Detailed Circuit Research Articles

Capacity estimation of lithium-ion battery through interpretation of electrochemical impedance spectroscopy combined with machine learning

Lithium-ion batteries inevitably undergo degradation over extended use, making precise capacity estimation essential for reliable state monitoring and health prognostics. However, conventional capacity estimation methods, which rely predominantly on time-domain information, offer limited insights due to the complex degradation mechanisms intrinsic to lithium-ion batteries. Accordingly, a capacity estimation approach leveraging partial frequency electrochemical impedance spectroscopy data integrated with machine learning is proposed. A reconstructed simplified fractional-order model characterized by a minimal set of parameters and superior fitting performance is introduced to extra health indicators from EIS measurement. Detailed equivalent circuit parameter extraction is performed, and subsequently incorporated into an enhanced Gaussian process regression model for capacity estimation. The proposed approach is validated using datasets derived from real-driving profiles including comprehensive comparisons, demonstrating its feasibility and effectiveness. Experimental results confirm that the framework achieves high estimation accuracy, strong generalization, and robust performance, even in scenarios with limited data availability and training noise presence, with a root mean square error not exceeding 0.81 %, a mean absolute error below 0.62 %, and a maximum absolute error of no more than 0.0653 Ah.

Design of a Broadband and High-Isolation CMOS Active Quasi-Circulator

Using industry standard TSMC 90 nm CMOS technology, a broadband and high-isolation active quasi-circulator MMIC was fabricated to meet the needs of future communication system integration. An active balun and phase inverter stage made of a common-source (CS) field-effect transistor (FET) comprised the quasi-circulator that was suggested. A cascade CS stage was used to produce the active balun, which increased the isolation of |S12| and |S23| through the unilateral characteristic of the FET and allowed for transmission of |S21| and |S32|. Further, a CS FET connected in parallel to ports 1 and 3 for phase cancellation can effectively increase the quasi-circulator’s isolation |S31|. Theoretical analysis for the detailed circuit was shown in the main paper. The results show that all isolations are better than 20 dB over 5–33 GHz, and the measured findings reveal that the suggested quasi-circulator has an insertion loss of less than 10 dB. It is possible to reach an isolation level of 55 dB between ports 1 and 3, which is greater than present research.

A brainstem circuit amplifies aversion.

Dynamic gain control of aversive signals enables adaptive behavioral responses. Although the role of amygdalar circuits in aversive processing is well established, the neural pathway for amplifying aversion remains elusive. Here, we show that the brainstem circuit linking the interpeduncular nucleus (IPN) with the nucleus incertus (NI) amplifies aversion and promotes avoidant behaviors. IPN GABA neurons are activated by aversive stimuli and their predicting cues, with their response intensity closely tracking aversive values. Activating these neurons does not trigger aversive behavior on its own but rather amplifies responses to aversive stimuli, whereas their ablation or inhibition suppresses such responses. Detailed circuit dissection revealed anatomically distinct subgroups within the IPN GABA neuron population, highlighting the NI-projecting subgroup as the modulator of aversiveness related to fear and opioid withdrawal. These findings unveil the IPN-NI circuit as an aversion amplifier and suggest potential targets for interventions against affective disorders and opioid relapse.

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

Readout circuit noise analysis and low-noise design for piezoelectric micro-machined ultrasonic transducers with large parasitic capacitance.

Readout circuits are fundamental components in many application systems that utilize piezoelectric micro-machined ultrasonic transducers (pMUTs). This study models the noise and signal transfer functions of trans-impedance amplifiers (TIAs), charge-sensitive circuits, and voltage-mode readout circuits in detail. A series of simulations and experiments were conducted to elucidate the advantages and disadvantages of these circuit types. Both theoretical and experimental results indicate that the intrinsic capacitance of large pMUTs can significantly degrade the quality of the readout signal. Furthermore, while the TIA-based readout circuit demonstrates clear gain advantages, it is also susceptible to considerable noise interference. This work proposes an improved readout circuit design that effectively mitigates noise interference while preserving the gain advantages of the TIA architecture. The implemented prototype circuit successfully reduces the noise from 73 mVp-p to 13 mVp-p.

An Analytical Model for an Anode-Supported Solid Oxide Fuel Cell

This study presents a one-dimensional, non-isothermal, steady-state model to analyze performance of an anode-supported solid oxide fuel cell (SOFC). The modeling domain encompasses components including a NiO-YSZ support and functional layer forming the anode, a YSZ electrolyte, a GDC interlayer, as well as LSCF+GDC and LSCF constituting the cathode. The model distinguishes different types of electrode polarization resistances using experimental impedance data at various temperatures under open circuit voltage (OCV), employing a detailed equivalent circuit model to determine the temperature and partial pressure dependencies of electrode exchange current densities. Moreover, this approach serves as an efficient method for estimating the conductivity of both the anode and cathode. As a result, activation energies and ohmic overpotentials are derived, considering microstructural and geometrical aspects, which allows for accurate quantification of the ohmic resistance associated with ionic conduction.The model incorporates coupled multi-physics elements, such as mass and charge transport, electrochemical reactions, and heat transfer. A notable feature of the model is its consideration of reaction zone layers near the electrolyte, where electrochemical reactions generate electrons, oxide ions, and water vapor. Enhancing prediction accuracy, the model applies multilayer discretization within the electrode and electrolyte to reflect actual material properties and localized conditions more accurately. The model predicts the performance of a single-cell SOFC using pure hydrogen at the anode and varying oxygen concentrations (100%, 50%, 20%, and 10%) at different operating temperatures (700°C, 660°C, 620°C, and 580°C) and design conditions. Simulated results are compared with experimental data, and the impact of various operating and design parameters on SOFC performance is examined. Findings indicate that kinetic and concentration overpotentials in an anode-supported SOFC are lower than ohmic overpotentials, even at higher current densities. Furthermore, ohmic overpotential is identified as the primary contributor to total cell potential loss, highlighting the need for its minimization to improve cell performance.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain’s ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks’ dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Flexible gating between subspaces in a neural network model of internally guided task switching.

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

A bistable inhibitory optoGPCR for multiplexed optogenetic control of neural circuits

Information is transmitted between brain regions through the release of neurotransmitters from long-range projecting axons. Understanding how the activity of such long-range connections contributes to behavior requires efficient methods for reversibly manipulating their function. Chemogenetic and optogenetic tools, acting through endogenous G-protein-coupled receptor pathways, can be used to modulate synaptic transmission, but existing tools are limited in sensitivity, spatiotemporal precision or spectral multiplexing capabilities. Here we systematically evaluated multiple bistable opsins for optogenetic applications and found that the Platynereis dumerilii ciliary opsin (PdCO) is an efficient, versatile, light-activated bistable G-protein-coupled receptor that can suppress synaptic transmission in mammalian neurons with high temporal precision in vivo. PdCO has useful biophysical properties that enable spectral multiplexing with other optogenetic actuators and reporters. We demonstrate that PdCO can be used to conduct reversible loss-of-function experiments in long-range projections of behaving animals, thereby enabling detailed synapse-specific functional circuit mapping.

Performance evaluation of coupled-inductor DC-DC converter for wind energy conversion system

ABSTRACT This paper studies the performance of single-switch-coupled inductor-based quadratic DC-DC converter employed in the machine side converter of wind energy conversion system. The performance of the converter is evaluated in terms of voltage conversion ratio, voltage and current stress on the switch of the converter. The detailed circuit analysis, power loss analysis, modelling and integration issues have been elaborated in this work. The DC-link voltage regulation of wind energy conversion system is carried out using closed-loop control scheme including maximum power point tracking scheme and dual-loop control scheme. Simulation results and experimental results have been provided in this work.

A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

A simple zero-voltage switching technique for a single-phase inverter based on bi-directional inductor current

The majority of soft-switching inverters often use auxiliary semiconductor switches that run for extremely brief periods to provide ZVS conditions for the main switches, potentially raising the inverter system’s cost. This work presented a simple zero-voltage switching (ZVS) approach by employing a bi-directional inductor current for the single-phase inverter, which reduced the cost of auxiliary switches and simplified the soft-switching inverter design. All switches can achieve ZVS operation without additional resonant components. The conventional PWM control strategies are applied in the proposed technique, and thus the problems existing in variable switching frequency converters are removed. This article describes the soft-switching approach and detailed circuit functioning. Different key parameters are optimized to improve efficiency. To confirm the suggested ZVS method, a hardware inverter prototype is created, built, and tested. To validate the inverter’s characteristics and validity, experimental results are presented.

Design of Preamplifier for Ultrasound Transducers

In diagnostic ultrasound imaging applications, preamplifiers are used as first-stage analog front-end amplifiers for ultrasound transducers because they can amplify weak acoustic signals generated directly by ultrasound transducers. For emerging diagnostic ultrasound imaging applications, different types of preamplifiers with specific design parameters and circuit topologies have been developed, depending on the types of the ultrasound transducer. In particular, the design parameters of the preamplifier, such as the gain, bandwidth, input- or output-referred noise components, and power consumption, have a tradeoff relationship. Guidelines on the detailed design concept, design parameters, and specific circuit design techniques of the preamplifier used for ultrasound transducers are outlined in this paper, aiming to help circuit designers and academic researchers optimize the performance of ultrasound transducers used in the diagnostic ultrasound imaging applications for research directions.

Design and Implementation of a High-gain Switched-capacitor Step-up Multi-level Inverter with Reduced Components Count

Emergence of compact design power converters for applications such as photovoltaic system, active power filter, and high-frequency AC system has introduced multi-level inverters (MLIs). In the last decade, rigorous research has been carried out to transform multi-input MLIs to single-input step-up type that will be extremely beneficial in different applications. The proposed MLI in this work is a reduced components based switched-capacitor MLI. It consists of one input source and using the capacitor voltage, it can produce a four-time step-up output. One of the capacitors is charged to magnitude the input voltage and the other two capacitors are charged to two-time the input voltage magnitude. The charging of all the capacitors is executed by utilizing them in series-parallel combination with the switching operation. By maintaining charging–discharging effectively within each cycle, the voltages are naturally balanced. A thorough comparison is carried to justify the new development in terms of reduction in number of components and voltage stress. The power loss is estimated in the proposed circuit in detail. Extensive analysis is carried out to verify the operational ability and results are provided under different scenarios such as change in modulation index, load, and supply side variations.

New single‐stage bidirectional three‐phase ac‐dc solid‐state transformer

AbstractHigh‐power applications with low‐voltage (LV) dc loads, for example, fast charging stations for electric vehicles (EVs), are typically supplied from the medium‐voltage (MV) grid. Aiming for low volume, MVac‐LVdc solid‐state transformers (SSTs) that provide galvanic separation with medium‐frequency transformers (MFTs) are thus considered, which are conventionally realized as two‐stage systems that consist of an ac‐dc power‐factor‐correction (PFC) rectifier and an isolated dc‐dc converter. This letter extends a new single‐stage isolated bidirectional PFC rectifier concept to MV levels, resulting in an SST that performs MVac‐LVdc conversion in a single converter stage and, unlike many modular SST concepts, employs only a single MFT. The SST's primary side operates modular‐multilevel‐converter (MMC) bridge legs, whose input voltages contain large ac components, in the quasi‐two‐level mode to minimize the cell capacitors. The secondary side employs a standard LV three‐phase rectifier, and a dual‐active‐bridge‐(DAB)‐like modulation strategy allows power flow regulation and ensures sinusoidal grid currents. The concept is explained and validated using detailed circuit simulations of an exemplary 1‐MW system operating between a 10‐kV three‐phase grid and an 800‐V dc output. The component stresses are evaluated, and an efficiency of 98.1% and a power density of up to 0.6 kW/dm3 are estimated.

Circuit Simulator Implementation of an Equivalent Circuit Model of Self‐Assembled Electret Vibrational Energy Harvesters Based on An Energy Diagram

This paper reports an equivalent circuit model of self‐assembled electret (SAE) micro‐electro‐mechanical systems (MEMS) vibrational energy harvesters (VEHs). For the first time, a detailed equivalent circuit model of the SAE‐MEMS VEH has been implemented on a circuit simulator, considering the device energy diagram. The implemented equivalent circuit model allows analysis of the relationship between output current and SAE properties, device structures, and electrode materials. The validity of the model is confirmed by comparing the measurement and simulation results. Thus, the proposed model would be useful for the design of SAE‐MEMS VEHs. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Behavioral, neural and ultrastructural alterations in a graded-dose 6-OHDA mouse model of early-stage Parkinson's disease

Studying animal models furthers our understanding of Parkinson’s disease (PD) pathophysiology by providing tools to investigate detailed molecular, cellular and circuit functions. Different versions of the neurotoxin-based 6-hydroxydopamine (6-OHDA) model of PD have been widely used in rats. However, these models typically assess the result of extensive and definitive dopaminergic lesions that reflect a late stage of PD, leading to a paucity of studies and a consequential gap of knowledge regarding initial stages, in which early interventions would be possible. Additionally, the better availability of genetic tools increasingly shifts the focus of research from rats to mice, but few mouse PD models are available yet. To address these, we characterize here the behavioral, neuronal and ultrastructural features of a graded-dose unilateral, single-injection, striatal 6-OHDA model in mice, focusing on early-stage changes within the first two weeks of lesion induction. We observed early onset, dose-dependent impairments of overall locomotion without substantial deterioration of motor coordination. In accordance, histological evaluation demonstrated a partial, dose-dependent loss of dopaminergic neurons of substantia nigra pars compacta (SNc). Furthermore, electron microscopic analysis revealed degenerative ultrastructural changes in SNc dopaminergic neurons. Our results show that mild ultrastructural and cellular degradation of dopaminergic neurons of the SNc can lead to certain motor deficits shortly after unilateral striatal lesions, suggesting that a unilateral dose-dependent intrastriatal 6-OHDA lesion protocol can serve as a successful model of the early stages of Parkinson’s disease in mice.

The Design of a Low-Noise, High-Speed Readout-Integrated Circuit for Infrared Focal Plane Arrays

This paper describes the design of a low-noise, high-speed readout-integrated circuit for use in InGaAs infrared focal plane arrays, and analyzes the working principle and noise index of the pixel circuit in detail. The design fully considers the dynamic range, noise, and power consumption of the pixel circuit in which a capacitance transimpedance amplifier structure is adopted as the input stage circuit, and chip fabrication via an XFAB 0.18 µm CMOS process is successfully realized. The ROIC adopts monolithic integration and implements various functions, such as windowing, subsampling, and different integration and readout modes. The ROIC reached an array scale of 32 × 32, a frame rate of 100 Hz, and a readout rate of 20 Mbps with an analog power consumption of less than 52 mW. The measurement results show that the input reference noise can be reduced to 143 e- via the CDS, and the fully customized scheme has certain advantages in the research of high-performance ROICs.

Review of multilevel inverter for high-power applications

PurposeTraditional level inverter technology has drawbacks in the aspect of Total harmonic distortion (THD) and switching losses for higher frequencies. Due to these drawbacks, two-level inverters have become unprofitable for high-power applications. Multilevel inverters (MLIs) are used to enhance the output waveform characteristics (i.e. low THD) and to offer various inverter topologies and switching methods.Design/methodology/approachMLIs are upgraded versions of two-level inverters that offer more output levels in current and voltage waveforms while lowering the dv/dt and di/dt ratios. This paper aims to review and compare the different topologies of MLI used in high-power applications. Single and multisource MLI's working principal and switching states for each topology are demonstrated and compared. A Simulink model system integrated using detailed circuit simulations in developed in MATLAB®–Simulink program. In this system, a constant voltage source connected to MLI to feed asynchronous motor with squirrel cage rotor type is used to demonstrate the efficacy of the MLI under different varying speed and torque conditions.FindingsMLI has presented better control and good range of system parameters than two-level inverter. It is suggested that the MLIs like cascade-five-level and NPC-five-level have shown low current harmonics of around 0.43% and 1.87%, respectively, compared to two-level inverter showing 5.82%.Originality/value This study is the first of its kind comparing the different topologies of single and multisource MLIs. This study suggests that the MLIs are more suitable for high-power applications.

A Single-Phase Seven-Level Triple Boost Inverter for Grid-Connected Transformerless PV Applications

A new seven-level common ground (CG) switched capacitor (SC)-based grid tied transformerless inverter has been introduced in this paper, which has three times boosting capability of input voltage. Thus, this inverter is preferable for low voltage PV applications without any boost conversion stage. To step-up the input PV voltage and facilitate seven steps in output voltage, two SC cells are connected in parallel. A virtual dc link capacitor serves key role for negative half cycle on behalf of H-Bridge and helping in constructing topology with common ground. The common grounding configuration of this inverter helps in bypassing the stray capacitance, which eliminates the leakage current and consequently reduces EMI considerably. Hence, further no requirement of any electro-magnetic interference (EMI) filter for electro-magnetic compatibility (EMC). Additionally, this inverter handles reactive power during the grid's lagging/leading power factor (pf) condition. To regulate active and reactive grid power, a proportional resonant (PR) current controller is designed. The proposed modulation scheme helps in balancing capacitor's voltages at their base values with allowable ripple. A detailed comparison also has been presented with other contemporary seven level inverters. The inverter circuit details, control technique, PWM scheme, thermal modeling, and loss analysis with component design guidelines are described in detail. Experimental results are presented to validate the effectiveness and feasibility of this proposed inverter.