Power Performance Research Articles

Distributed Cooperative Control Strategy for Stable Voltage Restoration and Optimal Power Sharing in Islanded DC Microgrids

Conventionally, voltage regulation and economic dispatch in DC microgrid are performed at different time scales, which cannot keep the distributed generators (DGs) at the optimal operating point in real time. In this paper, a novel distributed cooperative control strategy is proposed to synchronously achieve stable bus voltage restoration and optimal power sharing in islanded DC microgrids. The proposed strategy breaks the constraints of hierarchical control strategies, and incorporates the objectives of voltage regulation and economic dispatch into a distributed cooperative control framework to eliminate average bus voltage deviation and minimize total power generation costs in real time. The stability of the proposed strategy is verified by eigenvalue analysis and steady-state analysis. Furthermore, the concept of “incremental cost (IC) margin” is introduced in cooperative control considering capacity constraints to avoid the converter output power violation and performance degradation. Finally, the MATLAB/Simulink simulation and StarSim HIL experimental results verify the effectiveness and robustness of the proposed strategy in an islanded DC microgrid under various testing scenarios.

The Effects of an 8-Week Strength Training Toward the Specific Preparation Phase on Male Volleyball Athletes’ Performance

Background. Training programs are part of training management that need to be properly and correctly prepared and implemented. Study Purpose. The aim of the study was to evaluate the effects of an 8-week program of resistance training, carried out at a specific preparation phase, on the performance of male junior volleyball athletes. Materials and methods. This study was conducted as experimental research, with a sample of 24 male volleyball athletes ([mean ± SD] age: 17.96 ± 1.23 years; body weight: 75.56 ± 7.80 kg; height: 180.46 ± 7.14 cm; body fat: 17.62 ± 3.36%) who were randomly divided into experimental group (EG): n = 12 and control group (CG): n = 12. The training program lasted for eight weeks and took place at a specific preparation phase. Results. There was a significant increase in the indicators handgrip dynamometer left (p<0.014), handgrip dynamometer right (p<0.001), vertical jump (p<0.001), sit-and-reach test (p<0.000), leg dynamometer (p<0.000), and back dynamometer (p<0.001) in the experimental group after being given strength training treatment for eight weeks. Conclusions. When strength training is included in volleyball training, it can increase the performance in power, strength, and flexibility, especially when carried out at a specific preparation phase. Incorporating targeted stimuli, such as strength training, into training sessions appears to be a secure method of training for this age group and may help to contribute to continuous and gradual improvements in neuromuscular adaptation.

Vascular System Inspired 3D Electrolyte Network for High Rate and High Mass Loading Graphene Supercapacitor

AbstractHigh charge rate and high mass loading ability are of practical importance for high power and high volumetric performance supercapacitors but are always challenged by tortuous and sluggish ion diffusion in thick electrodes. Here, inspired by the hepatic vascular system, hierarchical 3D electrolyte networks are proposed to facilitate ion diffusion across laminar graphene films, resulting in a 14‐fold improved ion diffusion coefficient, better rate ability, and high mass loading ability for graphene supercapacitors. Such design enables graphene electrodes with high capacitance up to 236 F g−1, packing densities of 0.67–0.78 g cm−3 depending on structures; and core stack with 10 mg cm−2 mass loading exhibits a high energy density of 45.4 Wh L−1, which is among the state‐of‐the‐art graphene supercapacitors. This study provides a comprehensive investigation of structure‐related capacitive properties, guiding electrode design for high‐rate and high‐mass‐loading graphene supercapacitors. Moreover, such design also benefits solid‐state graphene supercapacitors by forming 3D gel electrolyte structures, which enable higher capacitance and better mechanical robustness, showing potential for flexible energy storage.

Klasifikasi Jenis Cacat pada Kulit Menggunakan Arsitektur GoogLeNet

Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. This study aims to develop deep learning architecture for classifying leather defect. We used 3600 leather digital images distributed in six types of leather defects. In this study we employed GoogLeNet for classifying the data. Our experiment successfully achieved accuracy of 0.904 in training process and 0.885 in testing process. This result indicated that GoogLeNet provided powerful performance for classifying the type of leather defects.

General Separation of Carbon Dots by Polyamide Chromatography.

Carbon dot (C-dot) separation/purification is not only a fundamental chemical issue but also an essential precondition for revealing C-dots' true nature. To date, adequate separation of C-dots has remained an open question due to the lack of an appropriate fine separation system. Herein, we discover and reveal that polyamide chromatography can provide versatile and powerful performances for C-dot separation. By a joint study of experiments and all-atom molecular dynamics simulations, we demonstrate that multiple interaction forces, including electrostatic repulsion/attraction, hydrogen bond, and van der Waals effects, exist simultaneously among the stationary phase, mobile phase, and the separated C-dots. Furthermore, the magnitude of these forces is dependent on the surface chemistry of the separated C-dots and the nature of the used mobile phases, providing a theoretical basis and experimental operability for C-dot separation. So, the proposed system possesses the capacity for adequately separating hydrophilic, amphiphilic, and lipophilic C-dots. The polyamide chromatography, due to its versatile and powerful separation performances, not only provides more thorough separation effects but also helps to correct our false perceptions from inadequate purified C-dots.

Performance Evaluation of Power Operated Chaff Cutter for Selected Fodder Crops

The study was conducted to evaluate the performance of power operated chaff cutter for three types of fodder crops, viz., jowar, maize, and sorghum, under load and no-load conditions during kharif (July to October, 2022) at Farm Machinery Testing Centre (FMTC), University of Agricultural Sciences, Raichur, Karnataka (16.2051°N,77.3303°E). The hardness of the blade was recorded to be 54 HRC, and the rate of wear of blades on a mass basis and a dimensional basis was recorded as 0.013% - 0.026% and 0.028% - 0.047% per hour, respectively. The power consumption of the chaff cutter at no load was recorded as 0.42 kWh. Similarly, the quantity of cut fodder received was measured as 621 - 623 kg h-1 and 1710 - 1816 kg h-1 for dry and wet fodder, respectively. The maximum specific energy consumption was recorded as 1123.21 - 2396.0 kg kWh-1 in dry and wet fodder, respectively. The quality of cut ranged from 0.38 - 0.42 and 0.41 - 0.43 in dry and wet fodder, respectively. The performance index of chaff cutters was recorded as 471.37 and 1006.34 for dry and wet fodder, respectively. The maximum efficiency was recorded as 96.62 - 97.87% and 97.50 - 98.10% for dry and wet fodder, respectively. The power-operated chaff cutter was operated for a long-run test (23.00 h) for cutting jowar (dry), maize, and sugarcane crops in both wet and dry conditions was done. No breakdown in the cutter head, feeding mechanism, transmission systems, body of the chaff cutter was noticed.

Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance

The present study utilizes high-fidelity large-eddy simulation (LES) to examine the upwind-hill impacts on wind-turbine wakes and power performance. Both the LES and Reynolds-Averaged Navier–Stokes (RANS) show good agreement with the experimental results of average velocity, whereas the LES exhibits great improvements in predicting turbulence characteristics. In turbine simulations, the wake half-width shows different behaviors in the hill and flat cases. The Gaussian and cosine distributions are good representations of the unskewed profiles of velocity deficit, although some discrepancies are seen around the wake edges. However, the spanwise profiles of velocity deficit become skewed in some hill cases. The non-zero pressure gradient (NZPG) model is employed to predict the velocity deficit in the wakes of a hilltop-sited turbine, and a generalized condition is proposed so that the NZPG model can work in the scenario where the pressure gradients at the turbine location or in its near wakes are not zero. Appropriately assigning initial values for the NZPG model is important for its successful use. The presence of the upwind hill has an adverse influence on the power output of the hilltop-sited turbine. The installation of wind turbines in the valley region near upwind hills is not recommended due to the limited wind-energy resources and intense turbulence levels.

Improvement in Laptop Heat Dissipation with Taguchi Method

This paper aims to investigate the feasibility of using system power consumption as a factor to improve laptop heat dissipation. The problems due to the CPU overheating are addressed. Based on the Taguchi method, the laptop fan parameters can be optimized with firmware adjustments only. In the Taguchi analysis, the fan speed, system power, and debounce time are considered as control factors, while the Cinebench point is utilized to evaluate the CPU performance. Experimental results demonstrate that the proposed heat dissipation scheme effectively reduces the idle time of a laptop fan. The improvement in heat dissipation can reduce CPU performance degradation because of overheating. According to the best combination of control factors, there is approximately a 5% increase in CPU performance despite a 0.35% increment in power consumption. This paper highlights the effectiveness of optimizing laptop fan parameters through firmware adjustments to improve heat dissipation and mitigate CPU overheating issues. Moreover, the study highlights the delicate balance between power consumption and performance gains. While there may be a slight increase in power consumption associated with the optimized heat dissipation scheme, the observed improvements in CPU performance outweigh this incremental power usage.

Smart current control of the wind energy conversion system based permanent magnet synchronous generator using predictive and hysteresis model

Introduction. Given the increasing demand for performance and efficiency of converters and power drives, the development of new control systems must take into account the real nature of these types of systems. Converters and dimmers power are nonlinear systems of a hybrid nature, including elements linear and nonlinear and a finite number of switching devices. Signals input for power converters are discrete signals that control the ‘opening and closing’ transitions of each component. Problem. In the multilevel inverters connected to grid, the switching frequency is the principal cause of harmonics and switching losses, which by nature, reduces the inverter’s efficiency. Purpose. For guarantee the satisfying quality of power transmitted to the electrical grid, while ensuring reduction of current ripples and output voltage harmonics. Novelty. This work proposes a new smart control, based on a predictive current control of the three level neutral point clamped inverter, used in Wind Energy Conversion System (WECS) connected to grid, based permanent magnet synchronous generator, powered by a hysteresis current control for the rectifier. This new formula guarantees handling with the influence of harmonics disturbances (similar current total harmonic distortion), voltage stress, switching losses, rise time, over or undershoot and settling time in WECS. Methods. The basic idea of this control is to choose the best switching state, of the power switches, which ameliorates the quality function, selected from order predictive current control of WECS. Results. Practical value. Several advantages in this intelligent method, such as the fast dynamic answer, the easy implementation of nonlinearities and it requires fewer calculations to choose the best switching state. In addition, an innovative algorithm is proposed to adjust the current ripples and output voltage harmonics of the WECS. The performances of the system were analyzed by simulation using MATLAB/Simulink.

Prognostic factors and clinical survival outcome in patients with primary mediastinal diffuse large B-cell lymphoma in rituximab era: A population-based study.

The goal of this study was to investigate the clinical characteristics, prognostic variables, and survival of patients with primary mediastinal diffuse large B cell lymphoma (PMBCL) in the rituximab era. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PMBCL patients diagnosed between 2000 and 2019. The Kaplan-Meier (K-M) technique and log-rank test were used to assess overall survival (OS) and disease-specific survival (DSS). The independent prognostic variables for OS and DSS were identified using univariate and multivariate Cox regression analysis. Nomograms were created to predict survival prospects according to identified prognostic indicators. Totally, 841 patients were enrolled with PMBCL. One-year, 5-year, and 10-year OS rates were 93.99%, 85.04%, and 81.76%, and the corresponding DSS rates were 95.27%, 87.37%, and 85.98%. The results of multivariate Cox regression analysis demonstrated that age, years of diagnosis, Ann arbor staging, and chemotherapy were independent prognostic factors for survival. Nomograms designed exclusively for PMBCL were created to forecast the likelihood of 1-year, 5-year, and 10-year OS and DSS, respectively. The Harrell concordance index (C-index) for the nomograms predictions of OS and DSS were 0.704 and 0.733, respectively, which showed the established model harboring powerful and accurate performance. The present study revealed that incidence of PMBCL has been consistently rising over the last 20 years. Simultaneously, survival rates have improved tremendously. Rituximab based immunochemotherapy has emerged as an effective treatment option, leading to enhanced OS and DSS outcomes. Furthermore, the nomograms specifically developed for PMBCL have demonstrated robustness and accuracy in forecasting OS and DSS rates at 1, 5, and 10 years. These predictive tools can be valuable for clinicians in accurately estimating prognosis and establishing personalized treatment plans and follow-up protocols.

Dynamic modeling and experimental study of multi-coil composite core energy harvester

To improve the output performance and working bandwidth of the magnetic levitation energy harvester, a new structure of multi-coil composite core energy harvester was proposed and verified by numerical simulation and experiment. Firstly, the working mechanism of the energy harvester was analyzed. The dynamics model and COMSOL Multiphysics finite element analysis model were established to study the influence of magnetic field and coil position on the output characteristics of the energy harvester. Then, an experimental platform was built to verify the proposed new energy harvester, and the influence of coil position on the output power of the energy harvester was explored. Three kinds of coil thicknesses, two kinds of coil winding directions, six kinds of coil connection modes and two kinds of magnet distribution experiments were designed. Finally, the influence of suspended core mass on output performance was analyzed, and the energy harvester with or without magnetic liquid was tested by sweeping frequency to evaluate its output performance. The experimental results show that the magnetic field near the coil can be enhanced effectively, and the output performance of the whole system can be improved by the magnet mutual repulsion energy harvester with coil connection mode CM1 in a consistent coil direction. With the increase of composite core mass, the output voltage and power of the energy harvester also increase, but the peak frequency decreases. Compared with the energy harvester without magnetic liquid, the output power performance of the designed multi-coil composite core energy harvester is significantly improved by 29%, and the frequency bandwidth growth rate is 73%. The normalized power density can reach 134.26 μWcm−3g−2. The new structure of the multi-coil composite core provides important reference significance for designing a more efficient magnetic levitation energy harvester.

An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning

Polynomial dimensional decomposition (PDD) is a surrogate method originated from the ANOVA (analysis of variance) decomposition, and has shown powerful performance in uncertainty quantification (UQ) accuracy and convergence recently. However, complex high-dimensional problems result in a large number of polynomial basis functions, leading to heavy computational burden, and the probability distributions of the input random variables are indispensable for PDD modeling and UQ, which may be unavailable in practical engineering. This study establishes an adaptive data-driven subspace PDD (ADDSPDD) for high-dimensional UQ, which employs two types of data for modeling the PDD basis function and the low-dimensional subspace directly, namely, the data of input random variables and the input-response samples. Firstly, we propose a data-driven zero-entropy criterion-based maximum entropy method for reconstructing the probability density functions (PDF) of input variables. Then, with the aid of the established PDFs, a data-driven subspace PDD (DDSPDD) is proposed based on the whitening transformation. To recover the subspace of the function of interest accurately and efficiently, we put forward an approximate active subspace method (AAS) based on the Taylor expansion under some mild premises. Finally, we integrate an adaptive learning algorithm into the DDSPDD framework based on the sparse Bayesian learning theory, obtaining our ADDSPDD; thus, the real subspace and the significant PDD basis functions can be identified with limited computational budget. We validate the proposed method by using four examples, and systematically compare four existing dimension-reduction methods with the AAS. Results show that the proposed framework is effective and the AAS is a good choice when the corresponding assumptions are satisfied.

Design and evaluation of low power and area efficient approximate Booth multipliers for error tolerant applications

Approximate computing is an innovative design methodology to reduce the design complexity with an improvement in power efficiency, performance and area by compromising on the requirement of accuracy. In this paper, 8-bit approximate Booth multipliers have been proposed based on the approximate Radix-4 modified Booth encoding algorithm and approximate compressors for partial product accumulation to produce the final products are proposed. Two approximate Probability Based Booth Encoders (PBBE-1 and PBBE-2) have been proposed and used in the Booth multipliers. Error parameters have been measured and compared with the existing approximate booth multipliers. Exact booth multiplier of novel design existing in the literature has also been implemented for comparison purpose. The proposed approximate multipliers are then used in applications like image multiplication and IIR bi-quad filtering to prove their performance. Simulation results prove that the proposed booth multipliers outperform the existing approximate booth multipliers in terms of power and area with better accuracy. Synthesis results prove that the proposed Multiplier 6 was found to be the most efficient with a 56 % power consumption improvement and a 47 % area improvement when compared to the exact multiplier. All the simulations are carried out using Cadence® Genus with 180 nm CMOS process technology.

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a tumor. ITH was involved in the cancer progression, aggressiveness, therapy resistance and cancer recurrence. Integrative machine learning procedure including 10 methods was conducted to develop an ITH-related signature (IRS) in The Cancer Genome Atlas (TCGA), GSE54467, GSE59455 and GSE65904 cohort. Several scores, including tumor immune dysfunction and exclusion (TIDE) score, tumor mutation burden (TMB) score and immunophenoscore (IPS), were used to evaluate the role of IRS in predicting immunotherapy benefits. Two immunotherapy datasets (GSE91061 and GSE78220) were utilized to the role of IRS in predicting immunotherapy benefits of skin cutaneous melanoma (SKCM) patients. The optimal prognostic IRS constructed by Lasso method acted as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in SKCM, with the area under the curve of 2-, 3- and 4-year receiver operating characteristic curve being 0.722, 0.722 and 0.737 in TCGA cohort. We also constructed a nomogram and the actual 1-, 3- and 5-year survival times were highly consistent with the predicted survival times. SKCM patients with low IRS scores had a lower TIDE score, lower immune escape score and higher TMB score, higher PD1&CTLA4 IPS. Moreover, SKCM patients with low IRS scores had a lower gene sets score involved in DNA repair, angiogenesis, glycolysis, hypoxia, IL2-STAT5 signaling, MTORC1 signaling, NOTCH signaling and P53 pathway. The current study constructed a novel IRS in SKCM using 10 machine learning methods. This IRS acted as an indicator for predicting the prognosis and immunotherapy benefits of SKCM patients.

Remote sensing scene classification using multi-domain sematic high-order network

Recently, convolutional neural networks (CNNs), which obtain powerful deep features in an end-to-end manner, have achieved powerful performance in remote sensing scene classification. However, the average or maximum pooling operations defined in the spatial domain and coarser-resolution features with high levels cannot extract reliable features and clear boundaries for small-scale targets in remote sensing scene imagery. This paper attempts to address these problems and proposes a multi-domain sematic high-order network for scene classification, named MSHNet. First, wavelet-spatial and detachable pooling blocks defined in the wavelet and spatial domains are inserted at the end of the convolutional block to learn the features in a more structural fusion manner. Second, multi-scale and multi-resolution semantic embedding modules are proposed to take full advantage of the complementary information and effectively maintain the spatial structures of learned deep features. Third, we employ a factorized bilinear coding approach to obtain compact and discriminative second-order features. MSHNet is thoroughly evaluated on two publicly available benchmarks, i.e., AID (Aerial Image Dataset) and NWPU-RESISC45 (Northwestern Polytechnical University-Remote Sensing Image Scene Classification 45). The extensive results illustrate that our MSHNet is competitive with other related multi-scale deep neural networks.

Two-dimensional metal–organic framework nanocomposite membranes with shortened ion pathways for enhanced salinity gradient power harvesting

Harnessing energy through the reverse electrodialysis-based salinity gradient power conversion is an exciting and promising technological endeavor. The key to its success lies in developing the advanced ion-selective membrane capable of enhancing ion flux and accelerating ion transport at confined nanospaces. Traditional two-dimensional (2D) membranes typically employ a layer-by-layer arrangement of nanosheets with an extremely narrow layer separation, limiting ion flux and creating elongated 2D ion pathways. In response to this limitation, our study introduces an innovative design of the 2D metal–organic framework (MOF)-based nanocomposite membrane (named as Cu-TCPP@SNF), which is composed of the 2D Cu-TCPP and natural-based silk nanofibers (SNFs). We show that the introduction of the space-charged SNFs can significantly enhance the stability of the 2D MOF nanocomposite membrane in electrolyte solutions, and this unique framework facilitates a multitude of abundant one-dimensional ion pathways across the membrane while maintaining the advantage of minimal layer separation. Our results demonstrate that the Cu-TCPP@SNF exhibits low resistance, thus facilitating ion transport. Furthermore, numerical simulations elucidate the impacts of the SNF content and membrane thickness on power performance. Remarkably, our membrane achieves a notable power density of ∼6.48 W/m2 by mixing artificial seawater and river water. Impressively, an ultrahigh performance of ∼29.5 W/m2 can be achieved under a 5 M/0.01 M NaCl gradient, surpassing the existing advanced membranes. These findings hold significant promise for the future of composite 2D membrane development, paving the way for scalable applications in salinity-based energy harvesting.

Improvements to adversarial training for text classification tasks

Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks.

Performance analysis of two generations of heaving point absorber WECs in farms of hexagon-shaped array layouts

ABSTRACT Numerical analyses are presented for two generations of a floating heaving point absorber wave energy converter (WEC) installed with different farm array layouts. The wave farm configurations are based on WECs developed by Waves4Power. The numerical models are developed in the DNV software package, Sesam. Parametric studies of the isolated WEC configurations and farm array layouts are conducted under typical environmental conditions and various incident wave directions to understand the hydrodynamic power performance and the levelised cost of energy (LCoE). Hexagonal layouts are proposed for deploying the WEC units and compared with a 10-unit layout termed StarBuoy, which has been reported in previous work. The results of the present study confirm that the interactions between arrayed units in a farm can have either positive or negative effects on the LCoE, which is dependent on the array layout and environmental conditions. The hexagonal array layouts lead to lower LCoE owing to constructive interaction effects.

Predicting the level of technical performance by indicating the Hook (side punch) and, the blasting power of arms as the basis of selecting juniors in boxing


 The problem of the research is lurking in informing the researchers and their inquiries about the function of the coaches in the institutions that are specialized in preparing and developing the junior players and the modest experience of researchers. They observed a phenomenon lying in the randomness of coaches in picking their players depending on subjective judgments and lack of using objective tests, in the addition to the rarity of studies that pertain to predicting this age groups in terms of physical and skill indicators which do not determine the suitable standards for their age level. The research aimed to identify the level of the Hook (side punch) performance and the blasting power of arms for the junior players and finding the correlational between the Hook (side punch) test and the blasting power of arms and finding scales (grades and levels) for the used tests and deriving predictive formula for the technical performance by indicating the Hook (side punch) and, the blasting power of arms as the basis of selecting juniors in boxing .The researchers assumed that there is a correlation between the Hook (side punch) test and the blasting power of arms, they used the descriptive method due to its relevance to the nature of the problem, the researchers identified the research population by using the purposive method which are the junior sixteen players of Hilla sport club .The research sample had been selected in the random method which are fourteen players formed 87.5% percentage from the original research population .The researchers used a set of tests for blasting power and technical performance of the Hook (side punch) skill when they concluded :Researchers found the measurement of the Hook (side punch) and the blasting power of the arms for the junior players of boxing.Calibers had been found (grades and levels for the used tests).Correlations and predicting formulas had been found for technical performance level.The researchers recommended the following 1-Coaches and experts should concentrate on the blasting power of arms in selecting talented juniors in boxing.2- Coaches should pay attention to the mechanism of selecting and choosing talented players in the other sports in term of focusing on the main characteristic of the game.3-Doing other studies and researches care about perfect mechanism in selecting talented players of different sports.
 
 

Lithium-Ion Battery Power Performance Assessment for the Climb Step of an Electric Vertical Takeoff and Landing (eVTOL) Application.

High power is a critical requirement of lithium-ion batteries designed to satisfy the load profiles of advanced air mobility. Here, we simulate the initial takeoff step of electric vertical takeoff and landing (eVTOL) vehicles powered by a lithium-ion battery that is subjected to an intense 15C discharge pulse at the beginning of the discharge cycle followed by a subsequent low-rate discharge. We conducted extensive electrochemical testing to assess the long-term stability of a lithium-ion battery under these high-strain conditions. The main finding is that despite the performance recovery observed at low rates, the reapplication of high rates leads to drastic cell failure. While the results highlight the eVTOL battery longevity challenge, the findings also emphasize the need for tailored battery chemistry designs for eVTOL applications to address both anode plating and cathode instability. In addition, innovative second-use strategies would be paramount upon completion of the eVTOL services.