Search Results (1,366)

The zinc–copper (Zn-Cu) voltaic battery is the first battery made in human history, but the Cu²⁺ dissolution issue leads to the reaction’s irreversibility. To tackle this challenge, solid-state electrolytes, ion exchange membranes, and functional electrolytes have been proposed to mitigate the Cu²⁺ dissolution; however, these approaches incur limitations like cell complexity, high cost, and anode corrosion. Herein, we develop a simple yet effective strategy to mitigate Cu²⁺ dissolution and build a rechargeable voltaic battery from cost-effective materials, including commercially available micro-copper powders and non-corrosive zinc acetate electrolyte. Importantly, the near-neutral Zn(Ac)₂ electrolyte provides some amounts of hydroxide and facilitates the Cu₂O/Cu solid–solid conversion reaction, thereby inhibiting the generation of soluble Cu²⁺ ions. As a result, the Zn-Cu battery exhibits a reversible capacity of ~130 mAh g⁻¹, a feasible voltage of 0.87 V, and a stable cycling life over 100 cycles. Our work provides a feasible strategy for developing rechargeable and cost-effective Zn-Cu batteries. Full article

The accelerated global transition toward eco-friendly mobility has necessitated robust decarbonization measures across the maritime sector, with battery-powered electric propulsion ships emerging as a promising alternative. Accordingly, the applicability of silicon carbide (SiC)-based technology to propulsion inverters, a key component of such vessels, is currently under investigation. Although life cycle assessment (LCA) studies comparing conventional silicon (Si)-based and SiC-based inverters have been conducted previously, these analyses neglect realistic operating profiles and load fluctuations, limiting their applicability. Furthermore, life cycle cost assessment (LCCA) integrating real-world operating conditions has rarely been addressed. To address these gaps, this study conducted a comparative LCA and LCCA of Si IGBT and SiC MOSFET inverters for marine electric propulsion systems across three vessel types: a cruise ship, a passenger and car ship, and a recreational boat, incorporating real-world load profiles to evaluate global warming potential (GWP), fossil depletion (FD), and cumulative energy demand (CED). The static LCA results showed negligible differences between inverter types, contributing less than 1% to total impacts. The dynamic LCA demonstrated that SiC MOSFET inverters reduced environmental impacts by approximately 57%, 52%, and 34% for cruise ships, passenger and car ships, and recreational boats, respectively. Despite a 40% higher initial investment cost, SiC inverters achieved payback periods well within vessel lifetimes across all vessel types. These findings support SiC inverters as a sustainable and economically viable solution for ship electrification. Full article

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

Power batteries used in electric-powered vessels, new-energy tractors or construction machinery typically require prolonged, continuous operation at high power levels, which can lead to significant heat buildup and pose serious threats to battery safety, cycle life, and operational stability. Traditional air-cooled and liquid-cooled systems struggle to meet the requirements for efficient heat dissipation under heavy loads. Phase change materials (PCMs) are ideal for passive battery thermal management due to their high latent heat but are severely limited by low thermal conductivity and liquid leakage. In this study, nitrogen-doped carbon nanotubes@SiO₂ (PNT@SiO₂) were synthesized and further fabricated into oriented porous aerogels by directional freeze-drying using cellulose-based materials as the skeleton. Polyethylene glycol-8000 (PEG-8000) was loaded via vacuum impregnation to obtain the PSAP composite PCM. The optimized composite exhibits a thermal conductivity of 0.93 W/m·K, 3.2 times that of pure PEG, with 96% PEG loading and a phase change enthalpy of 158 J/g. Battery thermal management tests demonstrate its excellent temperature control and heat suppression performance. This study provides a high-performance and feasible thermal management solution for power batteries used in relevant fields. Full article

The increasing global demand for high-performance, reliable, and sustainable energy storage systems has accelerated the development of supercapacitors as technologies capable of bridging the performance gap between conventional capacitors and batteries. Supercapacitors combine rapid charge–discharge capability, high power density, and exceptional cycle life through charge storage mechanisms based on ion adsorption and fast surface redox reactions at the electrode–electrolyte interface. This review examines the fundamental operating principles, charge storage mechanisms, electrode materials, mechanical and functional properties, fabrication methods, and engineering applications of modern supercapacitors. Carbon-based materials, metal oxides, conducting polymers, MXenes, sulfides, nitrides, borides, and emerging hybrid systems are critically compared in terms of capacitance, energy density, cycling stability, and mechanical robustness. Additionally, recent advances in scalable manufacturing approaches, including thin-film deposition and printing technologies, are discussed alongside key challenges such as limited energy density, interfacial instability, mechanical degradation, electrolyte compatibility, and large-scale processing. By consolidating recent developments across materials science, electrochemistry, and device engineering, this review provides insight into future directions for next-generation high-performance supercapacitor technologies. Full article

Aqueous zinc-ion batteries (AZIBs) are promising for grid-scale storage due to their safety, low cost, and environmental benignity. However, water-dipole enrichment in the inner Helmholtz plane (IHP) of Zn anodes triggers hydrogen evolution, corrosion, and dendrites, limiting cycle life. We report a trace “ionic liquid–polar solvent coupling” strategy: adding only 0.01 M EMIMBF₄ and 0.03 M DMSO to 2 M ZnSO₄ electrolyte. Hydrophobic EMIM⁺ adsorbs on the IHP to expel interfacial water, while BF₄⁻ enters the primary solvation shell and DMSO penetrates both first and second shells of Zn²⁺, forming a water-deficient coordination environment. This interfacial–solvation synergy suppresses parasitic reactions and directs preferentially oriented Zn deposition exclusively along the (101) facet, enabling dense vertical plating and in situ formation of a compact, inorganic-rich SEI (ZnCO₃–ZnSO₃–Zn(OH)₂). Consequently, Zn||Zn cells cycle stably for >5362 h at 1 mA cm⁻²/1 mAh cm⁻²; Zn||Cu cells achieve 1300 cycles with 99.8% average Coulombic efficiency; and Zn||V₂O₅ full cells retain 326.4 mAh g⁻¹ after 500 cycles. This work shows that minimal additive loading can simultaneously engineer the electrode–electrolyte interface and crystallographic deposition pathway, offering a simple yet robust design for ultra-stable AZIBs. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

Battery formation is important for ensuring the quality and service life of cells in lithium-ion battery (LIB) production. During the formation process, fault cells, such as low open-circuit voltage cells, are screened offline after the charging stage since, in most formation protocols, the online screening process is absent. This can lead to energy waste and extend the rework cycle of the fault cells in the LIB formation process. To address this problem, this paper considers the online fault cell screening problem, the formation pre-screening, in the LIB formation process as a classification task and proposes a data-driven model based on incomplete time-series data for formation pre-screening. First, the proposed model transforms segments of the incomplete charging voltage curve (ICVC) of the LIB as tokens, which is a more compact and less redundant data representation of the ICVC. Then, the attention-based feature encoder, Transformer encoder (TE), captures the dependency between tokens to extract features for the formation pre-screening. Finally, a task-specified decoder, feature enhance decoder (FED), is used to screen out fault cells online. The effectiveness of the proposed model is verified using real-world production data collected from a specific type of 18,650 lithium-ion cell under one formation protocol. The results on the investigated industrial dataset show that the proposed model achieves an accuracy of 98.73% and a miss rate of 1.92% during formation pre-screening, which is a 2.49% improvement in accuracy and an 8.98% decrease in miss rate compared with the deep residual network baseline. These results demonstrate the feasibility of using incomplete formation-stage voltage curves for online fault-cell pre-screening, which has the potential to reduce unnecessary charging and rework time in LIB production. Full article

The recast Energy Performance of Buildings Directive (EPBD) accelerates the transition from nearly zero-energy buildings (nZEBs) to zero-emission buildings (ZEBs), requiring solar readiness and life-cycle Global Warming Potential (GWP) disclosure. Yet operational performance, future-climate adaptation and whole-life carbon (WLC) are still often assessed separately, limiting actionable evidence for residential ZEB design in northern climates. This study provides an integrated design-decision framework coupling annual IDA-ICE simulations under five weather scenarios, including Urban Heat Island (UHI)-adjusted present and 2080 RCP8.5 + UHI files, with an EN 15978/Level(s)-based WLC assessment in One Click LCA for twelve design cases of a Lithuanian dwelling. For the PV-equipped baseline, heating electricity decreases by 24% and cooling increases by 31% from present conditions to 2080 RCP8.5 + UHI. External shading and night purge provide the strongest annual cooling and operative-temperature-exceedance reductions. The ZEB baseline reduces WLC by 19.0% relative to A0; the biogenic-insulation green-roof case gives the lowest non-storage WLC (−25.2%); and battery-assisted cases provide the largest reductions under the static B6 electricity factor (up to −52.1%). The findings provide case-study evidence that EPBD-aligned residential ZEB design should evaluate passive cooling, PV/storage and material choices jointly, rather than sequentially, when developing future performance thresholds and design guidance. Full article

Thermal management is critical for the safety, performance, and life cycle of lithium-ion (Li-ion) batteries. This study aims to determine the optimum settings and contribution levels of key parameters affecting the operating temperature of a three-dimensional (3D) thermal model of a cylindrical Li-ion battery. A Taguchi L9 orthogonal array was designed with four: (A) base fluid and (B) Al₂O₃volume fraction (Φ-Al₂O₃) of the nanofluid coolant, (C) battery–battery distance, and (D) inlet temperature (T_inlet), each varied on 3-level control factors. To minimize the maximum battery temperature (T_max), the “smaller-is-better” signal-to-noise (S/N) ratio approach and Analysis of Variance (ANOVA) were applied. The S/N analysis and ANOVA revealed that the base fluid (A: 44.96%) and T_inlet (D: 36.00%) were the most dominant factors influencing the T_max. The optimal design identified by the Taguchi method (A3-B3-C3-D1) successfully reduced the T_max to 33.5 °C, a 29.0 °C reduction compared with the initial air-cooled reference model (62.5 °C). Furthermore, the maximum temperature rise during the 2100 s operation was reduced by approximately 62%. This optimal T_max of 33.5 °C was even lower than the best result in the L9 array (35.5 °C), validating the strong predictive capability of the method. Full article

Polymer-engineered MXene composites have emerged as a versatile materials platform for electrochemical energy storage, offering a means to address key limitations associated with ion transport, structural instability, and interfacial reactivity. This review provides a unified perspective on how polymer integration modifies the structure–transport–stability relationships of MXene-based systems across Na-ion batteries, aqueous Zn-ion batteries, and supercapacitors. In Na-ion systems, polymer-mediated interlayer engineering and porosity control improve ion accessibility and mitigate diffusion limitations arising from the large ionic radius of Na⁺. In aqueous Zn-ion systems, polymer electrolytes and interfacial layers regulate Zn²⁺ solvation and deposition behavior, suppressing dendritic growth and parasitic reactions. In supercapacitors, polymer–MXene hybrids establish coupled ionic–electronic transport pathways and mechanically compliant architectures, enabling stable electrochemical performance under high-rate and deformable conditions. Particular emphasis is placed on the underlying mechanisms responsible for suppressing oxidation, preserving structural integrity, and extending cycle life, including interfacial passivation, desolvation regulation, and structural confinement. These coupled effects govern long-term electrochemical stability across different energy storage systems. Finally, recent advances in operando characterization, data-driven materials design, and scalable processing are discussed in the context of future development. By linking material design strategies to fundamental mechanisms, this review outlines a coherent framework for the rational development of polymer–MXene composites toward practical energy storage applications. Full article

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions. Full article

Emission factor matching is the most critical step in product carbon footprint (PCF) accounting based on life cycle assessment (LCA). However, this step has long been hindered by several major challenges: a lack of standardization, overreliance on expert judgment, inconsistencies in raw data, and complex processing workflows. To address these issues, this study proposes an automated emission factor matching algorithm that combines large language models (LLMs) with semantic retrieval. The algorithm proceeds in two stages: first, an LLM identifies the reference product within the LCA database; then, an embedding model retrieves the most relevant emission factors through high-precision matching. Depending on practical requirements, the algorithm can either automatically select a single best-match factor or rank multiple best-match candidates in descending order of match precision to assist LCA experts in decision-making. We evaluate the algorithm on eight industrial products—tires, cement, ammonium phosphate, wood products, textiles, electronics and electrical appliances, steel, and lithium batteries—using the Ecoinvent 3.10 LCA database. Results demonstrate that the algorithm achieves high precision and low processing latency, significantly outperforming manual expert screening. These findings confirm that the proposed algorithm enables efficient and accurate emission factor matching, thereby providing a reliable technical solution and decision-making pathway for large-scale, automated PCF accounting. Full article

Reliable estimation of battery remaining useful life (RUL) becomes difficult when the capacity trajectory contains regenerative rebounds, short-term oscillations, and long-range temporal dependence. To address this problem, an adaptive decomposition and hybrid deep-learning framework is proposed. First, the phototropic growth algorithm (PGA) is used to tune variational mode decomposition (VMD), allowing the capacity series to be separated into low-frequency trend information and high-frequency fluctuation information so that the influence of regeneration and noise is weakened. Next, a component-level predictor combining a temporal convolutional network (TCN), an attention mechanism (AM), and a Transformer is constructed. In this architecture, TCN learns multi-scale local features, AM enhances salient degradation cues, and the Transformer captures global long-horizon dependencies. To deduce the future capacity degradation path and the associated RUL, these estimated elements are synthesized. Results on the NASA, CALCE, and BIT datasets verify the effectiveness of the proposed framework. On NASA dataset, the average root mean square error (RMSE), mean absolute error (MAE), and absolute error (AE) reach 0.0123 Ah, 0.0073 Ah, and 0.5 cycles, respectively, improving on the strongest baseline by 11.9%, 19.7%, and 50.0%. On CALCE dataset, the corresponding values are 0.00695 Ah, 0.00499 Ah, and 1.75 cycles, and all R² values are higher than 0.9989, indicating strong accuracy and robustness in the presence of complex regeneration behavior. Supplementary BIT validation on three higher-capacity cells further achieves average RMSE, MAE, and AE of 0.01201 Ah, 0.00771 Ah, and 1.0 cycle, respectively. Full article

Battery-based electrification is increasingly recognised as a key pathway for reducing greenhouse-gas emissions in maritime transport, particularly for vessel segments characterised with short, predictable operation profiles. To ensure an environmentally sustainable transition, it is essential to quantify the potential environmental benefits of these solutions. Life Cycle Assessment (LCA), standardised by ISO 14040 and ISO 14044, is the internationally recognised methodology for evaluating environmental impacts across the entire life cycle and for consistently comparing options providing the same function. This study presents a methodological review of LCA applications to battery-based ship electrification, with the objective of analysing key assumptions, comparability issues, and limitations across the existing literature. A systematic review was conducted on 24 studies, focusing on core methodological aspects, including product system definition, functional unit selection, system boundaries, life cycle inventory modelling, and impact assessment methods, while considering contextual elements such as fleet segmentation and propulsion configurations to support the interpretation of methodological choices. The analysis reveals significant methodological heterogeneity across studies, particularly in product-system definitions, functional unit selection, modelling detail, and impact category coverage, which limits cross-study comparability. This review also highlights a strong concentration of applications on short-route passenger ferries, while other vessel categories remain underrepresented, further constraining the generalisability of the findings. Although a direct quantitative comparison of results is not methodologically appropriate due to this heterogeneity, climate change mitigation consistently emerges as a key benefit across the analysed studies. At the same time, the multi-impact perspective of LCA highlights relevant trade-offs related to material use, toxicity, and resource depletion. Overall, the findings underline the need for more harmonised methodological approaches and a holistic life cycle perspective to support robust and comparable environmental assessments as battery-based solutions expand within the maritime sector. This review provides a structured interpretation of methodological variability and identifies priorities for future LCA applications. Full article