeted optimization of next-generation LFP batteries. Full article

The automotive industry is under pressure to reduce greenhouse gas emissions. While the growth of electric vehicles is crucial, optimizing low-voltage batteries for conventional powertrain architecture (12–48 V) can help reduce carbon dioxide emissions. Currently, lithium iron phosphate (LFP) batteries dominate the low-voltage battery market due to their stability, safety, and ecological benefits as replacement to lead-acid. However, sodium-ion batteries (SIB) are emerging as a promising alternative to LFP, offering advantages in power, lifespan, cold temperature performance, integration, cost, material availability, and sustainability. These advantages of sodium-ion batteries make them a perfect candidate for fulfilling the requirements typically associated with 48 V applications as well. This contribution evaluates low-voltage SIB prototypes developed by the company IAV GmbH and its partners and explores their potential for automotive applications, aiming to share insights and assess future prospects. Full article

This study presents a cradle-to-grave Life Cycle Assessment (LCA) and cost analysis of next-generation low-temperature district heating networks based on water-condensed electric booster heat pumps. The research, conducted within the ENEA Portici Research Center framework, evaluates multiple case studies to assess environmental and economic sustainability. The system boundaries include heat generators (geothermal heat pump, solar thermal, and photovoltaic–thermal hybrid), network configurations (tree vs. ring), supply temperatures (25 °C vs. 45 °C), and renewable electricity shares (0–100%). Environmental impacts were quantified using the Environmental Footprint 3.1 method, focusing on key indicators such as climate change, resource use, and human toxicity. The results show that supply temperature is a critical factor: 45 °C scenarios lead to notably higher impacts, while network configuration has only marginal effects. Among generation technologies, the photovoltaic–thermal system proved the most sustainable, despite higher investment costs, whereas the solar thermal system displayed the largest environmental burden but lower costs. Geothermal systems showed intermediate performance, with notable impacts from mineral resource use. Renewable electricity integration consistently improved environmental outcomes, reducing climate change impacts by up to 10%. Storage system comparison revealed lithium iron phosphate (LFP) batteries as the most advantageous solution for electrical storage, and phase-change materials (PCM), particularly Rubitherm15, as the most environmentally favorable option for thermal storage, although traditional water tanks remain more cost-effective. Overall, the study highlights the crucial role of renewable integration and temperature optimization in enhancing the eco-efficiency of low-temperature district heating networks, providing guidelines for future sustainable design and deployment. Full article

Efficiently predicting the lifespan of lithium iron phosphate (LFP) batteries early in their operational life is critical to accelerating the development of energy storage systems while reducing testing time, cost, and resource consumption. Traditional degradation models rely on full-cycle testing to estimate long-term performance, which is both time- and resource-intensive. This study proposes a novel semi-empirical degradation model that leverages a small fraction of early-cycle data with just 5% to accurately forecast full-lifetime performance with high accuracy, with less than 1.5% mean absolute percentage error. The model integrates fundamental degradation physics with data-driven calibration, using an improved musical chairs algorithm modified with chaotic map dynamics to optimize model parameters efficiently. Trained and validated on a diverse dataset of 27 LFP cells cycled under varying depths of discharge, current rates, and temperatures, the proposed method demonstrates superior convergence speed, robustness across LFP operating conditions, and predictive accuracy compared to traditional approaches. These results provide a scalable framework for rapid battery evaluation and deployment, supporting advances in electric mobility and grid-scale storage. Full article

Sodium-ion batteries (SIBs) have arisen as a potential alternative to lithium-ion batteries (LIBs) as a result of the abundant availability of sodium resources at low production costs, making them in line with the United Nations Sustainable Development Goals (SDGs) for affordable and clean energy (Goal 7). The current review intends to comprehensively analyse the various modification techniques deployed to improve the performance of cathode materials for SIBs, including element doping, surface coating, and morphological control. These techniques have demonstrated prominent improvements in electrochemical properties, such as specific capacity, cycling stability, and overall efficiency. The findings indicate that element doping can optimise electronic and ionic conductivity, while surface coatings can enhance stability in addition to mitigating side reactions throughout cycling. Furthermore, morphological control is an intricate technique to facilitate efficient ion diffusion and boost the use of active materials. Statistically, the Cr-doped NaV_1−xCr_xPO₄F achieves a reversible capacity of 83.3 mAh/g with a charge–discharge performance of 90.3%. The sodium iron–nickel hexacyanoferrate presents a discharge capacity of 106 mAh/g and a Coulombic efficiency of 97%, with 96% capacity retention over 100 cycles. Furthermore, the zero-strain cathode Na₄Fe₇(PO₄)₆ maintains about 100% capacity retention after 1000 cycles, with only a 0.24% change in unit-cell volume throughout sodiation/desodiation. Notwithstanding these merits, this review ascertains the importance of ongoing research to resolve the associated challenges and unlock the full potential of SIB technology, paving the way for sustainable and efficient energy storage solutions that would aid the conversion into greener energy systems. Full article

Electric vehicles (EVs) have garnered significant attention in recent years due to their near-zero carbon dioxide emissions and compatibility with sustainable transportation systems. However, the lack of high-performance batteries remains a major barrier to widespread EV adoption. This study examines the variations in heat transfer coefficient and surface temperature of prismatic lithium iron phosphate (LiFePO₄) battery packs during discharge operations. Experiments were conducted using both forced air convection and natural convection. A wind tunnel was constructed to maintain an ambient temperature of 25 °C. The air flow rates were set at 0, 40, 80, and 120 g/s, while the battery pack spacings were 5, 10, and 15 mm. Discharge rates of 0.50, 0.75, and 1.00 C-rate were also examined. The results reveal that increasing the discharge rate led to a significant and uniform rise in surface temperature across the battery pack. Additionally, the voltage decreased gradually until an approximately 90% depth of discharge, after which it declined rapidly until the battery pack was depleted. Under forced convection, the voltage drop occurred slightly faster than that under natural convection. Greater spacing between battery packs enhanced cooling efficiency. Higher air flow rates increased the convection coefficient, whereas an increased discharge rate elevated the heat generation but reduced the heat convection coefficient. The highest heat dissipation was observed at a battery pack spacing of 15 mm, a discharge rate of 1.00 C, and an air flow rate of 120 g/s. The highest convection coefficient was achieved under the same spacing and air flow rate, but with a discharge rate of 0.50 C-rate. Full article

This study undertakes a detailed computational examination of a direct refrigerant cooling approach for a 50 Ah prismatic lithium iron phosphate (LiFePO₄) battery. We conducted a systematic assessment to determine how the cooling plate’s topological layout and flow orientation influenced key performance indicators, namely thermal homogeneity, heat removal efficiency, and hydraulic pressure loss. Utilizing a validated two-phase flow model with 1,1,1,2-Tetrafluoroethane (R134a), simulations were performed on six distinct serpentine channel designs under a wide range of operating scenarios, covering variations in mass flow rate, saturation temperature, and inlet vapor quality. The simulation data revealed a strong correlation between the cooling plate’s geometric parameters and the system’s thermal behavior. In terms of uniformity, the optimized Case 6 configuration significantly outperformed Case 2, achieving a 76% improvement by narrowing the maximum mid-plane temperature difference from 2.02 °C down to 0.48 °C. A trade-off was observed regarding the mass flow rate: while higher rates lowered the peak temperature by approximately 18%, they simultaneously led to increased hydraulic pressure loss and slight non-uniformity. Similarly, decreasing the saturation temperature improved heat extraction but exacerbated flow resistance. Notably, this study identified an inlet vapor quality of 0.1 as the optimal point for maximizing temperature uniformity. These insights provide a robust theoretical foundation for optimizing the design and operation of compact direct refrigerant-based BTMSs. Full article

During the recycling process of spent lithium-ion batteries (LIBs), there is a large number of fluoride ions in the leaching solution. These fluoride ions not only affect the quality of lithium products, but they also have adverse effects on the environment. Therefore, the efficient and deep removal of the characteristic pollutant fluoride ions is currently a hot topic in the field of recycling spent LIBs. In this study, a porous zirconium-based adsorbent was prepared and its adsorptive properties were characterized. Due to the excellent affinity between zirconium and fluorine, the zirconium-based adsorbent exhibited excellent adsorption performance in the leaching solution of spent lithium iron phosphate (SLFP) batteries. Under the optimal adsorption conditions, the adsorption capacity reached 113.78 mg/g, and it surpassed most commercial adsorbents. The zirconium-based adsorbent followed the Langmuir isotherm model for fluoride adsorption with correlation coefficients consistently exceeding 0.95, and exhibited pseudo-second-order kinetics, demonstrating goodness-of-fit values above 0.998. The negative Gibbs free energy change thermodynamically confirms the spontaneous nature of the adsorption process. The structure of the adsorbent before and after adsorption was characterized, and the adsorption mechanism was elaborated in detail. Furthermore, the influence of the coexistence of different anions on the adsorption of fluoride ions by zirconium-based adsorbent was studied in a real leaching solution from SLFP calcine. This study provides a feasible approach to deep defluoridation for leachate from spent LIBs, and has the advantages of simple operation and high adsorption capacity. Full article