Zinc‐ion hybrid supercapacitors (ZIHSCs) are an emerging class of predominantly aqueous‐based energy‐storage devices that bridge supercapacitors and rechargeable batteries by coupling fast capacitive processes with zinc‐based Faradaic reactions. While aqueous electrolytes are the most widely employed, recent developments have also extended to non‐aqueous electrolyte systems. Their intrinsic safety, low cost, and high‐rate capability make them attractive for wearable, microelectronic, and autonomous systems. However, their practical implementation remains hindered by zinc anode instability, kinetic mismatch between capacitive and battery‐type cathodes, electrolyte trade‐offs, and scalability of complex device architectures. Herein, we address the strategies to overcome these challenges examined from a mechanism‐guided and system‐level perspective. Emerging device concepts including flexible, micro‐ZIHSCs, self‐healing systems and hybrid air‐assisted configurations are discussed as viable pathways to overcome intrinsic material limitations. Finally, key knowledge gaps, standardization needs, and future research directions are outlined to accelerate the translation of ZIHSCs from laboratory studies to application‐relevant energy‐storage technologies.
With the rapid development of high‐power and high‐density electrochemical energy storage stations, conventional liquid cooling plates (LCPs) with serpentine channels suffer from long flow paths, large temperature differences, high flow resistance, and excessive energy consumption, which severely restrict the safety, efficiency, and cycle life of lithium iron phosphate batteries. Herein, we propose a nested dual‐inlet‐dual‐outlet serpentine channel LCP (NSLCP). The new structure shortens the single‐path flow length of coolant, improves flow distribution uniformity, and eliminates excessive coolant temperature rise along the flow path. Thus, the heat dissipation efficiency, temperature uniformity, and system energy consumption are greatly enhanced. Results show that the thermal management system achieves significantly better overall performance with a channel width of 8 mm and a coolant mass flow rate of 2 g·s −1 , compared with conventional structures. In the full operating range of 25–45 °C ambient temperatures and 0.5–2 C discharge rates, the maximum temperature and temperature difference of the battery module are controlled below 28 and 2 °C, respectively. The proposed LCP provides an innovative and practical solution for high‐performance thermal management systems in large‐scale energy storage stations.
Accurate state‐of‐charge (SOC) estimation is essential for ensuring the safety and efficiency of lithium‐ion battery systems under complex operating conditions. To address limitations in convergence speed and estimation accuracy, this paper proposes an improved Latin Hypercube Tuna Swarm Optimization (LHTSO) algorithm. The method enhances population initialization via Latin hypercube sampling, incorporates a stage‐adaptive search strategy, and introduces an elite‐guidance mechanism to improve global optimization performance. An integrated LHTSO‐BP‐UKF framework is further developed for SOC estimation. Experimental validation is conducted under multiple driving cycles (Dynamic Stress Test (DST), New European Driving Cycle (NEDC), Federal Test Procedure (FTP), Urban Dynamometer Driving Schedule (UDDS)) and a wide temperature range (−10 to 40 °C). Results demonstrate that the proposed method consistently outperforms conventional Unscented Kalman Filter (UKF) and its variants. Under the challenging DST condition at 25 °C, the proportion of samples with estimation error exceeding 1% is reduced from 80.20% to 4.10%, achieving a 96.55% relative improvement. Moreover, the method maintains stable and bounded estimation under low‐temperature conditions. These results confirm the robustness, generalization capability, and practical applicability of the proposed approach.
Accurate, real‐time, and noninvasive monitoring of internal battery temperature is essential for understanding thermal behavior and enabling early thermal runaway warning in lithium‐ion batteries (LIBs). Although simplified thermal prediction models have been widely investigated for internal temperature estimation, their validation is predominantly conducted under room‐temperature conditions, and their reliability under elevated ambient temperatures remains insufficiently explored. In this study, the robustness and generalization capability of a simplified thermal prediction model are systematically validated under different ambient temperatures using fiber Bragg grating (FBG) sensing technology. To obtain operando internal temperature data, FBG sensors are embedded into the central void of commercial 18650 LIBs, while additional sensors simultaneously monitor the battery surface and ambient temperatures. The prediction model is evaluated under different ambient temperatures and charging/discharging rates. The predicted internal temperatures show good agreement with the experimentally measured values across all operating conditions, demonstrating high reliability and robustness under complex thermal boundaries and elevated‐temperature environments. The proposed validation framework provides experimental support for the applicability of surface temperature‐based internal temperature prediction models under realistic operating conditions.
Carbon materials play an indispensable role in lithium‐ion batteries (LIBs) due to their excellent electrical conductivity, structural tunability, chemical stability, and abundance. This paper provides a systematic review of the applications of various carbon materials in LIBs. It focuses on the characteristics and applications of graphite, amorphous carbon, graphene, and carbon nanotubes as battery materials, and notes that the carbon materials currently in use have a significant impact on battery performance. The paper also identifies key challenges in the application of carbon materials in LIBs. This review aims to provide a systematic theoretical framework for the design of high‐performance carbon‐based materials for LIBs.
The global transition from fossil fuels to carbon‐free energy has accelerated interest in renewable fuels such as hydrogen and ammonia (NH 3 ). Ammonia has emerged as a promising carbon‐free energy carrier because it can be liquefied at approximately 1 MPa under ambient conditions, has a high‐octane number of about 110, and contains 17.6 wt% hydrogen, making it attractive for large‐scale energy storage and transportation. This article focuses on earlier research and recent studies on technical developments in direct injection (DI) of NH 3 ‐assisted combustion. Focus on the combustion characteristics of NH 3 DI in ICEs, as well as the spray characteristics, safety, and handling of NH 3 . This review paper develops the fundamentals of the mechanism of NH 3 combustion technology, providing new insights into the application of NH 3 in ICEs. Although NH 3 possesses favorable storage characteristics, its application in ICEs is challenged by a high autoignition temperature ( ∼ 650 °C), low laminar flame speed ( ∼ 7 cm/s), and lower heating value (18.8 MJ/kg), which contribute to combustion instability, NO x formation, and NH 3 slip. The review also explains the fundamental combustion mechanisms of ammonia and identifies key research gaps and future directions for developing efficient, low‐emission ammonia‐fueled internal combustion engines, supporting the transition towards sustainable and carbon‐free transportation.
Carbon monoxide (CO) in gasification‐derived syngas reduces hydrogen quality and can deactivate downstream catalysts. This study investigates Fe‐based catalysts with varying Cu, Al, and Ti molar ratios for CO removal via the water–gas shift reaction. Catalytic performance was evaluated at 250–300 °C, gas hourly space velocities of 3000–10 000 h −1 , and steam/CO ratios of 1:1–3:1. Among the catalysts studied, Fe 1 Cu 3 Al 10 exhibited the best performance, achieving 96% CO conversion, CO 2 selectivity above 90%, and an H 2 yield of 40.8%. Catalyst characterization using X‐ray diffraction, scanning electron microscopy, N 2 adsorption–desorption, X‐ray photoelectron spectroscopy, H 2 ‐temperature‐programmed reduction, and CO temperature‐programmed desorption revealed that Ti incorporation altered catalyst crystallinity and morphology, whereas Fe 1 Cu 3 Al 10 maintained good structural stability after reaction. Under integrated gasification conditions, Fe 1 Cu 3 Al 10 reduced the CO concentration to 2.1% while increasing the H 2 yield to 41%. These findings identify Fe 1 Cu 3 Al 10 as a promising catalyst for CO removal and hydrogen enrichment in gasification‐derived syngas upgrading systems.
The accelerating transition toward carbon‐neutral energy systems drives the large‐scale integration of renewable generation, reshaping modern power grids. As a key enabler of long‐distance renewable energy transmission, Voltage Source Converter‐based High‐Voltage Direct Current systems have become essential infrastructure. However, line protection remains a critical bottleneck due to ultrafast fault transients, strong converter–line coupling, and sensitivity under weak‐grid conditions. This review identifies three intrinsic paradoxes constraining existing methods: the speed–reliability trade‐off, the laboratory–field generalization gap, and the intelligence–explainability tension. Conventional traveling‐wave and differential schemes struggle to maintain robustness in multi‐terminal and renewable‐dominated scenarios. Data‐driven approaches, including machine learning and deep learning, improve adaptability but rely on statistical correlations, leading to limited interpretability and reduced robustness under unseen conditions. To address these challenges, this review discusses an Large Language Model–Knowledge Graph‐based decision‐support paradigm that supports protection supervision, logic validation, adaptive setting review, and post‐fault diagnosis, while retaining primary trip initiation within deterministic protection. By combining mechanism‐oriented representation with semantic reasoning, this framework supports interpretable, consistent, and adaptive supervisory protection functions, paving the way for next‐generation DC systems in renewable‐dominated grids.
The increasing demand for sustainable energy storage has highlighted the limitations of lithium‐ion batteries, including resource depletion, thermal instability, electrode degradation, and limited recyclability. These challenges have stimulated interest in microbial fuel cells (MFCs), which simultaneously generate electricity and treat organic waste through the metabolic activity of electroactive microorganisms. However, their large‐scale application is hindered by inefficient extracellular electron transfer (EET), unstable biofilms, and high interfacial resistance. This review summarizes recent advances in electroactive bacteria and polymeric materials for improving MFC performance. Particular emphasis is placed on conductive polymers, biopolymers, polymer composites, and polymer‐based membranes that enhance microbial adhesion, biofilm stability, and charge transport. The review highlights how polymeric materials facilitate direct electron transfer (DET) by forming conductive bridges between bacteria and electrodes, while mediated electron transfer (MET) is enhanced through redox‐active polymers and electron‐shuttling molecules. Recent developments in polymer–carbon and polymer–metal oxide hybrid materials are also discussed for their roles in improving conductivity, durability, and power generation. Overall, this review provides insights into polymer‐assisted interface engineering for developing efficient, stable, and scalable MFCs for sustainable bioenergy applications.
Due to significant electrothermal coupling, solid‐state lithium‐ion batteries pose challenges for accurately estimating battery state in battery management systems. This paper proposes a Dual‐Extended Kalman Filter (DEKF) framework for joint estimation of state of charge (SOC) and state of temperature (SOT). A second‐order equivalent circuit model and a two‐state thermal model are established, with parameters identified via particle swarm optimization and recursive least squares. An electrothermal coupling model is then built, and a DEKF‐based estimator is designed to simultaneously estimate SOC, surface temperature, and core temperature. Validation under four typical dynamic driving cycles shows that for SOC estimation, the mean absolute error (MAE), and root mean square error (RMSE) remain below 0.75% and 0.85%, respectively. For temperature estimation, the MAE for surface and core temperatures was kept within 0.5924 and 0.6293 °C, respectively, while the RMSE was kept within 0.7076 and 0.7611 °C, respectively. This study proposes a feasible method for state estimation in solid‐state batteries.
This study establishes a three‐dimensional, nonisothermal, two‐phase flow anion exchange membrane electrolyzer cell (AEMEC) numerical model that includes a microporous layer (MPL). The effects of flow field design, operating parameters, and key component structural parameters on the performance of electrolytic cells were studied. Results indicate that the serpentine flow channel, with its “piston flow” effect, can achieve more uniform species distribution and temperature field compared to parallel flow channels. It exhibits superior electrochemical performance at high operating voltage with a current density increase of approximately 7.8% at 2.3 V. Parameter analysis shows that increasing operating voltage and inlet water temperature can significantly improve gas production rate. For MPLs, there exists an optimal range of MPL parameters (porosity ε = 0.5–0.6, thickness d = 25–35 µm) that can achieve the best balance between mass transfer resistance and conductivity. In addition, reducing the thickness of the AEM can effectively reduce the Ohmic loss. This study provides theoretical basis and data support for the design and optimization of key components of high‐performance AEMEC.
One of the most difficult challenges of this century is the quick conversion of anthropogenic carbon dioxide (CO 2 ) into value‐added solar chemicals/fuels. The best method for transforming CO 2 into beneficial compounds like glucose and oxygen is natural photosynthesis. In this context, herein, an artificial photosynthesis over ultrasonically engineered SCN@Fe 3 O 4 magnetic nanorods was designed for a selective carboxylation reaction (CO 2 ‐to‐formic acid production). The SCN@Fe 3 O 4 magnetic nanorods were employed as an efficient visible light–responsive photocatalyst for the regeneration of NADH (nicotinamide adenine dinucleotide hydrogenase) from NAD + (nicotinamide adenine dinucleotide), where NADH serves as the biologically active hydrogen carrier. Under visible light irradiation, the optimized photocatalytic system achieved an in situ NADH regeneration (74.49%), which subsequently promoted the enzymatic reduction of CO 2 ‐to‐HCOOH using formate dehydrogenase (FDH). The system produced HCOOH (170.70 µM) under optimized conditions in a phosphate buffer medium containing NAD + , Rh‐based electron mediator, and triethanolamine (TEOA) as a sacrificial agent. The greater yield and high efficiency attained here suggest a sustainable and high‐throughput approach to useful enzymatic applications.
CO 2 methanation not only reduces CO 2 emissions but also enables the efficient utilization of carbon resources. The development of highly efficient and stable catalysts is crucial for the application of this reaction. CeO 2 supported Ni catalyst (NiCe) showed high potential for CO 2 methanation. In this paper, we prepared Ni 3 Ce 1 catalysts by three preparation methods: urea hydrothermal method (UHT), coprecipitation method (CP), and sol–gel method (SG), to establish a rational design framework by quantitatively decoupling metal–support interactions (MSI). By comparing the effects of preparation parameters including hydrothermal temperature, precipitation pH, and complexing agent‐to‐metal ion ratios, on catalyst structure and CO 2 methanation performance, the optimal catalysts for each method were identified. Among them, the catalyst Ni 3 Ce 1 ‐CP‐12 prepared by CP, exhibited the best performance, achieving CO 2 conversion as high as 84.6% at 300 °C with good selectivity. This is attributed to its enhanced reducibility, increased surface oxygen vacancies, suitable distribution of basic sites and high surface Ni 0 sites.
The substantial energy consumption of conventional cold chain systems presents a significant challenge to global sustainability goals. While phase change materials (PCMs) offer promising thermal regulation solutions, they typically suffer from rigidity, leakage risks, and corrosiveness, especially in subzero applications. Composite phase change gels (CPCMGs) emerge as an innovative alternative, overcoming rigidity and leakage issues through their inherent flexibility, water retention properties, and salt compatibility. However, material corrosiveness and limited stability continue to hinder practical implementation. This study developed two low‐leakage CPCMGs with phase transition temperatures of −21.6 and −21.5 °C. These cryogenic gels demonstrate negligible supercooling, exceptional leakage resistance, and high latent heat values of 246.6 and 243.9 J/g. After 500 thermal cycles, the CPCMGs maintained excellent stability and thermophysical properties. Comprehensive corrosion evaluation through 60‐day immersion tests on six common metals, employing mass loss analysis and surface morphology characterization, confirmed the gel’s corrosion resistance. The most susceptible material, carbon steel (Q235), exhibited a minimal corrosion rate of only 0.02698 g/(m 2 day) in CPCMG. This breakthrough resolves the persistent challenge of balancing stability, corrosion resistance, and cost in low‐temperature PCMG development, while advancing sustainable cold chain applications for aquatic product preservation and biomedical sample transport.
Uncontrolled Li dendrite growth and interfacial side reactions severely limited the performance of the Li metal anode. Herein, we present a simple and effective strategy to construct a firmly adhered Ga–Sn layer on Li metal to form a Ga–Sn/Li composite anode. By harnessing the self‐healing function of Ga‐based liquid metals and strong Sn–Li interaction, the interfacial layer homogenizes Li + flux, promotes uniform Li deposition, suppresses side reactions, and accelerates Li + transport kinetics. Consequently, the Ga–Sn/Li symmetric cells achieve an extended cycle lifetime of 1200 h at 1 mA cm −2 and 1 mAh cm −2 . The Ga–Sn/Li||NCM811 full cells maintain a capacity of more than 125.7 mAh g −1 after 200 cycles at a rate of 0.5 C, representing a capacity retention rate of 86.7%. This study proposes a simple way for the practical application of Li metal anodes and demonstrates the potential of the Ga–Sn alloy interface.
Water flooding severely degrades the performance and lifespan of proton‐exchange membrane fuel cells (PEMFCs), impeding their large‐scale application. A water flooding diagnostic approach is proposed based on the uniformity of current density distribution. This is achieved by employing a dead‐ended PEMFC equipped with a 36‐subarea current density detection plate for high‐accuracy positioning. Experimental results demonstrate a positive correlation between the standard deviation (SD) of current density distribution and cell water content, following the linear equation y = 0.093 x + 59.22 ( R 2 = 0.955). Comparative tests show that constant voltage mode stabilizes water content at 78% ± 2% and SD at 380 ± 5 mA/cm 2 to suppress flooding, whereas constant current mode leads to excessive water accumulation (92.2%) and a sharp SD surge to 619.0 mA/cm 2 , causing voltage collapse. Temperature tests (30–60 °C) confirm that elevated temperatures increase the visible water content inside the cell due to the condensation of water vapor, thereby enhancing current density inhomogeneity. This study establishes SD as a quantitative indicator for flooding severity, enabling precise localization of local flooding and providing critical guidance for PEMFC water management optimization.
To address the conflict between charging speed, safety, and lifespan in lithium‐ion batteries, this paper proposes a physics‐consistent and deployable deep reinforcement learning (DRL) intelligent charging strategy. Existing DRL studies often overlook the algebraic loop coupling between power and voltage, leading to nonphysical oscillations in control signals. To overcome this, we construct a high‐fidelity digital twin environment with a closed‐loop current analytical solution, ensuring physical authenticity during training. Building on this, an improved Soft Actor‐Critic (SAC) framework is proposed, featuring a multidimensional normalized reward function for multi‐objective optimization and integrating Neural Architecture Search (NAS), pruning, and INT8 quantization for model lightweighting. Experiments across four representative scenarios (standard, low‐temperature, high‐temperature, and aged battery conditions) demonstrate that, compared to baselines like CC–CV and DDPG, the proposed strategy reduces charging time by 12.5% while effectively regulating peak temperature within scenario‐adaptive safety limits and eliminating sawtooth oscillations. Statistical validation ( p < 0.01) across 200 independent evaluation runs confirms the significance of the reported improvements. Furthermore, robustness analysis under practical uncertainties including sensor noise and control latency demonstrates the policy's resilience for real‐world deployment, and resource evaluation confirms its feasibility for embedded Battery Management Systems (BMS).
Proton exchange membrane fuel cells are often constrained by nonuniform reactant distribution, liquid–water accumulation, and localized heat build‐up during full‐scale single‐cell operation. To clarify the coupled effects of mass transfer, water management, and thermal regulation, a three‐dimensional multiphase numerical model incorporating an irregular bump‐type gas distribution zone (GDZ) is developed for full‐plate‐scale flow‐field analysis. Reactant‐transport analysis indicates that anode‐side hydrogen is progressively consumed along the flow direction, while concentration differences among adjacent channels remain relatively small. In contrast, cathode‐side oxygen shows more pronounced lateral maldistribution because of its lower effective concentration, greater diffusion resistance, and product‐water accumulation, with the concentration deviation increasing from 2.45% at 20 mm to 5.45% at 80 mm. Liquid water accumulates downstream and preferentially stagnates in low‐velocity channels and the outlet GDZ, indicating weakened drainage and intensified oxygen starvation. Forced‐convection cooling improves output performance, whereas a realistic cooling‐water boundary reduces net power density by only about 0.5%. Moreover, coflow of coolant and oxygen better coordinates heat removal and outlet drainage, increasing the cell voltage to 0.6013 V, with a maximum improvement of 0.69%.
Effective thermal management is essential for ensuring the performance and safety of lithium‐ion batteries. In this study, a composite phase change material (CPCM)‐liquid cooling coupling battery thermal management system (BTMS) is proposed to suppress heat accumulation and reduce thermal gradients in 18 650 battery packs under high‐rate discharge conditions. An electrochemical–thermal coupling model was established and validated through experiments and multiphysics simulations to predict the spatiotemporal heat generation behavior of the battery. Liquid cooling plates were arranged on the upper and lower surfaces of the module, and the effects of cold plate structure, inlet/outlet configuration, turbulence column radius, channel height, and coolant inlet velocity on thermal performance and pressure drop were systematically investigated. To further improve temperature uniformity, a CPCM composed of calcium chloride hexahydrate and expanded graphite was introduced. Optimal Latin Hypercube Sampling (OLHS), NSGA‐III, and entropy‐weighted TOPSIS were employed for multi‐objective optimization. Under an ambient temperature of 25 °C and a 3C discharge rate, the optimized system achieved a maximum temperature ( T max ) of 29.49 °C, a temperature difference (Δ T ) of 2.92 °C, and a pressure drop (Δ P ) of 29.83 Pa, reduced by 3.8%, 14.4%, and 74.3%, respectively, compared with the initial BTMS.
Lithium‐ion battery thermal management systems (BTMS) are critical for safety, longevity, and performance, yet existing reviews typically treat material innovations, control algorithms, and sustainability as separate topics. This article presents a systematic review that integrates these three dimensions and critically examines the inherent contradictions within each. Analyzing literature from 2010 to 2024, we identify two key trade‐offs: (i) between thermal conductivity and latent heat in phase change materials (PCMs) and (ii) between “black‐box” speed and physical interpretability in artificial intelligence (AI)‐driven control. We show that while high‐thermal‐conductivity composites and AI‐based predictive models can accelerate thermal runaway prediction by orders of magnitude, practical deployment remains limited by unresolved material incompatibilities, the lack of standardized multiphysics modeling protocols, and insufficient experimental validation of virtual data. Unlike prior reviews that focus on single technologies, this work provides an integrated, critical roadmap that bridges material science, control theory, and environmental sustainability. Future directions emphasize balancing property trade‐offs (e.g., gradient conductivity designs, multifunctional polymer skeletons), validating AI models with physical benchmarks, and designing recyclable, bio‐based BTMS materials to meet carbon neutrality goals.
