Search Results (492)

An accurate estimation of the state of health (SOH) of Li-ion batteries is critical for the efficient and safe operation of battery-powered systems. Traditional methods for SOH estimation, such as Coulomb counting, often struggle with sensitivity to measurement noise and time-consuming tests. This study addresses this issue by combining incremental capacity (IC) analysis and a novel neural network, Kolmogorov–Arnold Networks (KANs). Fifteen features were extracted from IC curves and a 2RC equivalent circuit model was used to identify the internal resistance of batteries. Recursive least squares were used to identify the parameters of the equivalent circuit model. IC features and internal resistance were considered as input variables to establish the SOH estimation model. Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. The performance of the five models was compared and analyzed. The results demonstrated that the hybrid models integrated with the KAN performed better than the conventional models, and the LSTM-KAN model had higher estimation accuracy than that of the other models. The model achieved a mean absolute error of less than 0.412% in SOH prediction in the test and validation dataset. The proposed model does not require complete charge and discharge data, which provides a promising tool for the accurate monitoring and fast detection of battery SOH. Full article

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency. Full article

The rise in biological therapies has revolutionized oncology, with immunotherapy leading the charge through breakthroughs such as CAR-T cell therapy for melanoma and B-ALL. Modified bispecific antibodies and CAR-T cells are being developed to enhance their effectiveness further. However, CAR-T cell therapy currently relies on a costly ex vivo manufacturing process, necessitating alternative strategies to overcome this bottleneck. Targeted in vivo viral transduction offers a promising avenue but remains under-optimized. Additionally, novel approaches are emerging, such as in vivo vaccine boosting of CAR-T cells to strengthen the immune response against tumors, and dendritic cell-based vaccines are under investigation. Beyond CAR-T cells, mRNA therapeutics represent another promising avenue. Targeted delivery of DNA/RNA using lipid nanoparticles (LNPs) shows potential, as LNPs can be directed to T cells. Moreover, CRISPR editing has demonstrated the ability to precisely edit the genome, enhancing the effector function and persistence of synthetic T cells. Enveloped delivery vehicles packaging Cas9 directed to modified T cells offer a virus-free method for safe and effective molecule release. While this platform still relies on ex vivo transduction, using cells from healthy donors or induced pluripotent stem cells can reduce costs, simplify manufacturing, and expand treatment to patients with low-quality T cells. The use of allogeneic CAR-T cells in cancer has gained attraction for its potential to lower costs and broaden accessibility. This review emphasizes critical strategies for improving the selectivity and efficacy of immunotherapies, paving the way for a more targeted and successful fight against cancer. Full article

Pulsed power equipment is often characterized by high energy density and field intensity. In the presence of strong electric field intensity, charge accumulation within insulators exacerbates electric field non-uniformity, leading to potential insulation breakdown, thereby posing a significant threat to the safe operation of pulsed power equipment. In this manuscript, we introduce nonlinear composite materials with field-dependent conductivity and permittivity to adaptively regulate the distribution of the pulsed electric field in insulation equipment. Finite-element modeling and analysis of the needle-plate electrodes and high-voltage bushing are carried out to comprehensively investigate the non-uniformity of the distribution of the electric field and the homogenization effect of various nonlinear materials in the presence of pulsed excitations of different timescales. Numerical results indicate that the involvement of nonlinear composite materials significantly improves the electric field distribution under pulse excitations. In addition, variations in the rising time of the pulses affect the maximum electric field intensity within the insulators considerably, but for pulses of nanosecond and microsecond scales, the tendencies are the opposite. Finally, via the simulations of the bushing, we illustrate that some measures proposed for improving the uniformity of the electric field under low frequencies, e.g., increasing the length of the electric field equalization layer and the distance of the underside of the electric field equalization layer from the grounding screen, are still effective for the homogenization of pulsed electric field. Full article

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions. Full article

The recent developments in electric vehicle (EV) necessities the requirement of a human intervention free charging system for safe and reliable operation. Wireless power transfer (WPT) technology shows promising options to automate the charging process with user convenience. However, the operation of the WPT system is designed to operate at a high-frequency (HF) range, which requires proper control and modulation technique to improve the performance of power electronic modules. This paper proposes a dead-time (DT) integrated Pulse Density Modulation (PDM) technique to provide better control with minimal voltage and current ripples at the switches. The proposed technique is investigated using a LCC-LCL compensated WPT system, which predominantly affects the high-frequency voltage and current ripples. The performance analysis is studied at different density conditions to explore the impact of the integrated PDM approach. Moreover, the PDM technique gives better control over the power transfer at different levels of load requirement. The simulation and experimental analysis was performed for a 3.7 kW WPT prototype test system under different modes of operation of the high-frequency power converters. Both the simulated and experimental results demonstrate that the proposed PDM technique effectively enhances the efficiency of the HF inverter while significantly reducing output current ripples, power dissipation and improving the overall WPT system efficiency to 92%, and leading to a reduction in the power loss in the range of 10% to 20%. This leads to improved overall system control and performance. Full article

High-nickel (Ni) ternary lithium-ion batteries (LIBs) are widely used in low-pressure environments such as in the aviation industry, but their attribute of high energy density poses significant fire hazards, especially under low pressure where thermal runaway behavior is complex, thus requiring relevant experiments. This study investigates the thermal runaway characteristics of LiNi_0.8Mn_0.1Co_0.1O₂ (NCM811) 18650 LIBs at different states of charge (SOCs) (75%, 100%) under various ambient pressures (101 kPa, 80 kPa, 60 kPa, 40 kPa). The results show that, as the pressure is decreased from 101 kPa to 40 kPa, the onset time of thermal runaway is extended by 28.2 s for 75% SOC and by 40.8 s for 100% SOC; accordingly, the onset temperature of thermal runaway increases by 19.3 °C for 75% SOC and by 33.5 °C for 100% SOC; the maximum surface temperature decreases by 70.8 °C for 75% SOC and by 68.2 °C for 100% SOC. The cell mass loss and loss rate slightly decrease with reduced pressure. However, ambient pressure has little impact on the time and temperature of venting as well as the voltage drop time. SEM/EDS analysis verifies that electrolyte evaporates faster under low pressure. Furthermore, the oxygen concentration is lower under low pressure, which consequently leads to a delay in thermal runaway. This study contributes to understanding thermal runaway characteristics of high-Ni ternary LIBs and provides guidance for their safe application in low-pressure aviation environments. Full article

Semiconductor superlattices are periodic nanostructures consisting of epitaxially grown quantum wells and barriers. For thick barriers, the quantum wells are weakly coupled and the main transport mechanism is a sequential resonant tunneling of electrons between wells. We review quantum transport in these materials, and the rate equations for electron densities, currents, and the self-consistent electric potential or field. Depending on superlattice configuration, doping density, temperature, voltage bias, and other parameters, superlattices behave as excitable systems, and can respond to abrupt dc bias changes by large transients involving charge density waves before arriving at a stable stationary state. For other parameters, the superlattices may have self-sustained oscillations of the current through them. These oscillations are due to repeated triggering and recycling of charge density waves, and can be periodic in time, quasiperiodic, and chaotic. Modifying the superlattice configuration, it is possible to attain robust chaos due to wave dynamics. External noise of appropriate strength can generate time-periodic current oscillations when the superlattice is in a stable stationary state without noise, which is called the coherence resonance. In turn, these oscillations can resonate with a periodic signal in the presence of sufficient noise, thereby displaying a stochastic resonance. These properties can be exploited to design and build many devices. Here, we describe detectors of weak signals by using coherence and stochastic resonance and fast generators of true random sequences useful for safe communications and storage. Full article

A wastewater treatment plant (WWTP) prototype coupled with Moringa oleifera seeds (MOSs) was developed to evaluate its effectiveness to reduce metallic trace elements (MTEs) in domestic wastewater. The WWTP is composed of a septic tank (F0) where wastewater is treated by biological processes under anaerobic conditions, followed by a bacterial filter (F1) where wastewater is filtered under aerobic conditions, followed by an infiltration well (F2), which provides additional filtration of wastewater before discharge into the soil. MTEs present in waters can bind with humic substances contained in colloid particles and then be eliminated by coagulation–flocculation with a cationic polyelectrolyte. MOSs contain positively charged cationic polymers that can neutralize the colloids contained in waters, which are negatively charged. Based on this observation, 300 mg·L⁻¹ of MOS was added into F0, 50 mg·L⁻¹ into F1, and 50 mg·L⁻¹ into F2 mg·L⁻¹. MOS activation in samples was performed by stirring rapidly for 1.5 min, followed by 5 min of gentle stirring and 3 h of settling. The data analysis shows that wastewater samples had significant concentrations of MTEs, particularly for Cu, Ni, Sr, and Ti, and sediment samples had high amounts of Cr, Cu, Ni, Sr, Ti, and V. The addition of MOS to F0, F1, and F2 samples resulted in reductions in MTE concentration of up to 36%, 71%, 71%, 29%, 93%, 81%, 13%, 52%, and 67% for Co, Cr, Cu, Ni, Pb, Se, Sr, Ti, and V, respectively. The quantified MTEs (As, Co, Cr, Cu, Ni, Pb, Se and V) in treated samples were reported to be lower than UN-EP standards for a safe reuse for irrigation and MOS proved to be as effective as chemical coagulants such as lime and ferric iron for the removal of MTEs contained in wastewater. These results highlight the potential of MOSs as natural coagulants for reducing MTE content in domestic wastewater. This study could be the first to evaluate the effectiveness of MOS in reducing 10 MTEs, including As, Co, Se, Sr, Ti, and V, which are currently understudied. It could also provide a better understanding of the origin of MTEs found in domestic wastewaters and how an effective treatment process can result in high-quality treated wastewaters that can be reused for irrigation without posing health or environmental risks. However, more research on MOSs is needed to determine the type and composition of the coagulant substance found in the seeds, as well as the many mechanisms involved in the decrease in MTEs by MOSs, which is currently understudied. A better understanding of MOS structure is required to determine the optimum alternative for ensuring the optimal effect of MOS paired with WWTP in removing MTEs from domestic wastewaters. Full article

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%. Full article

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO₂ battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. Full article

GaN HEMT devices are sensitive to the single event effect (SEE) caused by heavy ions, and their reliability affects the safe use of space equipment. In this work, a Ge ion (LET = 37 MeV·cm²/mg) and Bi ion (LET = 98 MeV·cm²/mg) were used to irradiate Cascode GaN power devices by heavy ion accelerator experimental device. The differences of SEE under three conditions: pre-applied electrical stress, different LET values, and gate voltages are studied, and the related damage mechanism is discussed. The experimental results show that the pre-application of electrical stress before radiation leads to an electron de-trapping effect, generating defects within the GaN device. These defects will assist in charge collection so that the drain leakage current of the device will be enhanced. The higher the LET value, the more electron–hole pairs are ionized. Therefore, the charge collected by the drain increases, and the burn-out voltage advances. In the off state, the more negative the gate voltage, the higher the drain voltage of the GaN HEMT device, and the more serious the back-channel effect. This study provides an important theoretical basis for the reliability of GaN power devices in radiation environments. Full article

In recent decades, taste sensors have been increasingly utilized to assess the taste of oral medicines, particularly focusing on bitterness, a major obstacle to patient acceptance and adherence. This objective and safe method holds promise for enhancing the development of patient-friendly medicines in pharmaceutical companies. This review article introduces its application in measuring the intensity of bitterness in medicine, confirming the achievement of taste masking, distinguishing taste differences between branded and generic medicines, and identifying substances to suppress bitterness in target medicines. Another application of the sensor is to predict a significant increase in bitterness when medicine is taken with certain foods/beverages or concomitant medication. Additionally, to verify the sensor’s predictability, a significant correlation has been demonstrated between the output of a bitter-sensitive sensor designed for drug bitterness (BT0) and the bitterness responses of the human taste receptor hT2R14 from BitterDB (huji.ac.il). As a recent advancement, a novel taste sensor equipped with lipid/polymer membranes modified by 3-Br-2,6-dihydroxybenzoic acid (2,6-DHBA), based on the concept of allostery, is introduced. This sensor successfully predicts the bitterness of non-charged pharmaceuticals with xanthine skeletons, such as caffeine or related compounds. Finally, the future prospects of taste sensors are discussed. Full article

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell’s temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery’s electrical and thermal limitations during the charging process. Full article

Electroactive microfiber-based scaffolds aid neural tissue repair. Carbon microfibers (CMFs) coated with the conducting polymer poly(3,4-ethylenedioxythiophene) doped with poly[(4-styrenesulfonic acid)-co-(maleic acid)] (PEDOT:PSS-co-MA) provide efficient support and guidance to regrowing axons across spinal cord lesions in rodents and pigs. We investigated the electrical and structural performance of PEDOT:PSS-co-MA-coated carbon MFs (PCMFs) for long-term, biphasic electrical stimulation (ES). Chronopotentiometry and electrochemical impedance spectroscopy (EIS) allowed the characterization of charge transfer in PCMFs during ES in vitro, and morphological changes were assessed by scanning electron microscopy (SEM). PCMFs that were 4 mm long withstood two-million-biphasic pulses without reaching cytotoxic voltages, with a 6 mm length producing optimal results. Although EIS and SEM unveiled some polymer deterioration in the 6 mm PCMFs, no significant changes in voltage excursions appeared. For the preliminary testing of the electrical performance of PCMFs in vivo, we used 12 mm long, 20-microfiber assemblies interconnected by metallic microwires. PCMFs-assemblies were implanted in two spinal cord-injured pigs and submitted to ES for 10 days. A cobalt–alloy interconnected assembly showed safe voltages for about 1.5 million-pulses and was electrically functional at 1-month post-implantation, suggesting its suitability for sub-chronic ES, as likely required for spinal cord repair. However, improving polymer adhesion to the carbon substrate is still needed to use PCMFs for prolonged ES. Full article