Search Results (349)

This paper describes the design and implementation of a battery equalizer circuit for small satellites, developed under the New Space philosophy exclusively using commercial off-the-shelf (COTS) components. The primary objective is to ensure high reliability for mission-critical power systems while adhering to strict cost constraints. In order to achieve this objective, the design incorporates a robust analog control circuit, thereby avoiding the complexities and potential single-point failures associated with digital controllers. A comprehensive study of various cell-balancing topologies was conducted, leading to the selection, hardware implementation, and comparative analysis of the two most suitable candidates. The results of this study provide a validated, cost-effective, and reliable battery equalizer solution for developers of small satellites. Full article

Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article

To address the limitations of conventional single-stack fuel cell hybrid systems using equivalent hydrogen consumption strategies, this study proposes a multi-stack energy management strategy incorporating fuel cell health degradation. Leveraging a fuel cell efficiency decay model and lithium-ion battery cycle life assessment, power distribution is reformulated as an equivalent hydrogen consumption optimization problem with stack degradation constraints. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) approach achieves global optimization. The experimental results demonstrate that compared with the Frequency Decoupling (FD) method, the GA-PSO strategy reduces hydrogen consumption by 7.03 g and operational costs by 4.78%; compared with the traditional Particle Swarm Optimization (PSO) algorithm, it reduces hydrogen consumption by 3.61 g per operational cycle and decreases operational costs by 2.66%. This strategy ensures stable operation of the marine power system while providing an economically viable solution for hybrid-powered vessels. Full article

There is an increasing demand for the development of efficient and sustainable battery recycling processes. Currently, many recycling processes rely on toxic inorganic acids to recover materials from high-value battery chemistries such as lithium nickel manganese cobalt oxides (NMCs) and lithium cobalt oxide (LCOs). However, as cell manufacturers seek more cost-effective battery chemistries, the value of the spent battery value chain is increasingly diluted by chemistries such as lithium iron phosphate (LFPs). These cheaper alternatives present a difficulty when recycling, as current recycling processes are geared towards dealing with high-value chemistries; thus, the current processes become less economical. To date, much research is focused on treating a single battery chemistry; however, often, the feed material entering a battery recycling facility is contaminated with other battery chemistries, e.g., LFP feed contaminated with NMC, LCO, or LMOs. This research aims to selectively leach various battery chemistries out of a mixed feed material with the aid of a green organic acid, namely oxalic acid. When operating at the optimal conditions (2% solids, 0.25 M oxalic acid, natural pH around 1.15, 25 °C, 60 min), this research has proven that oxalic acid can be used to selectively dissolve 95.58% and 93.57% of Li and P, respectively, from a mixed LFP-NMC mixed feed, all while only extracting 12.83% of Fe and 8.43% of Mn, with no Co and Ni being detected in solution. Along with the high degree of selectivity, this research has also demonstrated, through varying the pH, that the selectivity of the leaching system can be altered. It was determined that at pH 0.5 the system dissolved both the NMC and LFP chemistries; at a pH of 1.15, the LFP chemistry (Li and P) was selectively targeted. Finally, at a pH of 4, the NMC chemistry (Ni, Co and Mn) was selectively dissolved. Full article

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

This paper introduces a data-driven methodology for accurately estimating the residual capacity (RC) of lithium iron phosphate (LFP) batteries through a tailored artificial neural network (ANN) architecture. The proposed model integrates a long short-term memory (LSTM) layer with a fully connected layer, leveraging their combined strengths to achieve precise RC predictions. A distinguishing feature of this study is its ability to deliver highly accurate estimates using a limited dataset that was derived from a single cylindrical LFP battery with a 40 Ah capacity and collected during a controlled experimental campaign. Despite the constraints imposed by the dataset size, the ANN demonstrates remarkable performance, underscoring the model’s capability to operate effectively with minimal data. The dataset is partitioned into the training and testing subsets to ensure a rigorous evaluation. Additionally, the robustness of the approach is validated by testing the trained ANN on data from a second battery cell subjected to a distinct aging process, which was entirely unseen during training. This critical aspect underscores the method’s applicability in estimating RC for batteries with varying aging profiles, a key requirement for real-world deployment. The proposed LSTM-based architecture was also benchmarked against a GRU-based model, yielding significantly lower prediction errors. Furthermore, beyond LFP chemistry, the method was tested on a broader NMC dataset comprising seven cells aged under different C-rates and temperatures, where it maintained high accuracy, confirming its scalability and robustness across chemistries and usage conditions. These results advance battery management systems by offering a robust, efficient modeling framework that optimizes battery utilization across diverse applications, even under data-constrained conditions. Full article

Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging rates and ambient temperatures in overcharge-induced thermal runaway, filling the knowledge gaps associated with multi-indicator thermal management approaches. Through experiments on prismatic LFP cells across five operational conditions (1C/35 °C, 1.5C/5 °C, 1.5C/15 °C, 1.5C/25 °C, and 1.5C/35 °C), synchronized infrared thermography and electrochemical monitoring quantitatively characterize the thermal–electric coupling dynamics throughout overcharge-to-runaway transitions. The experimental findings reveal three key observations: (1) Charge rate and temperature have synergistic amplification effects on triggering thermal runaway. (2) Contrary to intuition, while low-current/high-temperature charging enhances safety versus high-current/high-temperature conditions, low-temperature/high-current charging triggers thermal runaway faster than high-temperature/high-current scenarios. (3) Staged multi-indicator lithium battery thermal runaway warning signals would be more accurate (first peaks > 0.5 °C/s temperature rise rate + >10 V/s voltage drop rate). These findings collectively demonstrate the imperative for next-generation battery management systems integrating real-time ambient temperature compensation with adaptive C-rate control, fundamentally advancing beyond conventional single-variable thermal regulation strategies. Intelligent adaptation is critical for mitigating thermal runaway risks in LFP battery operations. Full article

Inconsistencies in lithium-ion battery packs pose significant challenges for both electric vehicles and energy storage systems, causing diminished energy utilization and accelerated battery aging. This study investigates the characteristics and aging processes of 32 batteries, creating simulation models for cells and packs based on experimental data. Through a controlled single-variable approach, the decoupled analysis of multi-parameter inconsistencies is carried out. Simulation results demonstrate that parallel-connected packs can maintain charge consistency without the need for external balancing systems, thanks to their self-balancing mechanisms. On the other hand, series-connected packs experience accelerated capacity degradation primarily due to charge inconsistencies linked to differences in Coulombic efficiency (CE) and the initial state of charge (SOC). For packs with minor capacity variations and temperature inconsistencies, a passive balancing current of 0.001 C can effectively eliminate up to 3.8% of capacity loss caused by charge inconsistencies within 15 cycles. Active balancing systems outperform passive ones primarily when there is significant capacity inconsistency. However, for packs that have undergone capacity screening before assembly, both active and passive balancing systems prove to be equally effective. Additionally, inconsistencies in internal resistance have a minimal impact on overall pack capacity but limit the power of both series-connected and parallel-connected packs. These findings offer essential insights for the development of balancing systems within battery management systems. Full article

In this study, we systematically analyzed selective lithium plating on graphite (Gr)–silicon (Si) composite anodes for lithium-ion batteries during fast charging, using electrochemical techniques. To achieve this, half-cells were first constructed with single Gr and Si electrodes, and lithium plating on each electrode was examined at different charging rates. It was observed that lithium plating on both electrodes began at a lower state of charge (SoC) as the charge rate increased. Furthermore, at a given charge rate, lithium plating occurred on the Si electrode at a lower SoC than on the Gr electrode. Based on the experimental findings, the lithium plating behavior of Gr and Si as a function of the charge rate was formulated to investigate the plating behavior of hypothetical composite electrodes with varying Gr–Si ratios. The lithium plating behavior observed on the actual composite electrode was consistent with that predicted from the hypothetical composite electrode, which was simulated using the same Gr–Si ratio based on the behaviors of the individual electrodes. By comparing the results from the single and composite electrodes, it is proposed that lithium plating occurs first on Si and then on Gr at low charge rates, whereas, at high charge rates, it proceeds first on Gr and then on Si. We discuss how to extrapolate the preferential plating signals—namely, plating onto Si at low charge rates and onto Gr at high charge rates—that are not directly evident in the signal from the actual composite electrode. Full article

In polymer-based lithium batteries, polymer electrolytes (PEs) exhibit limited ionic conductivity at room temperature (25 °C). To address this issue, this paper describes a hexagonal-structure-based single-ion conducting gel polymer electrolyte (h-SICGPE) framework with a robust and efficient cross-linked polymer network, applicable to polymer-based batteries even at 25 °C. The proposed cross-linked polymer network backbone of the h-SICGPE, as a semisolid-state thin film type, has the homogeneous honeycomb structure incorporating anion receptor(s) inside each of its hexagonal closed cells and is obtained by cross-linking between trimethylolpropane tris(3-mercaptopropionate) and poly(ethylene glycol) diacrylate in a newly synthesized anion–receptor solution. The excellent structural capability of the h-SICGPE incorporating Li⁺/TFSI⁻ can enhance ionic conductivity and electrochemical stability by suppressing crystallinity and expanding free volume. Further, the anion receptor in its free volume helps to effectively increase the lithium-ion transference number by immobilizing counter-anions. Experimental results demonstrate dramatically superior performance at 25 °C, such as ionic conductivity (2.46 mS cm⁻¹), oxidative stability (4.9 V vs. Li/Li⁺), coulombic efficiency (97.65%), and capacity retention (88.3%). These results confirm the developed h-SICGPE as a promising polymer electrolyte for high-performance polymer-based lithium batteries operable at 25 °C. Full article

Efficient electrocatalysts for the oxygen reduction reaction (ORR) are essential for numerous energy storage and conversion systems, including zinc–air batteries and fuel cells. Cutting-edge Pt/C catalysts remain the most efficient ORR catalysts to date; however, their high cost and inadequate stability impede their use in commercial devices. Recently, transition metal-based electrocatalysts are being pursued as ideal alternatives for cost-effective and efficient materials with a promising future. This review provides an in-depth analysis of the principles, synthesis, and electrocatalytic assessment of noble metal and transition metal-based catalysts derived from diverse gel precursors, including hydrogels, aerogels, xerogels, metal–organic gels, and metal aerogels. Electrocatalysts derived from gel precursors have garnered significant interest due to their superior physicochemical properties, including an exceptionally high surface area, adjustable porosity, adaptability, and scalability. Catalysts obtained from gel precursors offer numerous advantages over conventional catalyst synthesis methods, including the complete utilization of precursors, precise control over surface area and porosity, and uniform distribution of ORR active sites. Among the various types, metal aerogels are distinguished as the superior catalysts, exceeding the Department of Energy’s (DoE) 2025 targets for the mass and specific activities of ORR catalysts. In contrast, hydrogel- and aerogel-derived catalysts excel in terms of ORR activity, specific surface area, and the potential to incorporate high loadings of single-atom catalysts composed of transition metals. Ultimately, we unequivocally categorized the electrocatalysts into high-, moderate-, and low-performance tiers, identifying the most promising catalyst candidate within each gel classification. Concluding insights, future outlooks, and recommendations were provided for the advancement of cost-effective, scalable electrocatalysts derived from gels for fuel cells and zinc–air batteries. Full article

Analytical solutions for the temperature change of a lithium-ion battery during thermal runaway were derived by the equation of linearizing thermal decomposition reaction. This study focuses on the representative temperature of the battery cell (zero-dimensional) during the heating test. First, the thermal decomposition reaction was modeled from DSC tests data of the electrode assuming Arrhenius-type temperature dependency. Subsequently, the reaction was simplified by a linear function of temperature and the analytical solution was derived as the exponential function with respect to time. The validity and applicability of the analytical solution are discussed by comparing it with a one-dimensional thermal runaway simulation. Further study was carried out for multiple batteries in consideration of cell-to-cell propagation of the thermal runaway and the applicability was discussed. As results, the single-cell predictions agreed generally with numerical results, especially with higher heating and lower latent heat. A delay in thermal runaway onset in multiple cells, linearly dependent on inter-cell conductivity, was quantified analytically. Parameter adjustments improved the alignment of analytical and numerical results for multiple cells, enabling quick thermal assessments. While numerical simulation is needed for high accuracy, this analytical framework offers new insights and facilitates initial analyses. Full article

In this work, a battery active grouping equalization control strategy based on model predictive control (MPC) was proposed, which can promote cell consistency, equalization speed and energy loss during the battery equalization process. The dynamic group equalization topology based on reconfigurable circuits can achieve dynamic grouping. Using a battery state observation estimator and the MPC controller, multiple non-adjacent cells can realize simultaneous equalization in a single equalization process. An algorithm is designed to determine the optimal energy transfer direction and the optimal equalization current. The objective function of this algorithm incorporates weight coefficients that represent the relative importance of equalization time and energy loss. Simulation tests are conducted to evaluate the battery pack state-of-charge (SOC) root mean square, average temperature, and equalization time under various weight coefficients. Compared with two other traditional equalization control strategies, the proposed strategy reduces the equalization time by 43.93%, decreases the battery pack SOC variance by 50.18%, and improves the energy transfer efficiency by 0.59%. Full article

This paper presents a novel adaptive cell recombination strategy for balancing lithium-ion battery packs, targeting electric vehicle (EV) applications. The proposed method dynamically adjusts the series–parallel configuration of individual cells based on instantaneous state of charge (SoC) and load demand, without relying on conventional DC-DC converters or passive components. A hardware-efficient switching topology using SPDT (Single Pole Double Throw) switches enables flexible recombination and fault isolation with minimal complexity. The control algorithm, implemented in MATLAB/Simulink, evaluates multiple cell-grouping configurations to optimize balancing speed, energy retention, and operational safety. Simulation results under charging, discharging, and resting conditions demonstrate up to 80% faster balancing compared to sequential methods, with significantly lower component count and minimal energy loss. Validation using Panasonic NCR18650PF cells confirms the model’s real-world applicability. The method offers a scalable, high-speed, and energy-efficient solution for integration into next-generation battery management systems (BMS), achieving performance gains typically reserved for more complex converter-based architectures. Full article