Search Results (1,437)

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article

Accurate electric vehicle (EV) charging load forecasting is essential for grid planning and resource allocation, yet existing approaches struggle with the inherent sparsity of charging data—a phenomenon characterized by excessive zeros representing periods of no charging activity. This paper addresses this challenge through a novel framework combining a Zero-Inflated Neural Network (ZINN) architecture with an Evolutionary Neural Architecture Search (ENAS) algorithm. ZINN explicitly decomposes the forecasting problem into binary classification (predicting charging occurrence) and regression (estimating energy magnitude conditioned on occurrence), enabling the model to learn distinct patterns for the absence and presence of charging events. Rather than relying on manually designed architectures, ENAS automatically discovers optimal encoder and decoder configurations from a comprehensive search space encompassing modern architectures (LSTM, GRU, Transformer, and iTransformer), layer configurations, activation functions, and hyperparameters. The evolutionary algorithm balances prediction accuracy with computational efficiency through multi-objective optimization. Extensive experiments on real-world EV charging data from 30 stations in Wuhan demonstrate that the ZINN+ENAS framework achieves the lowest prediction error compared to conventional baselines, with the discovered optimal configuration substantially outperforming hand-crafted designs. Comprehensive ablation studies reveal that the asymmetric dual-head architecture and adaptive regularization strategies are critical for handling data sparsity. These findings highlight the importance of explicit zero-inflation modeling and automated architecture discovery for specialized forecasting tasks, providing practitioners with an open-source framework for practical EV charging load prediction. Full article

Excessive phosphorus discharge from aquaculture effluent significantly contributes to coastal eutrophication, while conventional adsorbents exhibit limited phosphorus removal efficiency in high-salinity, weakly alkaline seawater effluent. This study developed iron/calcium co-modified chlorella biochar (FCBC) through co-impregnation and high-temperature pyrolysis, optimizing the preparation process via the Box–Behnken response surface method. The optimal conditions were identified as an iron concentration of 2.5 mol/L, a calcium concentration of 2.0 mol/L, a pyrolysis temperature of 717 °C, and a duration of 113 min. Under these conditions, FCBC achieved a phosphorus removal rate of 93.23% within 3 h, which was significantly higher than that of the unmodified Chlorella biochar (BC, <8% within the same reaction time). The Fe/Ca co-modification endowed FCBC with a positively charged surface, an increased average pore size of 22.773 nm, and good magnetic responsiveness (saturation magnetization of 6.68 emu·g⁻¹). FCBC demonstrated remarkable adaptability, achieving over 97% phosphorus removal across a pH range of 3 to 11, salinity levels of 5 to 40‰, and phosphorus concentrations of 1 to 15 mg/L. Its adsorption kinetics conformed to pseudo-second-order kinetics (R² = 0.987) and the Freundlich model (R² = 0.971), with efficient phosphorus removal primarily attributed to iron–calcium synergistic effects. FCBC presents significant potential for phosphorus treatment in marine aquaculture effluents. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

To address the application requirements of energy storage devices for new energy electric vehicles—including high energy density, high-power density, fast charging and discharging, and long-term cycling stability—traditional symmetric supercapacitors are often limited by low energy density and poor compatibility between the anode and cathode, making it difficult to meet the high-efficiency energy storage demands under the dynamic operating conditions of electric vehicles. This study focuses on the regulation of hierarchical thin-film structures and the innovative heterogeneous coating interface engineering with precise slurry coating and film-forming optimization and designs and fabricates NiCo₂O₄/NiFeO composite thin-film electrodes and Mn-doped porous carbon (Mn-PC) thin-film electrodes. The uniform, compact and stable coating formation on nickel foam substrates via controllable slurry coating facilitates the efficient integration of active materials and conductive supports. The electrode slurries were coated onto conductive nickel foam substrates, and high-performance aqueous supercapacitors were assembled using an asymmetric configuration. A systematic study was conducted covering material preparation, structural characterization, electrochemical testing, and full-device performance evaluation. Using techniques such as XRD, XPS, SEM, TEM, BET, and an electrochemical workstation, the study revealed the structure–activity relationships among material morphology, crystalline phases, pore structure, and electrochemical performance, elucidating the charge storage mechanisms of the composite electrode films and the principles of synergistic adaptation between the anode and cathode. The results indicate that NiCo₂O₄ nanowires decorated with in situ-grown NiFeO nanosheets to form a composite structure; when coated onto nickel foam, this forms a uniform, porous electrode film with a specific surface area of 171.3 m²/g, a specific capacitance as high as 1746 F/g at 1 A/g, and a capacity retention rate of 94.0% after 10,000 cycles. After coating and film formation, the Mn-PC anode introduced pseudocapacitive active sites through uniform Mn doping, resulting in a film electrode specific capacitance of 348 F/g and significantly improved rate and cycling performance. The assembled NiCo₂O₄/NiFeO//Mn-PC asymmetric supercapacitor exhibits a thin-film electrode specific capacitance of 153 F/g at 1 A/g, with a maximum energy density of 52 Wh/kg. Even at a power density of 9000 W/kg, it maintains 45 Wh/kg, and retains 89.5% of its capacity after 10,000 cycles, with overall performance outperforming most previously reported transition metal-based devices. This coating-engineered electrode fabrication strategy breaks through the interface mismatch and structural instability bottlenecks of traditional thin-film electrodes, providing a novel material system and an efficient coating assembly strategy for high-performance supercapacitor thin-film electrodes in new energy electric vehicles, and offers experimental evidence and technical references for the development and application of high-power energy storage coating devices for automotive use, as well as the innovative design of electrode coating engineering in energy storage fields. Full article

With the continuous increase in the number of Retired Lithium-Ion Batteries (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale applications. To address these issues, this paper presents a rapid MRC estimation method using a hybrid Convolutional Neural Network (CNN), Conv Block Attention Module (CBAM), and Long Short-Term Memory (LSTM) architecture. The proposed approach extracts key voltage and capacity features from only the initial 30 min charging phase, integrating both factory and laboratory data. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies, and the CBAM adaptively emphasizes critical characteristics. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, achieving a testing R² of 98.05% and a Mean Absolute Percentage Error (MAPE) of 1.60%. These results highlight the superior performance of the proposed framework, exhibiting strong potential for high-throughput battery sorting and large-scale health monitoring systems. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO₂-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis. Full article

This paper proposes an advanced wind energy conversion and management framework for improving grid integration and mitigating frequency and power fluctuations caused by wind intermittency. The studied system combines a permanent magnet synchronous generator (PMSG), a unidirectional Vienna rectifier on the machine side, a Li-ion battery energy storage system, and a bidirectional Vienna rectifier on the grid side. The main scientific challenge addressed in this work is to ensure efficient wind power extraction, secure battery charging/discharging operation, and stable power exchange with the grid under variable operating conditions. To this end, a comprehensive nonlinear state-space model of the overall system is first established. Then, nonlinear controllers based on integral sliding mode principles are developed to guarantee rotor-speed tracking, DC-bus voltage regulation, battery charging current limitation, and active/reactive power control. In addition, an adaptive observer is designed to estimate the battery open-circuit voltage and support the supervision of the state of charge. An energy management strategy is further proposed to coordinate the operating modes according to grid conditions and battery constraints. Simulation results demonstrate that the proposed approach effectively smooths wind power fluctuations, improves grid support capability, and enhances the overall dynamic performance of the wind energy conversion system. Full article

This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article

This study presents the design, thermodynamic modelling, and numerical optimisation of a medium-scale (100 kW) transcritical CO₂ air-source heat pump water heater (ASHP-WH) intended to deliver 90 °C domestic hot water under sub-zero ambient conditions. A detailed component-sizing methodology was established and implemented in AMESim 2404 using REFPROP-based property calculations, with model convergence confirmed by the mass and energy balance closure. Parametric investigations covering the discharge pressure, refrigerant charge, ambient air temperature, and water outlet temperature were conducted through 140 steady-state simulations. The results show that the system achieved a heating capacity of 100–121 kW with a coefficient of performance (COP) of 2.7–3.3 across −15 °C to +10 °C ambient conditions. The optimal discharge pressure (≈11.2 MPa) and charge inventory (10 ± 2 kg) define a broad operating window that ensures COP stability (±2%) and avoids liquid carry-over. The exergetic efficiency remained above 0.75 throughout the tested climate range. Compared with published laboratory prototypes, the proposed 100 kW module demonstrates a superior performance at harsher sub-zero boundaries, highlighting its potential for commercial hot water and industrial applications. The findings provide actionable guidelines for component sizing, charge management, and adaptive pressure control, and establish a pathway from a numerical prototype to scalable field deployment of medium-scale transcritical CO₂ systems. Full article

In extreme scenarios such as nuclear explosions and high-energy radiation detection in space, UV-cured adhesives are usually used as coupling media to bind tapered optic fiber arrays with intensified charge-coupled devices or complementary metal–oxide semiconductors and a tapered optic fiber array for effective optical signal transmission. To address the issue of weak bonding strength caused by the small binding area between charge-coupled devices or complementary metal–oxide semiconductors and TOFA, a stage-wise curing process was investigated and proved to be efficient through comparison with the single curing process. The effect of interval time between the initial and final curing on coupling strength was characterized by tensile strength, shear strength and shock acceleration testing, and the samples were exposed to high and low temperatures for evaluation of their environmental adaptability. The curing mechanism was analyzed by surface morphology of the adhesive layer after decoupling and an energy-dispersive X-ray spectroscopy elemental analysis of interface layer. The results show that when the interval time is extended from 5 min to 60 min, the shock acceleration of the coupling device decreases by 26.1%, while the tensile and shear strengths also decrease by 49.4% and 60.7%, respectively. The decline in coupling strength is attributed to oxygen inhibition during interval time. The exposure of the adhesive surface to the air allows oxygen to diffuse into and react with active the free radicals that remain from the initial curing, which inhibits further polymerization and generates a thin, incompletely cured weak boundary layer. These findings provide insights for optimizing stage-wise curing processes and improving the reliability of coupled imaging devices. Full article

Chitosan, owing to its abundant amino and hydroxyl functional groups, serves as an effective biosorbent for the removal of toxic metal(loid) ions from water. This review summarizes recent advances in chitosan-based adsorbents specifically for arsenate (As(V)) and copper ions (Cu(II)), with an emphasis on adsorption mechanisms and electrospun nanofiber technologies. A conceptual “charge adaptation–structure synergy” model is proposed to elucidate the distinct adsorption behaviors of chitosan toward anionic and cationic substances: under acidic conditions, As(V) adsorption is dominated by electrostatic attraction to protonated amino groups, whereas at pH values near or above the pKa, Cu(II) removal proceeds via synergistic chelation involving deprotonated amino and hydroxyl groups. Competitive and synergistic interactions in binary systems, particularly between As(V) and coexisting anions such as phosphate, are also discussed. Notably, the kinetic advantages of electrospun chitosan nanofibers are highlighted, with equilibrium times shortened from several hours to approximately 0.5–2.6 h. Key challenges and future research directions are further discussed, including scalable manufacturing and the treatment of complex wastewater matrices. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article