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Sustaining the driving force:Constant-voltage charging unlocks rapid and efficient Li-CO2 batteries 认领 引用
1
作者 Wenqing Ma Mingjuan Gao +10 位作者 Jianping Ma Haiyang Gao Xianhong Li Siyu Liu Tianzhen Jian Yuyan Wang Longhua Ding Aizhu Wang Lihan Cai Xin Yu Hong Liu 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第6期680-693,I0016,共14页
The large charging overpotential and sluggish kinetics of Li-CO2 batteries originate from both the high energy barrier for decomposing insulating discharge products and the mismatch between constantcurrent charging... The large charging overpotential and sluggish kinetics of Li-CO2 batteries originate from both the high energy barrier for decomposing insulating discharge products and the mismatch between constantcurrent charging and dynamic reaction kinetics.While cathode catalyst design dominates research,charging protocol optimization remains a critically underexplored avenue.Herein,we demonstrate that a constant-voltage(CV)charging strategy,as opposed to the conventional constant-current(CC)mode,dramatically enhances the charging rate,energy efficiency,and cyclability.Using a Ru@rGO cathode,the 4.0 V CV-charging protocol CV charging is identified as optimal,enabling rapid charging(~13.6 min),high energy efficiency of 72%,and stable cycling over 200 cycles.In-situ differential electrochemical mass spectrometry reveals rapid and efficient CO2 evolution kinetics under the 4.0 V CVcharging mode.Combined with X-ray photoelectron spectroscopy and Raman mapping,it is shown that this protocol ensures near-complete decomposition of both Li2 CO3 and Li2C2O4,whereas CC-charging and suboptimal CV-charging protocols leave significant residues or induce severe parasitic reactions.This work establishes CV-charging as an effective strategy to overcome the kinetic limitations of Li-CO2 batteries for a specific configuration,and provides a framework for optimizing charging protocols across battery systems. 展开更多
关键词 Constant current charging Constant voltage charging Operation protocol Li-CO2battery Energy efficiency
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Investigating the gas generation during Li plating in fast-charging Li-ion batteries 认领 引用
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作者 Yuanmao Chen Luoyi Ding +7 位作者 Qinghui Zeng Yongteng Dong Xinyang Yue Xianping Si Donglin Zhang Qingtuan Qu Zheng Liang Wei Hao 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第7期696-701,共6页
Lithium plating and gas evolution during fast charging of graphite-based lithium-ion batteries(LIBs)are among the pivotal challenges contributing to rapid capacity loss.However,the mechanisms underlying gas generation... Lithium plating and gas evolution during fast charging of graphite-based lithium-ion batteries(LIBs)are among the pivotal challenges contributing to rapid capacity loss.However,the mechanisms underlying gas generation and corresponding mitigation strategies in electrolytes comprising mixed organic molecules and Li salts remain underexplored.Herein,we employed first-principles studies to simulate the lithiation process of electrolytes and predicted gas formation at anode interfaces with Li plating.Our results emphasize the critical role of Li salts in initiating solvent molecule decomposition and the exacerbation of interfacial degradation under conditions of elevated temperature and prolonged annealing,giving rise to the production of CO,C2H4,CH4,and H2,along with a significant increase in SEI's electronic conductivity.Moreover,our computations highlight that ethylene carbonate(EC)in commercial electrolytes is the overarching cause of interface instability and gas evolution.Experimental validations demonstrate that reducing the EC content in electrolytes results in an enhancement of the specific capacity of LiNi0.8Co0.1Mn0.1O2|graphite full cells from 158.13 m Ah/g to 182.53 m Ah/g,and an improvement in capacity retention from 72.0%to 80.4%over 130 cycling at 3 C.This research provides a theoretical framework for designing fast-charging electrolytes with stable interfaces and minimal gas generation. 展开更多
关键词 Li-ion battery Gas generation Fast charging Li plating Electrolyte engineering
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Highly accurate calibration of particle number portable emissions measurement systems(PN-PEMS)via actual soot charging probability correction 认领 引用
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作者 Haotian Guo Chengxiang Guo +6 位作者 Tongzhu Yu Huaqiao Gui Yixin Yang Zhe Ji Junjie Liu Jiguang Wang Jianguo Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第6期1-11,共11页
The upcoming Euro 7 vehicle emissions regulation sets new requirements for calibrating particle number portable emissions measurement systems(PN-PEMS).In current PN concentration calibration methods,arbitrary selectio... The upcoming Euro 7 vehicle emissions regulation sets new requirements for calibrating particle number portable emissions measurement systems(PN-PEMS).In current PN concentration calibration methods,arbitrary selection of aerosolmaterials and incorrect estimation ofmultiply-charged particles lead to calibration deviation.This study proposes a novel,accurate calibration method based on the calibration and correction of the actual charging probability of soot for calibrating PN-PEMS.The accurate calibration results of a PN-PEMS show a maximum reduction of 7.65%in the+1-charging probability deviation and of 3.15%in the counting efficiency deviation.The calibration of the actual charging probability of soot introduces a calibration uncertainty increment of 0.09%-2.71%,and the overall uncertainty is<4.76%,whichmakes the calibration results more credible.The interaction of calibration aerosols of different physicochemical properties with the condensation particle counter working fluid and catalytic stripper device is the main reason for the different PN-PEMS calibration results.The calibration of the actual charging probability of particles is the fundamental method to eliminate the calibration deviation caused by the estimation and correction of multiply-charged particles. 展开更多
关键词 Accurate calibration Portable emissions measurement systems(PEMS) Particle number(PN) Soot Charging probability Multiply-charged correction
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A Hybrid Encoding-Based Coordinated Optimization Method for Charging Matrix Design in the Blast Furnace Ironmaking Process 认领 引用
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作者 Jicheng Zhu Zhaohui Jiang +4 位作者 Dong Pan Haoyang Yu Chuan Xu Ke Zhou Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1689-1706,共18页
A well-designed charging matrix(CM)is crucial for advancing green and low-carbon production in the blast furnace(BF)ironmaking process.Over recent years,metaheuristics algorithms have been applied to optimize CM,parti... A well-designed charging matrix(CM)is crucial for advancing green and low-carbon production in the blast furnace(BF)ironmaking process.Over recent years,metaheuristics algorithms have been applied to optimize CM,partially reducing reliance on on-site workers.However,CM optimization is a challenging mixed-variable constraint optimization problem.Prior studies predominantly simplify CM to either continuous or discrete forms via variable fixation or type conversion,which hinders the efficient joint optimization of heterogeneous variables,limiting optimization accuracy and search efficiency.To tackle this barrier,this study proposes a novel method named Hybrid Encoding-based Adaptive Coordinated Differential Evolution(HE-ACoD E),marking the first attempt to optimize CM from a mixed-variable perspective.First,a hybrid encoding scheme is devised to provide a unified representation for the mixed variables in CM.Then,a coordinated mixed-variable mutation strategy is developed,effectively facilitating the synchronized evolution of continuous and discrete variables.Moreover,a constraintaware selection operator and a weight-guided parameter adaptation strategy are proposed,which collaboratively guide the population toward feasible,high-quality solutions across different evolutionary stages and problem landscapes.Extensive comparison experiments on two industrial scenarios demonstrate that HE-ACoD E outperforms state-of-the-art CM optimization and mixed-variable optimization methods in terms of accuracy,stability,and convergence performance. 展开更多
关键词 Blast furnace charging matrix(CM) differential evolution mixed-variable optimization
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An evaluation method for the aggregate adjustable capability of photovoltaic-storage-charging stations considering local security constraints 认领 引用
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作者 Chao Li Jiawei He +4 位作者 Tingzhe Pan Zijie Meng Xinlei Cai Xin Jin Zechun Hu 《Global Energy Interconnection》 EI CSCD 2026年第1期108-118,共11页
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c... As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners. 展开更多
关键词 Photovoltaic Energy storage Electric vehicle charging station Flexibility aggregation
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Research on Electric Vehicle Charging Optimization Strategy Based on Improved Crossformer for Carbon Emission Factor Prediction 认领 引用
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作者 Hongyu Wang Wenwu Cui +4 位作者 Kai Cui Zixuan Meng BinLi Wei Zhang Wenwen Li 《Energy Engineering》 EI 2026年第1期332-355,共24页
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje... To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization. 展开更多
关键词 Carbon factor prediction electric vehicles ordered charging multi-objective optimization Crossformer
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Self-Maintained Thermal Charging Cells for the Efficient Harvesting of Waste Heat at Elevated Temperatures 认领 引用
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作者 Tingting Meng Yimin Xuan Yunjie Xiong 《Energy & Environmental Materials》 SCIE EI CAS CSCD 2026年第3期433-439,共7页
Thermal charging cells face two main challenges that limit their practical applications.1)Still lacking the systems suitable for operation under higher-temperature environments,even though high-temperature waste-heat ... Thermal charging cells face two main challenges that limit their practical applications.1)Still lacking the systems suitable for operation under higher-temperature environments,even though high-temperature waste-heat recovery systems have greater application potential and practical significance compared with room-temperature systems.2)There are limitations in the self-sustaining performance of continuous discharge under temperature differences,which hold critical significance for the real-world implementation of thermal charging cells.This study has successfully constructed a high-temperature-resistant thermal charging cells system that can operate at 160℃ by optimizing the design of electrode solutions and layered electrode materials,which is currently the highest temperature achieved as far as we know.This high-temperature-resistant thermal charging cells system can achieve a considerable thermal voltage of 960 mV and an impressive Carnot-relative efficiency of 14%,outperforming the state-of-the-art thermoelectric systems.This work has investigated the self-maintained capability of the thermal charging cells system under the opposing effects of ionic concentration and temperature differences between the electrodes and experimentally verified this performance by adjusting the lithium-ion concentration and temperature difference.Furthermore,the stability of the system under long-term charge and discharge cycles was tested,making it the longest running system currently.This work significantly highlighted the broad application prospects of thermal charging cells systems in practical implementations,particularly in advanced thermal energy harvesting and conversion technologies. 展开更多
关键词 elevated temperature self-maintained thermal charging thermoelectric conversion
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Enhancing IoT-Enabled Electric Vehicle Efficiency:Smart Charging Station and Battery Management Solution 认领 引用 被引量：1
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作者 Supriya Wadekar Shailendra Mittal +1 位作者 Ganesh Wakte Rajshree Shinde 《Energy Engineering》 EI 2026年第1期153-180,共28页
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a... Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem. 展开更多
关键词 Battery management system internet of electric vehicles MATLAB/Simulink smart charging state of charge vehicle-to-grid
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Development of an asymmetric arc-shaped magnetic coupler for the AUV wireless charging system 认领 引用
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作者 Shuo Gong Mingwei Lin +2 位作者 Dejun Li Ri Lin Canjun Yang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期253-265,共13页
The underwater wireless charging system can improve the endurance of autonomous underwater vehicles(AUVs)and extend their detection range.However,the magnetic couplers for the AUV wireless charging system are currentl... The underwater wireless charging system can improve the endurance of autonomous underwater vehicles(AUVs)and extend their detection range.However,the magnetic couplers for the AUV wireless charging system are currently not very compatible with AUVs,have high electromagnetic interference,and have low power levels.The new asymmetric arc-shaped magnetic coupler proposed in this paper can fit the surface of an AUV without changing its hydrodynamic model,making it suitable for use in the AUV wireless charging system.The radial coupling scheme can effectively prevent electromagnetic interference of the internal components by limiting the magnetic flux outside the AUV.By building the magnetic circuit model of the magnetic coupler and optimizing its structural parameters,the magnetic coupler has good misalignment tolerance performance.To test the effectiveness of the magnetic coupler,a practical 3 kW prototype has been developed.Experiments show that the system can achieve effective power transmission under AUV rotational misalignment±20°and axial misalignment±20 mm. 展开更多
关键词 AUV Magnetic coupler Wireless charging Underwater Misalignment tolerance
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A generalizable physics-informed neural network for lithium-ion battery SOH estimation utilizing partial charging segments 认领 引用
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作者 Sijing Wang Ruoyu Zhou +3 位作者 Yijia Ren Honglai Liu Yiting Lin Cheng Lian 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期977-986,I0021,共10页
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di... Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions. 展开更多
关键词 State of health Feature extraction Charging process Physics-informed neural network Generalization
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Insights into the charging behavior of tight oil under nanoscale confinement effects 认领 引用
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作者 Chuan-Yi Tang Leng Tian +3 位作者 Song Zhang Man-TianLi Zhen-Tao Yu You-Guo Yang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2713-2722,共10页
As conventional oil dwindles,tight oil gains importance for its vast potential.Thephysical property limit refers to the minimum pore size at which reservoir fluid can be charged under a defined accumulation overpressu... As conventional oil dwindles,tight oil gains importance for its vast potential.Thephysical property limit refers to the minimum pore size at which reservoir fluid can be charged under a defined accumulation overpressure,and it is crucial for the accurate assessment of tight oil reservoirs.Micro-and nano-scale pores are the primary storage spaces in tight formations,and the size effect,arising from strong fluidsolid interactions,plays a significant role in influencing the chargingbehavior of tight oil.However,understanding of tight oil charging in confined space is still limited.In this work,a typical tight oil from theFengchengFormation in the Junggar Basin was chosen as the researched objective.Moleculardynamics simulations were employed to investigate the charging process of tight oil into nanoslits preoccupied by formation water.The critical pressures for tight oil injection into nanoslits with varying sizes were calculated to determine the physical property limit of tight oil charging.Under critical charging conditions,the capillary force acts as the dominant resistance and approximated the threshold charging pressure.The simulated threshold charging pressure was significantly higher than the capillary force predicted by the classic Young-Laplace equation without considering size effects.This result suggests that conventional fluid mechanics theories in confined space overestimate the physical property limit,leading to an inflated assessment of tight oil accumulation.Simulation results show that,as the nanoslit size decreases,interfacial tension and contact angle increase,while water film thickness decreases.By accounting for these size effects,the modified capillary force closely matched the simulated threshold charging pressure.Using the modified capillary force,the physical property limit of tight oil in the Fengcheng Formation,under an accumulation overpressure of 15 MPa,was examined to be 7.2 nm.Furthermore,the effect of mineral types on threshold charging pressure was investigated and give order of illite>calcite>orthoclase>quartz.Additionally,a comparison between the calculated charging pressures with experimentally measured values was conducted,and their strong consistency confirms the validity of the revised Young-Laplace model.This study enhances our understanding of the tight oil charging mechanisms,highlights the importance of size effects,and provides significant insightsfor the accumulation assessment of tight oil reservoirs. 展开更多
关键词 Tight oil Charging behavior Size effects The capillary force
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Transitioning toward efficient smart charging of electric vehicles:Challenges and perspectives 认领 引用
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作者 Lennard SUND Hossein SABER +2 位作者 Janik MUIRES Saber TALARI Wolfgang KETTER 《ENGINEERING Management》 CSCD 2026年第2期559-569,共11页
Rapid diffusion of electric vehicles(EVs)is central to global decarbonization strategies.Yet,largescale uncoordinated charging threatens the stability of the distribution grid,increases congestion costs,and can erode ... Rapid diffusion of electric vehicles(EVs)is central to global decarbonization strategies.Yet,largescale uncoordinated charging threatens the stability of the distribution grid,increases congestion costs,and can erode the environmental benefits of electrified mobility.Smart charging has therefore emerged as a critical paradigm for aligning mobility demand with power system constraints through adaptive,information-driven coordination.This commentary develops a comprehensive operationsmanagement-oriented perspective on EV smart charging as a complex socio-techno-economic system shaped by interacting physical infrastructures,digital platforms,market mechanisms,and heterogeneous human behavior.We synthesize existing research into an integrated framework that distinguishes between a physical layer(vehicles,charging infrastructure,grids,and mobility needs)and a digital layer(data exchange,coordination,and service provision),and organize the landscape into five interdependent ecosystems:electricity,charging,mobility services,users,and regulation.Building on this framework,we articulate five key research opportunities:(i)coordination of heterogeneous stakeholders with conflicting objectives;(ii)scalable and flexible control strategies;(iii)strategic infrastructure and investment planning under uncertainty;(iv)effective governance and policy design,and(v)modeling EV user preferences and behavior.We highlight how addressing these opportunities can enhance system efficiency,reliability,sustainability,and user acceptance.By positioning smart charging at the intersection of energy and mobility systems and emphasizing the central role of digital-physical integration,this commentary provides a unifying lens and a forward-looking research agenda for scholars seeking to contribute to the design of smart,sustainable urban mobility and power systems. 展开更多
关键词 electric vehicles smart charging energy artificial intelligence sustainability transportation
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Dual‑salt electrolyte design enabled by synergistic solvation and interfacial regulation for fast charging of lithium‑ion batteries 认领 引用
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作者 Jun-Sen Jiang Lan-Fang Que +5 位作者 Rui-Chi Li Fu-Da Yu Xuan Wang Ji-Huai Wu Can-Zhong Lu Yi-Ming Xie 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期484-494,I0011,共11页
To address the performance limitations of conventional LiPF6-carbonate electrolytes under extreme temperatures and high-rate charging,lithium difluoro(oxalato)borate(LiDFOB)is introduced into the LiPF6-carbonate elect... To address the performance limitations of conventional LiPF6-carbonate electrolytes under extreme temperatures and high-rate charging,lithium difluoro(oxalato)borate(LiDFOB)is introduced into the LiPF6-carbonate electrolyte to form a dual-salt system.The optimization mechanism enhancing the fast-charging capability of LiNi0.52Co0.2Mn0.28O2(NCM523)cathode is systematically explored.Molecular dynamics simulations and electrochemical characterization demonstrate the reconstruction of Li+solvation structures,expanding the voltage window and reducting Li+desolvation barriers.In addition,the incorporation of LiDFOB induces the generation of a LiF/LixBOyFz-enriched cathode-electrolyte interphase,which effectively suppresses the dissolution of transition metals.In situ impedance measurements reveal the accelerated interfacial charge transfer kinetics.As expected,the NCM523 cathode achieves an 82%state-of-charge(SOC)in 12 min at 5 C(25°C)with 87%capacity retention after 100 cycles,and exhibits a 65%higher discharge capacity at 1 C than the baseline at−20°C.The 1 Ah pouch cells based on LiNi0.52Co0.2Mn0.28O2cathodes,graphite anodes,and 0.5 wt%LiDFOB-modified electrolyte demonstrate fast-charging capabilities:charging 97%of the pouch cell capacity within 30 min(2 C)and 80%within 15 min(4 C)at 25°C.This study offers a practical electrolyte design strategy that enhances the fast-charging performance of lithium-ion batteries(LIBs)over a wide temperature range(from−20 to 25°C). 展开更多
关键词 Lithium-ion batteries Fast charging Li+solvation structure LiF/LixBOyFzenriched interface Dual-salt LiPF6-carbonate electrolyte
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QFedFormer:A Privacy-Preserving Federated Transformer with Blockchain-Anchored Incentives for Dynamic EV Charging Pricing 认领 引用
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作者 Lilia Tightiz LMinh Dang Hyosik Yang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1348-1382,共35页
We present QFedFormer,a federated transformer for dynamic electric vehicle(EV)-charging price prediction that combines quantization-aware training,SHAP-guided explainability,and blockchain-based incentives.The framewo... We present QFedFormer,a federated transformer for dynamic electric vehicle(EV)-charging price prediction that combines quantization-aware training,SHAP-guided explainability,and blockchain-based incentives.The framework trains across distributed charging stations without centralizing user data,and programmable contracts set tariffs from forecasted demand and user-declared flexibility,while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs.On a real-world dataset,QFedFormer attains an energydemand RMSE of 1.82±0.02 kWh and a tariff RMSE of 11.83±0.10 KRW/kWh(MAPE 2.7±0.2%)in the non-private baseline,outperforming FedAvg and Block-FeDL by 14.1%and 9.5%,respectively.Under client-level differential privacy(DP)with(σ=1.6,C=1,p=0.1,δDp=10−5),QFedFormer achieves(ε=2.0,δDp=10−5)after 50 rounds under a Rényi accountant,with forecast accuracy degrading modestly to 1.95 kWh RMSE(~7.1%relative increase vs.nonprivate baseline).Blockchain evaluation shows an average audit latency of 58 ms per audit round,while a permissioned Ethereum-compatible deployment sustains more than 500 client updates per minute with gas costs of~$0.039/client per audit round.These results indicate that QFedFormer enables accurate,privacy-preserving,and auditable coordination of EV-grid interactions,offering both regulators and service providers a practical deployment pathway. 展开更多
关键词 Blockchain federated learning EV charging dynamic pricing differential privacy SHAP tokenized incentives
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Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG 认领 引用
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作者 Xiangyi Le Deyu Lin +2 位作者 Yufei Zhao Wang Miao Yong Liang Guan 《Computers, Materials & Continua》 SCIE EI 2026年第9期2376-2395,共20页
The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustaina... The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks.However,in large-scale Battery-free SWIPT-enabled Sensor Networks(BSSN)characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates,employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency.To overcome these challenges,a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient(MCEC-MADDPG)is proposed in this paper.Specifically,we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements.To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments,the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process(POMDP).Subsequently,the Centralized Training with Decentralized Execution(CTDE)architecture of the MADDPG algorithm is leveraged to solve this POMDP,which effectively tackles the non-stationarity challenge inherent in multi-agent environments.Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability.It enables the adaptive emergence of spatial-division collaborative strategies,significantly enhances the average residual energy of the network,and elevates the node survival rate to nearly 90%.Compared with Deep Deterministic Policy Gradient(DDPG),the traditional static Partition-Greedy method,the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy,the proposed approach demonstrates substantial advantages. 展开更多
关键词 Battery-free SWIPT-enabled sensor networks multi-agent deep deterministic policy gradient multi-unmanned aerial vehicle collaborative energy charging partially observable Markov decision process centralized training with decentralized execution
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Experimental Analysis of Underground Wireless Charging Based on Backscatter-Assisted Distributed Beamforming 认领 引用
17
作者 Zhang Huaijin Liu Guanghua +2 位作者 Liao Changzhen Wang Jintao Jiang Tao 《China Communications》 SCIE EI CSCD 2026年第5期85-95,共11页
In order to avoid frequent manual replacement of underground sensor node batteries,many researches have been devoted to the realization of wireless powered underground sensor networks(WPUSNs).However,existing schemes ... In order to avoid frequent manual replacement of underground sensor node batteries,many researches have been devoted to the realization of wireless powered underground sensor networks(WPUSNs).However,existing schemes mainly focus on the design of routing protocols and network topologies,failing to address the challenge of activating energy harvesting circuits.To this end,we propose a backscatter-assisted distributed beamforming-based WPUSN(B2-WPUSN).The key insight of B2-WPUSN is utilizing backscatter to acquire the accurate channel state information(CSI)and designing the corresponding beamforming vector to concentrate the energy until it exceeds the startup threshold of the node.In particular,since backscatter causes additional attenuation,we use a LoRa signal,whose high sensitivity ensures correct channel estimation.We prototype B2-WPUSN on universal software radio peripheral(USRP)radios and evaluate its charging performance in a sandbox.The experimental results show that the average charging time is less than 40 seconds even at a soil moisture of 15%. 展开更多
关键词 backscatter distributed beamforming experimental underground wireless charging wireless powered underground sensor networks(WPUSNs)
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Charging Scheduling of Clustered Wireless Rechargeable Sensor Networks Considering Dynamic Selection of Cluster Heads 认领 引用
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作者 Mengqi Liu Haiqing Yao 《Computers, Materials & Continua》 SCIE EI 2026年第7期1062-1085,共24页
For the wide-coverage application scenarios,wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring,and use the mobile charger(MC)to su... For the wide-coverage application scenarios,wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring,and use the mobile charger(MC)to support the sustainable charging of the network.Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability.However,the existing work tends to use fixed triggering mechanism for cluster head(CH)rotation,and may trigger the rotation either too early or too late.Besides,the existing charging triggering mechanisms cannot track the changes in network topology in real time.As a result,both the network energy efficiency and the node failure rate degenerate correspondingly.To solve these problems,this work proposes a dynamic cluster head selection algorithm(DCHSA),which evaluates potential candidate CH sets based on the energy consumption,remaining energy and topological structure,and then select a new CH within this set based on the CH rotation energy consumption and the candidate CH evaluation mechanism.Furthermore,an adaptive dual-threshold selection algorithm based on dynamic energy consumption(ADTSA-DEC)is proposed to determine the set of requiring charging nodes and the trigger time for charging scheduling.The particle swarm optimization is then employed to implement the charging scheduling.Finally,extensive simulations validate that the newly proposed algorithms have outstanding accuracy and robustness in improving overall network energy efficiency and node survivability compared with existing methods. 展开更多
关键词 Clustered wireless rechargeable sensor networks cluster head rotation adaptive dual-threshold charging scheduling strategy particle swarm optimization
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A High-Performance Thermal Charging Cell with High Power Density and Long Runtime Enabled by Zn2+and NH4+Co-insertion 认领 引用
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作者 Zhiwei Han Shengliang Zhang +4 位作者 Helang Huang Jing Wang Hui Dou Tianran Zhang Xiaogang Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第5期831-845,共15页
Zn-based thermal charging devices,utilizing the synergistic effect of ion thermoextraction and thermodiffusion,are able to efficiently convert thermal energy into electrical energy and storage in the devices,making th... Zn-based thermal charging devices,utilizing the synergistic effect of ion thermoextraction and thermodiffusion,are able to efficiently convert thermal energy into electrical energy and storage in the devices,making them a highly promising technology for low-grade heat recovery and utilization.However,the low output power density and energy conversion efficiency resulted by the slow diffusion kinetics of Zn2+hinder their development.Herein,we present a highperformance thermal charging cell design using Zn2+/NH4+hybrid ion electrolyte,which not only maintains the high output voltage of the Zn-based thermoelectric system,but also significantly enhances the output power density due to the fast diffusion kinetics of NH4+.Based on this strategy,the thermal charging cell displays a high thermopower of 12.5 mV K-1and an excellent normalized power density of 19.6 mW m-2K-2at a temperature difference of 35 K.The Carnot-relative efficiency is as high as 12.74%.Moreover,it can operate continuously for over 72 h when the temperature difference persists,achieving a balance between thermoelectric conversion and output.This work provides a simple and effective strategy for the design of high-performance thermal charging cells for low-grade heat conversion and utilization. 展开更多
关键词 Thermal charging cells Zn2+/NH4+hybrid ions Low-grade heat conversion and storage High power density Hydrated V2O5
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MILOF-TCN:A Hierarchical Edge-Fog Framework for Monitoring Abnormal and Missing Patterns in Electric Vehicle Charging Data 认领 引用
20
作者 Hwa-Young Jeong 《Computers, Materials & Continua》 SCIE EI 2026年第9期2004-2024,共21页
The rapid growth of electric vehicle(EV)charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints.Conventional anomaly detection approac... The rapid growth of electric vehicle(EV)charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints.Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms,limiting their practical applicability in large-scale deployments.This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network(TCN)detector.The edge component suppresses non-informative patterns,while the fog layer performs temporal modeling on selectively forwarded data.This design enables controllable reduction of fog-level processing load.Under corrected end-to-end evaluation on real-world EV charging load data,the hierarchical pipeline should be interpreted as a system operating point rather than a uniformly superior detector.Relative to fog-only TCN-AE inference,the selected routing policy reduces fog workload by 34.9%and shortens average detection delay from 93.6 to 75.6 h,but increases false alarms per day from 0.88 to 7.29 and lowers F1 from 0.547 to 0.455.Sensitivity experiments over routing thresholds reveal a consistent trade-off among fog workload,alert burden,detection delay,and retained anomaly evidence.Additional routing diagnostics show that the primary source of performance degradation is information loss induced by filtering,rather than weakness of the fog detector on the forwarded subset.These findings suggest that hierarchical edge intelligence is a practical but calibration-sensitive direction for scalable anomaly monitoring in EV charging infrastructures. 展开更多
关键词 Edge computing fog computing anomaly detection electric vehicle charging time-series monitoring temporal convolutional networks missing data patterns hierarchical architecture
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