Search Results (160)

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

This study presents the design and techno-economic comparison of two standalone photovoltaic (PV) systems, each supplying a 1 kW critical load with 100% reliability under Cairo’s climatic conditions. These systems are modeled for both the constant and the night load scenarios, accounting for the worst-case weather conditions involving 3.5 consecutive cloudy days. The primary comparison focuses on traditional lead-acid battery storage versus green hydrogen storage via electrolysis, compression, and fuel cell reconversion. Both the configurations are simulated using a Python-based tool that calculates hourly energy balance, component sizing, and economic performance over a 21-year project lifetime. The results show that the PV/H₂ system significantly outperforms the PV/lead-acid battery system in both the cost and the reliability. For the constant load, the Levelized Cost of Electricity (LCOE) drops from 0.52 USD/kWh to 0.23 USD/kWh (a 56% reduction), and the payback period is shortened from 16 to 7 years. For the night load, the LCOE improves from 0.67 to 0.36 USD/kWh (a 46% reduction). A supplementary cost analysis using lithium-ion batteries was also conducted. While Li-ion improves the economics compared to lead-acid (LCOE of 0.41 USD/kWh for the constant load and 0.49 USD/kWh for the night load), this represents a 21% and a 27% reduction, respectively. However, the green hydrogen system remains the most cost-effective and scalable storage solution for achieving 100% reliability in critical off-grid applications. These findings highlight the potential of green hydrogen as a sustainable and economically viable energy storage pathway, capable of reducing energy costs while ensuring long-term resilience. Full article

The integration of lithium-ion (Li-ion) battery energy storage systems (LiBESSs) with photovoltaic (PV) generation offers a promising solution for powering auxiliary services (ASs) in high-voltage power stations. This study conducts a systematic literature review (SLR) to evaluate the feasibility, benefits, and challenges of this integration. The proposed SLR complies with the PRISMA 2020 statement, and it is also registered on the international PROSPERO platform (ID 1073599). The selected methodology includes the following key steps: definition of the research questions; search strategy development; selection criteria of the studies; quality assessment; data extraction and synthesis; and discussion of the results. Through a comprehensive analysis of scientific publications from 2013 to 2024, trends, advancements, and research gaps are identified. The methodology follows a structured review framework, including data collection, selection criteria, and evaluation of technical feasibility. From 803 identified studies, 107 were eligible in accordance with the assessed inclusion criteria. Then, a custom study impact factor (SIF) framework selected 5 out of 107 studies as the most representative and assertive ones on the topics of this SLR. The findings indicate that Li-ion BESSs combined with PV systems enhance reliability, reduce reliance on conventional sources, and improve grid resilience, particularly in remote or constrained environments. The group of reviewed studies discuss optimization models and multi-objective strategies for system sizing and operation, along with practical case studies validating their effectiveness. Despite these advantages, challenges related to cost, regulatory frameworks, and performance variability remain. The study concludes that further experimental validations, pilot-scale implementations, and assessment of long-term economic impacts are necessary to accelerate the adoption of BESS-PV systems in high-voltage power substations. This study was funded by the R&D program of the Brazilian National Electric Energy Agency (ANEEL) via project number PD-07351-0001/2022. Full article

Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating conditions. Optimizing thermal management systems remains critical, particularly for long-range and weight-sensitive applications. In these contexts, passive heat dissipation emerges as an ideal solution, offering effective thermal regulation with minimal additional system weight. This study aims to deepen the understanding of passive heat dissipation in 21700 battery cells and optimize their performance. Special emphasis is placed on analyzing heat transfer and the relative contributions of convective and radiative mechanisms under varying temperature and discharge conditions. Laboratory experiments were conducted under controlled environmental conditions at various discharge rates, ranging from 0.5×C to 5×C. A 3D-printed polymer casing was applied to the cell to enhance thermal dissipation, designed specifically to increase radiative heat transfer while minimizing system weight and reliance on active cooling solutions. Additionally, a numerical model was developed and optimized using experimental data. This model simulates convective and radiative heat transfer mechanisms with minimal computational demand. The optimized numerical model is intended to facilitate further investigation of the cell envelope strategy at the module and battery pack levels in future studies. Full article

This paper proposes the sizing optimization method and energy management strategy for a stationary hybrid energy storage system dedicated to a DC traction power supply system. The hybrid energy storage system consists of two modules—a supercapacitor, mainly dedicated to regenerative energy utilization, and a Li-ion battery, aimed to peak power reduction. The sizing method and energy management strategy proposed in this paper aim to reduce the aging effect of lithium-ion batteries. It is shown that the parameters of both modules could be sized independently. The supercapacitor module parameters are sized based on the results of a simulation determining the regenerative power, resulting in limited catenary receptivity. The simulation model of the DC electrification system is validated by comparing the results of the simulation with the measurements of 15 min average power in a 24 h cycle as average values of one year. The battery module is sized based on the statistical data of 15 min substation power value occurrences. The battery energy capacity, its maximum discharge C-rate, and the conditions determining its operation are optimized to achieve the maximum ratio of annual income resulting from peak power reduction to annual operating cost resulting from the battery aging process and total life cycle. The case study prepared for a typical 3 kV DC substation with mixed railway traffic shows that peak power could be reduced by ~1 MW, giving a ~10-year payback period for battery module installation, while the energy consumption could be decreased by 1.9 MWh/24 h, giving a ~7.5-year payback period for supercapacitor module installation. The payback period of the whole energy storage system (ESS) is ~8.4 years. Full article

The rapid rise in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has opened the door for diverse potential applications in powering indoor Internet of Things (IoT) devices. An energy harvesting system (EHS) powered by a PSC module with a backup Li-ion battery, which stores excess power at moments of high irradiances and delivers the stored power to drive the load during operation scenarios with low irradiances, has been designed. A DC-DC boost converter is engaged to match the voltage of the PSC and Li-ion battery, and maximum power point tracking (MPPT) is achieved by a perturb and observe (P&O) algorithm, which perturbs the photovoltaic (PV) system by adjusting its operating voltage and observing the difference in the output power of the PSC. Furthermore, the charging and discharging rate of the battery storage is controlled by a DC-DC buck–boost bidirectional converter with the incorporation of a proportional–integral (PI) controller. The bidirectional DC-DC converter operates in a dual mode, achieved through the anti-parallel connection of a conventional buck and boost converter. The proposed EHS utilizes DC-DC converters, MPPT algorithms, and PI control schemes. Three different case scenarios are modeled to investigate the system’s behavior under varying irradiances of 200 W/m², 100 W/m², and 50 W/m². For all three cases with different irradiances, MPPT achieves tracking efficiencies of more than 95%. The laboratory-fabricated PSC operated at MPP can produce an output power ranging from 21.37 mW (50 W/m²) to 90.15 mW (200 W/m²). The range of the converter’s output power is between 5.117 mW and 63.78 mW. This power range can sufficiently meet the demands of modern low-energy IoT devices. Moreover, fully charged and fully discharged battery scenarios were simulated to study the performance of the system. Finally, the IoT load profile was simulated to confirm the potential of the proposed energy harvesting system in self-sustainable IoT applications. Upon review of the current literature, there are limited studies demonstrating a combination of EHS with PSCs as an indoor power source for IoT applications, along with a bidirectional DC-DC buck–boost converter to manage battery charging and discharging. The evaluation of the system performance presented in this work provides important guidance for the development and optimization of new-generation PV technologies like PSCs for practical indoor applications. Full article

By employing electrochemical impedance spectroscopy, we performed an impedance analysis of three commercial Li-ion Panasonic NCR18650B cells in order to investigate the direct effects of their internal impedance on the operating voltage, rate capability, and efficiency and their practical capacity. We aimed to assess their performance, safety, and longevity when distinct fast charge/discharge rates were applied. By maintaining a constant fast discharge rate of 2C, we monitored the degradation speed and the influence of the C-rates on the LIBs by applying distinct charge rates, namely, 1C, 1.5C, and 2C. In order to understand how their performance correlates with usage conditions, an SoH evolution analysis, together with a Q–Q₀ total charge and energy consumption examination, was performed, taking into account that cycling monitoring is vital for ensuring their longevity and/or safety. Increasing the I_charge from 1C to 1.5C reduces the battery lifetime by ~50%, while in the case of fast charge/discharge rates of 2C, the lifetime performance decrease is almost ~70% due to a capacity loss that accelerates quickly when the charge rates increase. Moreover, for the latter cell, the last discharge rate can only go up to ~80% SoH, as the battery charge rate can no longer support faster degradation. In agreement with these results, the fluctuations in the Q–Q₀ total charge become more pronounced, clearly affecting LIB efficiency. High charge rates add an additional high voltage that increases the batteries’ stress, leading to a shorter lifetime. Energy consumption data follow the same trend, in which efficiency decreases dramatically when losses appear because the internal resistance causes more and more heat to be produced during both fast charging and discharging. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

Herein, we investigate the performance and safety of four of the early-stage, commercial Na-ion batteries available in 2024, representing the most popular cathode types across research and commercialization: polyanion (Na-VPF), layered metal oxide (Na-NMF), and a Prussian blue analog (Na-tmCN). The cells deliver a wide range of energy density with Na-tmCN delivering the least (23 Wh/kg) and Na-NMF delivering the most (127 Wh/kg). The Na-VPF cell was in between (47 Wg/kg). Capacity retention under specified cycling conditions and with periodic 0 V excursions was the most robust for the Na-tmCN cells in both cases. Accelerating rate calorimetry (ARC) and nail penetration testing finds that Na-NMF cells do undergo thermal runaway in response to abuse, while the Na-VPF and Na-tmCN exhibit only low self-heating rates (<1 °C/min). During these safety tests, all cells exhibited off-gassing, so we conducted in-line FTIR equipped with a heated gas cell to detect CO, CO₂, CH₄, toxic acid gases (HCN, HF, NH₃), and typical electrolyte components (carbonate ester solvents). Gases similar to those detected during Li-ion failures were found in addition to HCN for the Na-tmCN cell. Our work compares different types of commercial Na-ion batteries for the first time, allowing for a more holistic comparison of the safety and performance tradeoffs for different Na-ion cathode types emerging in 2024. Full article

Transition metal oxides (TMOs) occupy an increasing share in the search for new electrode materials for Li-Ion batteries. Despite promising electrochemical performances (up to 1000 mAh g⁻¹ in the case of conversion), these materials have poor cyclability linked primarily to hysteresis phenomena. To improve their electrochemical performance, one strategy consists of reducing the particle size. A second strategy relies on the incorporation of fluorine directly into electrode materials to limit the solid–electrolyte interface (SEI). Our study focuses on the impact of fluorination on the electrochemical performance of manganese oxide obtained by solid combustion synthesis (SCS). Two fluorinating agents were used: pure gaseous molecular fluorine F₂ and radical fluorine F^• through xenon difluoride XeF₂ decomposition. The use of F₂ results in strong fluorination localized primarily at the particle surface while XeF₂ diffuses deeper into the particle, resulting in the removal of residual carbon from the synthesis by combustion. The electrochemical performance of the oxide fluorinated with XeF₂ reaches more than 750 mAh g⁻¹ after 160 cycles, whereas that of the oxide fluorinated by F₂ barely exceeds that of the non-fluorinated oxide, less than 200 mAh g⁻¹ after 200 cycles. Full article

To promote sustainability and reduce the ecological footprint of recycling processes, this study develops an analytical tool for fast and accurate identification of components in photovoltaic panels (PVs) and Li-Ion battery waste, optimizing material recovery and minimizing resource wastage. The laser-induced breakdown spectroscopy (LIBS) technique was selected and employed to identify fluoropolymers in photovoltaic back sheets and to determine the thickness of layers containing fluorine. LIBS was also used for Li-Ion batteries to reveal the elemental composition of anode, cathode, and separator materials. The analysis not only revealed all the elements contained in the electrodes but also, in the case of cathode materials, allowed distinguishing a single-component cathode (cathode A containing LiCoO₂) from multi-component materials (cathode B containing a mixture of LiMn₂O₄ and LiNi_0.5Mn_1.5O₄). The results of LIBS analysis were verified using SEM-EDS analysis and XRD examination. Additionally, an indirect method for identifying fluoropolymers (polytetrafluoroethylene (PTFE) or poly(vinylidene fluoride) (PVDF)) employed to prepare dispersions of cathode materials was proposed according to the differences in wettability of both polymers. By enabling efficient material identification and separation, this study advances sustainable recycling practices, supporting circular economy goals in the renewable energy sector. Full article

Li-ion batteries are at risk of explosions caused by fires, primarily because of the high energy density of Li ions, which raises the temperature. Battery cases are typically made of plastic, aluminum, or SAF30400. Although plastic and aluminum aid weight reduction, their strength and melting points are low. SAF30400 offers excellent strength and corrosion resistance but suffers from work hardening and low high-temperature strength at 700 °C. Additionally, Ni used for plating has a low current density of 25% international copper alloy standard (ICAS). SAF2507 is suitable for use as a Li-ion battery case material because of its excellent strength and corrosion resistance. However, the heterogeneous microstructure of SAF2507 after casting and processing decreases the corrosion resistance, so it requires solution heat treatment. To address these issues, in this study, SAF2507 (780 MPa, 30%) is solution heat-treated at 1100 °C after casting and coated with Ag (ICAS 108.4%) using physical vapor deposition (PVD). Ag is applied at five different thicknesses: 0.5, 1.0, 1.5, 2.0, and 2.5 μm. The surface conditions and electrochemical properties are then examined for each coating thickness. The results indicate that the PVD-coated surface forms a uniform Ag layer, with electrical conductivity increasing from 1.9% ICAS to 72.3% ICAS depending on the Ag coating thickness. This enhancement in conductivity can improve Li-ion battery safety on charge and use. This result is expected to aid the development of advanced Li-ion battery systems in the future. Full article

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals. Full article

Solid-state electrolytes for lithium-ion batteries, which enable a significant increase in storage capacity, are at the forefront of alternative energy storage systems due to their attractive properties such as wide electrochemical stability window, relatively superior contact stability against Li metal, inherently dendrite inhibition, and a wide range of temperature functionality. NASICON-type solid electrolytes are an exciting candidate within ceramic electrolytes due to their high ionic conductivity and low moisture sensitivity, making them a prime candidate for pure oxidic and hybrid ceramic-in-polymer composite electrolytes. Here, we report on producing pure and Y-doped Lithium Aluminum Titanium Phosphate (LATP) nanoparticles by spray-flame synthesis. The as-synthesized samples consist of an amorphous component and anatase-TiO₂ crystalline particles. Brief annealing at 750–1000 °C for one hour was sufficient to achieve the desired phase while maintaining the material’s sub-micrometer scale. Rietveld analysis of X-Ray diffraction data demonstrated that the crystal volume increases with Y doping. At the same time, with high Y incorporation, a segregation of the YPO₄ phase was observed in addition to the desired LATP phase. Another impurity phase, LiTiOPO₄, was observed besides YPO₄ and, with higher calcination temperature (1000 °C), the phase fraction for both impurities also increased. The ionic conductivity increased with Y incorporation from 0.1 mS/cm at room temperature in the undoped sample to 0.84 mS/cm in the case of LAY0.1TP, which makes these materials—especially considering the comparatively low sintering temperature—highly interesting for applications in the field of solid-state batteries. Full article