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BackgroundNeurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input–output (IO) curve and the first-order approximation of the activated neural target.ObjectiveTo develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS.MethodFirst, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied.ResultsThe results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves’ lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S–D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper.ConclusionsSPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration.SignificanceThe proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

Funder: NIH U18EB029353, NSF GRFP, Funder: NIH U18EB029353, Funder: NIH R01DE021798, NSF GRFP, Funder: NIH R01DE021798, Implantable bioelectronic devices for the simulation of peripheral nerves could be used to treat disorders that are resistant to traditional pharmacological therapies. However, for many nerve targets, this requires invasive surgeries and the implantation of bulky devices (about a few centimetres in at least one dimension). Here we report the design and in vivo proof-of-concept testing of an endovascular wireless and battery-free millimetric implant for the stimulation of specific peripheral nerves that are difficult to reach via traditional surgeries. The device can be delivered through a percutaneous catheter and leverages magnetoelectric materials to receive data and power through tissue via a digitally programmable 1 mm × 0.8 mm system-on-a-chip. Implantation of the device directly on top of the sciatic nerve in rats and near a femoral artery in pigs (with a stimulation lead introduced into a blood vessel through a catheter) allowed for wireless stimulation of the animals' sciatic and femoral nerves. Minimally invasive magnetoelectric implants may allow for the stimulation of nerves without the need for open surgery or the implantation of battery-powered pulse generators.

Electric vehicle (EV) charging infrastructure will play a critical role in decarbonization during the next decades, energizing a large share of the transportation sector. This will further increase the enabling role of power electronics converters as an energy transition technology in the widespread adoption of clean energy sources and their efficient use. However, this deep transformation comes with challenges, some of which are already unfolding, such as the slow deployment of charging infrastructure and competing charging standards, and others that will have a long-term impact if not addressed timely, such as the reliability of power converters and power system stability due to loss of system inertia, just to name a few. Nevertheless, the inherent transition toward power systems with higher penetration of power electronics and batteries, together with a layer of communications and information technologies, will also bring opportunities for more flexible and intelligent grid integration and services, which could increase the share of renewable energy in the power grid. This work provides an overview of the existing charging infrastructure ecosystem, covering the different charging technologies for different EV classes, their structure, and configurations, including how they can impact the grid in the future.

Modular multilevel converters (MMCs) feature an established topology for the highest voltage and power levels. For lower levels, MMCs find a wide range of applications in generation, energy storage, and electric motor drive. However, the large quantity of expensive voltage sensors as well as high data-processing requirements for the module voltage balancing remains an obstacle. Additionally, the stark cost reductions of power electronic semiconductors have reduced their share in the total cost and rendered initially uneconomical module topologies appealing in low and medium voltages. In this article, we proposed an MMC topology with an active clamping path that balances the voltage of the modules in an arm. The proposed structure can achieve sensorless voltage balancing with a minimum control effort. The sensorless operation has the added benefit of reducing the communication as well as computation demand between the modules and the central controller. As compared with the state-of-the-art voltage balancing schemes, the proposed solution achieves a balanced system operation with less power loss, fewer components, and without any measurement. The presented analysis as well as the simulation and experimental results verify the benefits of the proposed topology. The results demonstrate that even in the face of a severe imbalance, all the module voltages are maintained within tight boundaries.

Modular multilevel converters (MMCs) enjoy the benefits of modularity and scalability. In particular, MMCs with symmetrical half-bridge submodules enable bipolar operation yet with a simple structure and low conduction losses. However, bulky dc capacitors in MMC submodules act as one major obstacle that retards the further improvement of system size, weight, and cost performance. Additionally, tight regulation and balance of dc capacitor voltages necessitate expensive voltage sensors paired with dedicated voltage controllers. This article proposes an effective hardware-based strategy that achieves MMC capacitance reduction through parallelization of symmetrical half-bridge submodules. On top of capacitance saving, the proposed strategy balances capacitor voltages in a sensorless fashion, which translates into the removal of voltage sensors and great simplification of control efforts. In addition, the proposed strategy allows fault-tolerant operation of MMCs. Finally, simulation and experimental results validate the effectiveness of the proposed strategy in capacitance reduction and capacitor voltage balancing of MMCs as static compensators.

Objective: This work aims to reduce the acoustic noise level of transcranial magnetic stimulation (TMS) coils. TMS requires high currents (several thousand amperes) to be pulsed through the coil, which generates a loud acoustic impulse whose peak sound pressure level (SPL) can exceed 130 dB(Z). This sound poses a risk to hearing and elicits unwanted neural activation of auditory brain circuits. Methods: We propose a new double-containment coil with enhanced winding mounting (DCC), which utilizes acoustic impedance mismatch to contain and dissipate the impulsive sound within an air-tight outer casing. The coil winding is potted in a rigid block, which is mounted to the outer casing by its acoustic nodes that are subject to minimum vibration during the pulse. The rest of the winding block is isolated from the casing by an air gap, and sound is absorbed by foam within the casing. The casing thickness under the winding center is minimized to maximize the coil electric field output. Results: Compared to commercial figure-of-eight TMS coils, the DCC prototype has 10-33 dB(Z) lower SPL at matched stimulation strength, whilst providing 22% higher maximum stimulation strength than equally focal commercial coils. Conclusion: The DCC design greatly reduces the acoustic noise of TMS while increasing the achievable stimulation strength. Significance: The acoustic noise reduction from our coil design is comparable to that provided by typical hearing protection devices. This coil design approach can enhance hearing safety and reduce auditory co-activations in the brain and other detrimental effects of TMS sound., 8 pages, 5 figures

Cascaded H-bridge and modular multilevel converters (MMC) are on the rise with emerging applications in renewable energy generation, energy storage, and electric motor drives. However, their well-known advantages come at the price of complicated balancing, high-bandwidth isolated monitoring, and numerous sensors that can prevent MMCs from expanding into highly cost-driven markets. Therefore, an obvious trend in research is developing control and topologies that depend less on measurements and benefit from simpler control. Diode-clamped topologies are considered among the more applicable solutions. The main problem with a diode-clamped topology is that it can only balance the module voltages of a string in one direction; therefore, it cannot provide a completely balanced operation. This article proposes an effective balancing technique for the diode-clamped topology. The proposed solution exploits the dc component of the arm current by introducing a symmetrically level-adjusted phase-shifted modulation scheme, and ensures the balancing current flow is always in the correct direction. The advantages of this method are sensorless operation, reduced computation, and control effort, lower communication requirement, removing the voltage sensors, and low overall cost. Analysis and detailed simulations provide insight into the operation of the new balancing technique and the experimental results confirm the provided discussions.

High-power silicon field-effect transistors as well as insulated gate bipolar transistors and particularly wide-bandgap semiconductor transistors typically need negative turn- off voltage for a secure off -state, resilience to spurious turn on , and rapid transition through the saturation mode. We present a gate driver configuration with compact and very efficient supply of the high and low sides of power transistor bridges with asymmetric bipolar control voltages without the need for costly and lossy isolated dc–dc converter and only low-voltage active components. The underlying negative voltage gate driver supply circuit consists of only a few cost-efficient components, its electrical potential is referenced to the source of the controlled semiconductor power switch, and its operation is synchronized with the gate driver output. It outcompetes the established use of isolated dc–dc converters in gate circuits with negative voltage needs with respect to cost, size, reliability, and efficiency. Importantly, the proposed negative voltage supply allows bootstrapping without the need for controllable high-voltage semiconductors for the sake of further cost reduction. We present detailed design rules of the circuit and experimentally validate circuit and control. In the automotive prototype implementation, size of the gate driver supply was reduced by 61%, cost by 57%, and loss by more than 16%.

Since the introduction of modular multilevel converters (MMC), various additional features have been introduced for MMCs, among them parallel inter-module connectivity. However, such a parallel mode requires appropriate modulation and switching strategies to exploit its full potential. Low-frequency switching modulation is widely used with half-bridge modules and a large body of research focusses on the optimum control and scheduling of these methods. However, so far, a suitable adaptation for MMCs with parallel mode is missing, and existing methods perform suboptimum. This article proposes a generic scheduling algorithm for simple and low-cost integration with low-frequency switching modulation. The proposed method reduces control complexity, minimizes power loss, and can be combined with most modulation techniques, including nearest-level modulation (NLM) and selective harmonic elimination. The text in this article provides a general algorithm for integrating this method with other control levels and analyzes its effect on MMCs with parallel functionality. Moreover, it examines the performance of the system with NLM combined with the proposed scheduler and compares it to other alternatives. Results show up to a 50% reduction in power loss with a similar modulation technique as well as significantly lower THD and power loss compared to optimized phase-shifted carrier modulation as another sensorless alternative method.

This article proposes an optimal discontinuous pulsewidth modulation (ODPWM) strategy to reduce the zero-sequence circulating current (ZSCC) for paralleled three-phase two-level converters. While existing methods generally entail a tradeoff between the ZSCC peak and the switching losses, this article fully explores the redundancy in the vector sequences to further improve both objectives. First, we derive eight candidate vector sequences for 60° sectors, each sequence with minimal switching times and ZSCC changing rate. Second, we divide each 60° sector into 12 subsectors, each is equipped with one of the eight candidates. The comparisons show that the proposed method maintains the same maximum ZSCC peak of existing best methods while using the minimal switching times, or effectively reducing the switching loss by up to 50%; the ZSCC of the proposed method is minimal across the major modulation regions, guaranteeing a smaller loss of the common mode inductor and further reducing the semiconductor losses. We implement the proposed optimal vector sequences in a carrier-based modulation scheme, the references of which depends on the subsector. Despite the complex geometry of the subsectors, the decision procedure is simple. Finally, the experimental results confirm the effectiveness of the proposed ODPWM.

In more-electronics power systems, grid-forming power converters, which operate as ac voltage sources, regulate the grid frequency and voltages in replacement of synchronous generators. Notably, grid impedances greatly influence the small signal and voltage stability of grid-forming converters. As such, prior knowledge of grid impedances can be very helpful for controller design. However, grid impedance estimation schemes are normally designed for current-controlled grid-following converters. Moreover, they are either very complicated or only yield grid inductances in a generally intrusive way. To fill this research gap, an impedance estimation method well suited to grid-forming converters is proposed. The method consists of four operating modes, which work well in voltage and power control cases. In the voltage control case, the voltage amplitude perturbation or phase angle information is exploited. Subsequently, the grid inductance and resistance are derived from power measurement. Alternatively, the active or reactive power information serves to estimate the grid impedance in the power control case. The proposed method features an easy implementation without any harmonic distortion, safety concern, or dependence on control parameters. Moreover, the method operates nonintrusively in most scenarios. Furthermore, a novel Kalman filtering scheme is proposed to provide added incentives. Finally, simulation and experimental results validate the effectiveness and simplicity of the proposed method.

H-bridge-based multilevel converters (e.g., cascaded H-bridge converters) benefit from modularity and scalability. However, they suffer from the complexity and costs associated with a large count of semiconductor switches, together with their drivers and peripheral circuits, as well as higher conduction losses as compared to half-bridge-based counterparts, as two switches in each submodule must simultaneously conduct to provide a current path. To reduce the number of active switch semiconductors and conduction losses, this article proposes novel multilevel converters with symmetrical half-bridge submodules. The symmetrical half-bridge submodule features a bipolar voltage output, a reduced switch count, and simplicity. Furthermore, this article proposes a sensorless voltage balance scheme that successfully gets rid of capacitor voltage mismatch problems through diodes and submodule parallelization. This scheme can greatly reduce capacitor voltage ripples, thereby allowing the saving of dc capacitances, particularly in the case of numerous submodules. Finally, simulation and experimental results validate the superiority of the proposed multilevel converters and voltage balance scheme.

Cascaded multilevel converters have been gaining popularity in static compensators (STATCOMs) owing to their excellent modularity, output quality, and fault tolerance. However, cascaded multilevel converters require a large number of galvanically isolated voltage sensors and complex communication systems in order to balance the module dc-link voltages. This article proposes a reduced series/parallel module (RSPM) that intends to replace H-bridges in cascaded multilevel STATCOMs. Any adjacent RSPMs can be configured in series, bypass, or parallel. Cycling the parallel state through all modules balances all capacitor voltages in a sensorless manner. Since the RSPM-balancing process occurs at the hardware level, H-bridges can be easily replaced by RSPMs without changing higher-level control loops. Compared to existing series/parallel modules, the RSPM has the same functionalities but uses 25% fewer transistors. Despite the reduced switch count, the RSPM is shown to achieve even better efficiency under certain load conditions. The RSPM also attains similar semiconductor utilization ratio as H-bridge modules. The features of the RSPMs are verified on simulations and a down-scaled experimental setup.

This article aims to obtain the optimal combination of the switching loss, maximum zero-sequence circulating current (ZSCC), and line current ripple, which benefits most of the applications. The analysis shows that the interleaved discontinues pulsewidth modulation (IDPWM) maintains the minimum switching loss and line current ripples, but it introduces a larger ZSCC. Therefore, to obtain the optimal combination of the switching loss, maximum ZSCC peak, and line current ripple, it is essential to retain the minimum switching times and the optimal line current ripple of IDPWM but further reducing its overly large ZSCC. Further analysis reveals that IDPWM includes the vector combinations with medium ZSCC change rates, resulting in the larger ZSCC. Given the redundancies of the vector combinations, this article proposes a simple matrix to modify the original modulation signals of IDPWM, eliminating the medium ZSCC change rates. The proposed PWM scheme retains the minimum line current ripple and switching times as those of IDPWM while further reducing its overly large ZSCC, as validated by analytical results as well as experimental results. Since the proposed method obtains the optimal combination among switching loss, line current ripple, and maximum ZSCC peak, we name the proposed PWM scheme a high-performance DPWM.

Split-battery converters based on cascaded H-bridges (CHBs) are gaining popularity due to their excellent physical modularity. During operation, however, the batteries experience substantial current ripple. Conventional ripple-current reduction methods rely on bulky passive components or complicated control. This article presents modulation and common-mode voltage injection methods for cascaded double-H-bridge converters (CHB2). The control methods directly mitigate the source of the ripple current—the fluctuating arm power—by exploiting the parallel interconnection across the CHB2 arms. In the lab setup, the proposed solution approximately halves the battery current ripple compared to the CHB counterpart. Finally, this article studies component sizing and limitations of the proposed solution.

This article proposes a line-current ripple minimization pulsewidth modulation strategy with reduced zero-sequence circulating current (ZSCC) for two parallel three-phase two-level converters. We split each 60° sector into six subsectors, each applies the nearest three vectors to ensure minimal line-current ripple. To reduce the ZSCC, we further investigate all vector sequences of the nearest three vectors and derive an optimal vector sequence for each of the six subsectors. We unify all carrier sequences under a carrier-based modulation scheme with the assistance of voltage injections. The injected voltage can be computed by a simple algorithm that allows easy implementation in mainstream microcontrollers. The experimental results validate that the proposed method maintains the minimal line-current ripple while significantly reducing the ZSCC compared to the existing line current ripple minimization methods.

This paper proposes a hybrid space vector modulation (HBSVM) to reduce line current ripple while maintaining the optimal common mode voltage and zero sequence circulating current peak. The analysis reveals that the vector errors can be reduced by an equivalent basic vector with an adjustable length, which is synthesised by two basic vectors of the same vector angle. Then, a variable k is defined to denote the distribution of the two basic vectors. In addition, due to the implementation of k , a special vectors timing sequence is proposed. With the proposed generalised sequence, this paper investigates the current ripple over the switching period. Furthermore, we derive a map of the optimal distribution factor k opt to minimise the current ripple. Each 30° sector is divided into three subsectors; each applies k opt to minimise the current ripple. With the proposed two variables ( p and q ), a simple flowchart for k opt selection in the whole space vector plane is developed. The proposed HBSVM uses the optimal distribution k opt according to the position of the reference voltage, achieving the minimised line current ripple. The proposed HBSVM achieves the best line current quality while maintaining the optimal ZSCC and CMV peak, as validated by experiments.

BackgroundAccurate data on the sound emitted by transcranial magnetic stimulation (TMS) coils is lacking.MethodsWe recorded the sound waveforms of seven coils with high bandwidth. We estimated the neural stimulation strength by measuring the induced electric field and applying a strength–duration model to account for different waveforms.ResultsAcross coils, at maximum stimulator output and 25 cm distance, the sound pressure level (SPL) was 98–125 dB(Z) per pulse and 75–97 dB(A) for a 15 Hz pulse train. At 5 cm distance, these values were estimated to increase to 112–139 dB(Z) and 89–111 dB(A), respectively.ConclusionsThe coils’ sound was below, but near, relevant exposure limits for operators and may exceed some limits for the subject. Exposure standards may inadequately capture some risks to hearing. For persons near operating TMS coils we recommend hearing protection, and we consider it essential for the TMS subject.HighlightsCoil click varies by over 20 dB(Z) between TMS coils at matched stimulation strength.Close to TMS coil, sound pressure level may reach nearly 140 dB(Z).For rTMS, the continuous sound level can exceed 110 dB(A).Hearing protection is recommended during TMS, especially for the subject.

BackgroundMotor-evoked potentials (MEP) are one of the most prominent responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. Understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smaller responses, e.g., from single motor units. However, available detection and quantization methods suffer from a large noise floor.ObjectiveThis paper develops a detection method that extracts MEPs hidden below the noise floor. With this method, we aim to estimate excitatory activations of the corticospinal pathways well below the conventional detection level.MethodsThe presented MEP detection method presents a self-learning matched-filter approach for improved robustness against noise. The filter is adaptively generated per subject through iterative learning. For responses that are reliably detected by conventional detection, the new approach is fully compatible with established peak-to-peak readings and provides the same results but extends the dynamic range below the conventional noise floor.ResultsIn contrast to the conventional peak-to-peak measure, the proposed method increases the signal-to-noise ratio by more than a factor of 5. The first detectable responses appear to be substantially lower than the conventional threshold definition of 50 μV median peak-to-peak amplitude.ConclusionThe proposed method shows that stimuli well below the conventional 50 μV threshold definition can consistently and repeatably evoke muscular responses and thus activate excitable neuron populations in the brain. As a consequence, the IO curve is extended at the lower end, and the noise cut-off is shifted. Importantly, the IO curve extends so far that the 50 μV point turns out to be closer to the center of the logarithmic sigmoid curve rather than close to the first detectable responses. The underlying method is applicable to a wide range of evoked potentials and other biosignals, such as in electroencephalography.

This paper proposes a motor drive suitable for electric vehicles (EVs) with notably reduced losses as compared to conventional drives. The proposed drive feeds the dc-link with a dynamically reconfigurable battery formed by cascaded half-bridges containing battery units. The reconfigurable battery concurrently forms a modular multilevel dc-link able to synthesize variable dc-link voltages, which can adjust the dc link voltage to run a motor to operate at the optimum load point or even to contribute to modulation and greatly reduce the switching loss of the main inverter—in this case a standard two-level three-phase inverter. Up to 2/3 switching actions are avoided in the main inverter, which can run at maximum duty cycle and fundamental-frequency commutation. The saved switching effort in the main inverter is shifted to the modular multilevel dc link, but with much less loss due to the fractionized switching voltage and the use of field-effect transistors (FETs). At high drive speeds, the converter halves the total loss compared to a standard inverter only; at lower speeds and thus smaller modulation indices, the advantage is even more pronounced because of the dynamically lowered dc-link voltage. Other benefits include alleviated insulation stress for motor windings and direct battery balancing. The proposed motor drive is verified on a down-scaled setup with eight modules constituting the dc link.

ObjectiveThis article presents a novel transcranial magnetic stimulation (TMS) pulse generator with a wide range of pulse shape, amplitude, and width.ApproachBased on a modular multilevel TMS (MM-TMS) topology we had proposed previously, we realized the first such device operating at full TMS energy levels. It consists of ten cascaded H-bridge modules, each implemented with insulated-gate bipolar transistors, enabling both novel high-amplitude ultrabrief pulses as well as pulses with conventional amplitude and duration. The MM-TMS device can output pulses including up to 21 voltage levels with a step size of up to 1100 V, allowing relatively flexible generation of various pulse waveforms and sequences. The circuit further allows charging the energy storage capacitor on each of the ten cascaded modules with a conventional TMS power supply.Main resultsThe MM-TMS device can output peak coil voltages and currents of 11 kV and 10 kA, respectively, enabling suprathreshold ultrabrief pulses (> 8.25 μs active electric field phase). Further, the MM-TMS device can generate a wide range of near-rectangular monophasic and biphasic pulses, as well as more complex staircase-approximated sinusoidal, polyphasic, and amplitude-modulated pulses. At matched estimated stimulation strength, briefer pulses emit less sound, which could enable quieter TMS. Finally, the MM-TMS device can instantaneously increase or decrease the amplitude from one pulse to the next in discrete steps by adding or removing modules in series, which enables rapid pulse sequences and paired-pulse protocols with variable pulse shapes and amplitudes.SignificanceThe MM-TMS device allows unprecedented control of the pulse characteristics which could enable novel protocols and quieter pulses.

When providing ac output, modular multilevel converters (MMCs) experience power fluctuation in the phase arms. The power fluctuation causes voltage ripple on the module capacitors, which grows with the output power and inversely to the output frequency. Thus, low-frequency operations of MMCs, e.g., for motor drives, require injecting common-mode voltages and circulating currents, and strict dc voltage output relative to ground is impossible. To address this problem, this paper introduces a novel module topology that allows parallel module connectivity in addition to the series and bypass states. The parallel state directly transfers power across the modules and arms to cancel the power fluctuations and hence suppresses the capacitor voltage ripple. The proposed series/parallel converter can operate at a wide frequency range down to dc without common-mode voltages or circulating currents; it also allows sensorless operation and full utilization of the components at higher output frequencies. We present detailed simulation and experiment results to characterize the advantages and limitations of the proposed solution.

This paper presents proof-of-concept of a novel photovoltaic (PV) inverter with integrated short-term storage, based on the modular cascaded double H-bridge (CHB2) topology, and a new look-up table control approach. This topology combines and extends the advantages of various distributed converter concepts, such as string inverters, microinverters, and cascaded H-bridge (CHB) multilevel inverters. The proposed CHB2 inverter incorporates individual PV elements into modules that can dynamically connect to their neighbors not only in series but also in parallel, which reduces conduction losses and enables simple module balancing. Maximum-power-point tracking of the PV element is performed in each module. As a demonstration of the flexibility of the approach and to filter large power fluctuations before they enter the grid, each module further incorporates batteries for energy storage, which enables continuous power output by compensating solar power fluctuations, while the CHB2 can balance the batteries’ load and state of charge. The inverter provides exceptionally high output quality without large filters, since the multilevel ac output is finely quantized. The elimination of extensive output filtering provides for an extremely rapid dynamic response as well. We furthermore introduce an optimized, computationally efficient, look-up-table-based controller and module scheduler which leverages the large number of degrees of freedom provided by the flexible series-parallel CHB2 configurations to optimize conduction loss, switching loss, and load balance. The method can be extended to CHB2 circuits in general to improve their performance while simplifying control. Finally, we demonstrate that the inverter is robust to open-circuit failure of power switches or PVs.

Modules with series and parallel connectivity add new features and operation modes to modular multilevel converters (MMCs). Compared to full- and half-bridges, the series/parallel modules allow sensorless module balancing and reduce conduction loss with the same semiconductor area. However, in high-voltage applications with limited switching rates, the sensorless operation of the series/parallel modules suffers from large charge-balancing currents. This paper introduces a series/parallel module variant with a small port inductor. The port inductor suppresses the charge-balancing current despite low switching rates. We also propose a carrier-based modulation framework and show the importance of the carrier assignment in terms of efficiency and balancing. The proposed module and the modulation method are verified on a lab setup with module switching rates down to 200 Hz. The module voltages are kept within a narrow band with the charge-balancing currents below 5% of the arm current. The experimental results show practicality and advantages of the new series/parallel modules in high-voltage MMC applications.

Motor-evoked potentials (MEPs) are widely used for biomarkers and dose individualization in transcranial stimulation. The large variability of MEPs requires sophisticated methods of analysis to extract information fast and correctly. Development and testing of such methods relies on the availability for realistic models of MEP generation, which are presently lacking. This paper presents a statistical model that can simulate long sequences of individualized MEP amplitude data with properties matching experimental observations. The MEP model includes three sources of trial-to-trial variability: excitability fluctuations, variability in the neural and muscular pathways, and physiological and measurement noise. It also generates virtual human subject data from statistics of population variability. All parameters are extracted as statistical distributions from experimental data from the literature. The model exhibits previously described features, such as stimulus-intensity-dependent MEP amplitude distributions, including bimodal ones. The model can generate long sequences of test data for individual subjects with specified parameters or for subjects from a virtual population. The presented MEP model is the most detailed to date and can be used for the development and implementation of dosing and biomarker estimation algorithms for transcranial stimulation.

Cascaded H-bridge (CHB) converters are receiving growing attention in battery energy storage systems (BESS) due to their modularity and flexibility. However, direct generation of ac output in CHB-BESSs incurs large second-order current ripple in the batteries, which causes additional loss and might accelerate battery aging. Existing methods for ripple-current suppression usually require bulky passive components due to the high energy content of the ripple components. This paper presents a class of current injection methods for delta-configured CHB-BESSs. The injected currents flow through the CHB arms and transfer the original second-order oscillating power to the fourth or the sixth order, or even to an arbitrarily high-order frequency. As such, the battery current ripple appears at much higher frequencies with lower oscillating energy and can be easily filtered by small passive components. In the laboratory setup, the proposed methods reduced the battery root-mean-square current ripple to less than 10% of the dc component with negligible distortion in the loads. The proposed methods and the filter implementations show good tolerance to load frequencies and to the control error of the injected currents.

This paper presents a modular multilevel series/parallel converter (MMSPC) with intermodule switched-inductor power transfer. The switched-inductor voltage conversion feature allows controllable and efficient transfer of energy between modules with nonnegligible voltage difference, providing both step-down and step-up functionalities. Thus, this converter can accurately control and rapidly adjust the voltage of each module to generate an ac output voltage waveform with a controllable number of levels, increasing the quality of the output. Moreover, the intrinsic dc–dc conversion feature can generate a dc controllable output voltage and enable new applications. In this text, we specifically demonstrate how the flexibility of obtaining both ac and dc output with the same setup renders the topology promising for battery energy storage systems and dc microgrid applications. Experimental results validate the topology and concept of an MMSPC with intrinsic switched-inductor conversion.

ObjectiveWe present a power electronic system and magnetic nanoparticles for multiplexed magnetogenetic neurostimulation with three channels spanning a wide frequency range and rapid channel switching capability. This enables selective heating of magnetic nanoparticles with different coercivity using various frequency–amplitude combinations of the magnetic field. Such multiplexed operation could provide the technical means for selective magnetogenetic neurostimulation beyond its spatial focality limits.ApproachThe electronic system uses a hybrid of silicon metal–oxide–semiconductor and gallium-nitride field-effect transistors, which generate the required high-amplitude current up to 500 A in the sub-MHz range and the high-frequency current in the MHz range, respectively. Via three discrete resonance capacitor banks, the system generates an alternating magnetic field in the same liquid-cooled field coil at three distinct frequency channels spanning 50 kHz to 4 MHz. Fast switching between channels is achieved with high-voltage contactors connecting the coil to different capacitor banks. We characterized the system by the output channels’ frequencies, field strength, and switching time, as well as the system’s overall operation stability. Three types of iron oxide nanoparticles with different coercivity are developed to form three magnetothermal channels. Specific absorption rate and infrared thermal imaging measurements are performed with the nanoparticles to characterize their heating and demonstrate selective actuation for all three channels.Main resultsThe system achieved the desired target field strengths for three frequency channels (70 kA/m at 50 kHz, 10 kA/m at 500 kHz, and 1 kA/m at ≥2 MHz), with rapid switching speed between channels on the order of milliseconds. The system can operate continuously for at least two hours at 30% duty cycle with 125 Arms load in the coil, corresponding to a stimulation protocol of cycling the three channels at target strength with 3 s pulses and 7 s interpulse intervals. The nanoparticles were heated with selectivity between 2.3× and 9× for their respective frequency channel. The system’s intended use was thus validated with three distinct channels available for magnetothermal heating.SignificanceWe describe the first combination of a power electronic system and magnetic nanoparticles that achieves three stimulation channels. Selective actuation of nanoparticles is demonstrated for each channel using the same field coil, including a novel composition responding to magnetic fields in the MHz range. This approach could improve the speed and flexibility of frequency-multiplexed magnetogenetic neural stimulation.

Precisely timed activation of genetically targeted cells is a powerful tool for studying neural circuits and controlling cell-based therapies. Magnetic control of cell activity or “magnetogenetics” using magnetic nanoparticle heating of temperature-sensitive ion channels enables remote, non-invasive activation of neurons for deep-tissue applications and studies of freely behaving animals. However, the in vivo response time of thermal magnetogenetics is currently tens of seconds, which prevents the precise temporal modulation of neural activity similar to light-based optogenetics. Moreover, magnetogenetics has not provided a means to selectively activate multiple channels to drive behavior. Here we demonstrate that by combining magnetic nanoparticles with a rate-sensitive thermoreceptor (TRPA1-A) it is possible to achieve sub-second behavioral responses in Drosophila melanogaster. Furthermore, by tuning the properties of magnetic nanoparticles to respond to different magnetic field strengths and frequencies, we can achieve fast, multi-channel stimulation, analogous to optogenetic stimulation with different wavelengths of light. These results bring magnetogenetics closer to the temporal resolution and multiplexed stimulation possible with optogenetics while maintaining the minimal invasiveness and deep-tissue stimulation only possible by magnetic control.

Precisely timed activation of genetically targeted cells is a powerful tool for studying neural circuits and controlling cell-based therapies. Magnetic control of cell activity or “magnetogenetics” using magnetic nanoparticle heating of temperature-sensitive ion channels enables remote, non-invasive activation of neurons for deep-tissue applications and studies of freely behaving animals. However, the in vivo response time of thermal magnetogenetics is currently tens of seconds, which prevents the precise temporal modulation of neural activity similar to light-based optogenetics. Moreover, magnetogenetics has not provided a means to selectively activate multiple channels to drive behavior. Here we produce sub-second behavioral responses in Drosophila melanogaster by combining magnetic nanoparticles with a rate-sensitive thermoreceptor (TRPA1-A). Furthermore, by tuning the properties of magnetic nanoparticles to respond to different magnetic field strengths and frequencies, we can achieve sub-second, multichannel stimulation, analogous to multi-color optogenetic stimulation. These results bring magnetogenetics closer to the temporal resolution and multiplexed stimulation possible with optogenetics while maintaining the minimal invasiveness and deep-tissue stimulation only possible by magnetic control.

Funder: Technische Universität Dresden (1019), In value-based decision making, people have to weigh different options based on their subjective value. This process, however, also is influenced by choice biases, such as choice repetition: in a series of choices, people are more likely to repeat their decision than to switch to a different choice. Previously, it was shown that transcranial direct current stimulation (tDCS) can affect such choice biases. We applied tDCS over the medial prefrontal cortex to investigate whether tDCS can alter choice repetition in value-based decision making. In a preregistered study, we applied anodal, cathodal, and sham tDCS stimulation to 52 participants. While we found robust choice repetition effects, we did not find support for an effect of tDCS stimulation. We discuss these findings within the larger scope of the tDCS literature and highlight the potential roles of interindividual variability and current density strength.

This paper discusses some of the practical limitations and issues, which exist for the input–output (IO) slope curve estimation (SCE) in neural, brain and spinal, stimulation techniques. The drawbacks of the SCE techniques by using existing uniform sampling and Fisher-information-based optimal IO curve estimation (FO-IOCE) methods are elaborated. A novel IO SCE technique is proposed with a modified sampling strategy and stopping rule which improve the SCE performance compared to these methods. The effectiveness of the proposed IO SCE is tested on 1000 simulation runs in transcranial magnetic stimulation (TMS), with a realistic model of motor evoked potentials (MEPs). The results show that the proposed IO SCE method successfully satisfies the stopping rule, before reaching the maximum number of TMS pulses in 79.5% of runs, while the estimation based on the uniform sampling technique never converges and satisfies the stopping rule. At the time of successful termination, the proposed IO SCE method decreases the 95th percentile (mean value in the parentheses) of the absolute relative estimation errors (AREs) of the slope curve parameters up to 7.45% (2.2%), with only 18 additional pulses on average compared to that of the FO-IOCE technique. It also decreases the 95th percentile (mean value in the parentheses) of the AREs of the IO slope curve parameters up to 59.33% (16.71%), compared to that of the uniform sampling method. The proposed IO SCE also identifies the peak slope with higher accuracy, with the 95th percentile (mean value in the parentheses) of AREs reduced by up to 9.96% (2.01%) compared to that of the FO-IOCE method, and by up to 46.29% (13.13%) compared to that of the uniform sampling method.

This paper presents a multiobjective real-time controller for a modular multilevel converter capable of parallel module connectivity, the so-called modular multilevel series parallel converter (MMSPC). The MMSPC topology allows the batteries to be dynamically rewired in various series–parallel configurations, generating a wide range of output voltage levels. The novel control method parallelizes the modules to balance their voltages without the need for individual module voltage monitoring. Additionally, the controller optimizes across the large number of feasible system configurations to minimize switching and conduction losses. Finally, the controller efficiently encodes the system configuration with module interconnection states rather than the module switch states, which substantially simplifies control. Furthermore, this work experimentally validates the MMSPC topology and concept. In the prototype, the parallel mode reduced the system losses at 5 kW output power by 18% and 24% for load power factors of 1.0 and 0.8, respectively. Sensorless balancing via parallelization maintained well-matched module voltages (standard deviation = 0.045 V) over a 5-h battery discharge with highly variable load current. The reduced conduction losses and simple balancing capability of the MMSPC can enable new applications at medium and low voltages that benefit from its high-quality output, elimination of filtering magnetics, fast response, and modularity.

Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.

The large-scale deployment of grid-tied power electronic converters in renewable generation, electric motor drives, and energy storage systems speeds up the global energy transition. However, the stability problems faced by power converters in weak grids, which feature large and variable grid impedances, stand as a serious challenge that has puzzled the power electronic community for more than 15 years. Existing solutions to weak-grid-induced small-signal stability problems modify system and/or control parameters so that stable operation is achieved under a certain condition. However, such solutions are problem and application-specific. Therefore, the solutions can hardly be generalized to various operating conditions or stability problems. As such, this article proposes an estimation-based solution that targets multiple weak-grid-induced small-signal stability problems, particularly for converters with grid-supportive services. The essence of the proposed solution lies in the accurate estimation of real grid information rather than the point-of-common-coupling information. For implementation, Kalman filters serve as an estimator. Subsequently, the estimated information is used to design controllers, such as voltage droop, that allow for stable power conversion. Finally, simulation and experimental results validate the effectiveness and generality of the proposed solution. This work was supported in part by the National Science Foundation under Grant 1608929 and in part by the Duke Energy Research Seed Fund.

ObjectiveRobotic positioning systems for transcranial magnetic stimulation (TMS) promise improved accuracy and stability of coil placement, but there is limited data on their performance. This text investigates the usability, accuracy, and limitations of robotic coil placement with a commercial system, ANT Neuro, in a TMS study.Approach21 subjects underwent a total of 79 TMS sessions corresponding to 160 hours under robotic coil control. Coil position and orientation were monitored concurrently through an additional neuronavigation system.Main ResultsRobot setup took on average 14.5 min. The robot achieved low position and orientation error with median 1.34 mm and 3.48°. The error increased over time at a rate of 0.4%/minute for both position and orientation.SignificanceAfter the elimination of several limitations, robotic TMS systems promise to substantially improve the accuracy and stability of manual coil position and orientation. Lack of pressure feedback and of manual adjustment of all coil degrees of freedom were limitations of this robotic system.