Search Results (1,134)

The literature clearly indicates that both academia and industry are strongly committed to developing comprehensive processes for spent Li-ion battery (LIB) recycling. In this regard, the current study presents an original contribution by providing a quantitative assessment of a large-scale recycling plant designed for the treatment of completely spent LIBs. In addition to a concept of the basic process, this assessment also considers a case study of a thermal integration and CO₂ capture subsystem. Process flow modeling software was used to evaluate the contribution of all process steps and equipment to overall energy consumption and to mass balance the data required for the technical assessment of the large-scale recycling plant. To underline the advantages and identify the optimal novel process concept, several key performance indicators were determined, such as recovery efficiency, specific energy/material consumption, and specific CO₂ emissions. In addition, the economic potential of the recycling plants was evaluated for the defined case studies based on capital and O&M costs. The results indicate that, even with CO₂ capture applied, the thermally integrated process with the combustion of hydrogen produced in the recycling plant remains the most promising large-scale configuration for spent LIB recycling. Full article

Determining charge and discharge behavior is essential for optimizing charging strategies and evaluating balancing algorithms in battery energy storage systems and electric vehicles. Conventionally, a sequence of circuit simulations or tedious hardware tests is required to evaluate the performance of the balancing algorithm. To mitigate these problems, this paper proposes a variable capacitor model that can be easily built from the incremental capacity curve. This model provides a direct and insightful R-C time constant method for the charge/discharge time calculation. After validating the model accuracy by experimental results based on the cylindrical lithium-ion cell test, a switched-capacitor active balancing and a passive cell balancing circuit are implemented to further verify the effectiveness of the proposed model in calculating the cell balancing time within 2% error. Full article

Background/Objectives: Cognitive impairment is one of the most common and debilitating clinical features of Multiple Sclerosis (MS). Neuropsychological assessment, however, is time-consuming and requires personal resources, so, due to limited resources in daily clinical practice, information on cognitive profiles is often lacking, despite its high prognostic relevance. Time-saving and effective tools are required to bridge this gap. This study evaluates the sensitivity and specificity of a revised version of TRACK-MS (TRACK-MS-R), a recently published screening tool to identify cognitive impairment in MS in a fast and reliable way, offering a balance between efficiency and diagnostic yield for the individual patient. Methods: In this prospective cross-sectional study, 102 MS patients and 94 age-, sex-, and education-matched healthy controls (HC) completed an extensive neuropsychological assessment, including TRACK-MS-R, to test for cognitive processing speed (Symbol Digit Modalities Test, SDMT) and verbal fluency (Regensburger Word Fluency Test, RWT). Sensitivity of TRACK-MS-R was assessed by using the BICAMS-M battery as a reference, and specificity was determined by comparing MS patients to HC. Results: TRACK-MS-R demonstrated high sensitivity (97.44%) when compared to the gold standard as represented by BICAMS-M for early and accurately detecting cognitive impairment in MS patients. Additionally, as a potential cognitive marker, TRACK-MS-R showed a specificity of 82.98% in distinguishing MS patients from healthy controls. Conclusions: TRACK-MS-R proves to be a highly sensitive and time-efficient screening tool for detecting cognitive impairment in patients with MS, while demonstrating good specificity compared to HC. Whereas high sensitivity is a prerequisite for a valid screening tool, its relatively modest specificity compared to BICAMS-M (62.9%) calls for caution in interpreting standalone results but instead indicates more extensive neuropsychological testing. Its briefness and diagnostic accuracy support its implementation in routine clinical practice, particularly in time-constrained settings. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

This paper analyzes the availability of lithium resources required to support a global decarbonized energy system featuring electrical energy storage based on lithium iron phosphate (LFP) batteries. A net-zero carbon grid consisting of existing nuclear and hydro capacity, with the balance being a 50/50 mix of wind and solar power generation, is assumed to satisfy projected world electrical demand in 2050, incorporating the electrification of transportation. The battery electrical storage capacity needed to support this grid is estimated and translated into the required number of nominal 10 MWh LFP storage plants similar to the ones currently in operation. The total lithium required for the global storage system is determined from the number of nominal plants and the inventory of lithium in each plant. The energy required to refine this amount of lithium is accounted for in the estimation of the total lithium requirement. Comparison of the estimated lithium requirements with known global lithium resources indicates that a global storage system consisting only of LFP plants would require only around 12.3% of currently known lithium reserves in a high-economic-growth scenario. The overall cost for a global LFP-based grid-scale energy storage system is estimated to be approximately USD 17 trillion. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO₂ emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments. Full article

Background/Objectives: This study shows a lightweight hybrid framework based on a feedforward neural network using a single frontopolar electroencephalography channel, which is a practical configuration for wearable systems combining memory and a sleep stage transition probability matrix. Methods: Motivated by autocorrelation analysis, revealing strong temporal dependencies across sleep stages, we incorporate prior epoch information as additional features. To capture temporal context without requiring long input sequences, we introduce a transition-aware feature derived from the softmax output of the previous epoch, weighted by a learned stage transition matrix. The model combines predictions from memory-based and no-memory networks using a confidence-driven fallback strategy. Results: The proposed model achieves up to 85.4% accuracy and 0.79 Cohen’s kappa, despite using only a single 30 s epoch per prediction. Compared to other models that use a single frontopolar channel, our method outperforms convolutional neural networks, recurrent neural networks, and decision tree approaches. Additionally, confidence-based rejection of low-certainty predictions enhances reliability, since most of the epochs with low confidence in the sleep stage classification contain transitions between sleep stages. Conclusions: These results demonstrate that the proposed method balances performance, interpretability, and computational efficiency, making it well-suited for real-time clinical and wearable sleep staging applications using battery-powered computing devices. Full article

This study investigated the effects of a single-night sleep extension protocol on physical performance and cognitive function in physically active university students across different times of day. Using a within-subjects, counterbalanced crossover design, 24 physically active university students (17 males, 7 females; age: 22.7 ± 1.6 years) completed performance assessments under normal-sleep and sleep-extension conditions. Participants’ sleep was monitored via wrist actigraphy, and a comprehensive assessment battery comprising vertical jumps, Y-Balance tests, medicine-ball throws, 5 m shuttle-run tests, reaction-time tests, and digit-cancellation tests was administered at baseline (8 PM), morning (8 AM), and afternoon (4 PM). Sleep extension increased total sleep time by approximately 55 min (531.3 ± 56.8 min vs. 476.5 ± 64.2 min; p < 0.001, d = 0.91). Significant improvements were observed in 5 m shuttle-run performance at 8 AM (best distance: 102.8 ± 11.9 m vs. 93.3 ± 8.5 m, p < 0.001, d = 0.93; fatigue index: 13.1 ± 8.3% vs. 21.2 ± 9.5%, p < 0.001, d = 0.90), squat-jump heights (28.2 ± 8.0 cm vs. 26.3 ± 7.2 cm, p = 0.005, d = 0.25), simple reaction time (252.8 ± 55.3 ms vs. 296.4 ± 75.2 ms, p < 0.001, d = 0.66), and digit-cancellation performance (67.6 ± 12.6 vs. 63.0 ± 10.0 targets, p = 0.006, d = 0.40). Sleep extension significantly enhances both physical and cognitive performance in physically active individuals, with effects more pronounced during morning hours, partially attenuating typical circadian performance decline and establishing sleep extension as an effective, non-pharmacological strategy for optimizing performance capabilities. Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

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