Exponential Modular Multilevel Converter for Low Voltage Applications

This paper presents the structure and control of a single phase Exponential Modular Multilevel Converter (EMMC), which works as a bidirectional AC/DC converter. In addition to the main H-bridge converter, it uses series connected H-bridges with DC link capacitors. The nominal voltage rating of the capacitors is increased with each module by factor of two. In this manner, the number of output voltage levels exponentially increases with the number of series connected H-bridges. By using low-voltage MOSFETs it is possible to achieve a very high efficiency, especially at partial loading. The high number of voltage levels reduces the output voltage THD, while using a low switching frequency. Thus, the required grid filter size can be substantially reduced. Furthermore, the additional capacitor modules increase the nominal output voltage at the AC side, so that the flow of the active and reactive power can be dynamically adjusted. Therefore, the EMMC could be used, for instance, as a vehicle charger directly connected to the grid

Overview of Battery Impedance Modeling Including Detailed State-of-the-Art Cylindrical 18650 Lithium-Ion Battery Cell Comparisons

Electrical models of battery cells are used in simulations to represent batteries\u27 behavior in various fields of research and development involving battery cells and systems. Electrical equivalent circuit models, either linear or nonlinear, are commonly used for this purpose and are presented in this article. Various commercially available cylindrical, state-of-the-art lithium-ion battery cells, both protected and unprotected, are considered. Their impedance properties, according to four different equivalent circuit models, are measured using electrochemical impedance spectroscopies. Furthermore, the pricing, impedance, specific energy, and C-rate of the chosen battery cells are compared. For example, it is shown that the energy density of modern 18650 cells can vary from a typical value of 200 to about 260 Wh kg(-1), whereas the cell price can deviate by a factor of about 3 to 5. Therefore, as a result, this study presents a concise but comprehensive battery parameter library that should aid battery system designers or power electronic engineers in conducting battery simulations and in selecting appropriate battery cells based on application-specific requirements. In addition, the accuracies and computational efforts of the four equivalent circuit models are compared

Online and On-Board Battery Impedance Estimation of Battery Cells, Modules or Packs in a Reconfigurable Battery System or Multilevel Inverter

This paper shows two approaches to determine the battery impedance of battery cells or battery modules when used in a reconfigurable battery system (RBS) or in any type of modular multilevel converter (MMC) for electric drive applications. A generic battery model is used and the concepts of the recursive time and frequency-domain parameter extraction, using a current step and an electrochemical impedance spectroscopy, are explained. Thus, it is shown and demonstrated that the balancing current of neighboring cells/modules ,when in parallel operation, can be used, similar to the time-domain parameter extraction utilizing a current step, to determine the battery parameters. Furthermore, it is shown and demonstrated that a part of the inverter can be used as variable AC voltage source to control a sinusoidal current through the motor inductances of the drive train, which can be injected to the inserted battery cells/modules of an adjacent phase to perform an on-board impedance spectroscopy. Using either of the two presented approaches, the individual battery impedances can be easily determined, yielding the state of health (SOH) and the power capability of individual battery cells/modules. Nonetheless, the analyzed approaches were just considered to be applied at machine standstill, which is not suitable for grid-tied applications

Sensorless Capacitor Voltage Balancing of a Grid-Tied, Single-Phase Hybrid Multilevel Converter with Asymmetric Capacitor Voltages using Dynamic Programming

This paper shows a sensorless capacitor voltage balancing control approach for a grid-connected, single-phase hybrid multilevel inverter based on an NPC main stage with a voltage stiff DC-link and an arbitrary number of H-Bridge modules (capacitor modules) with asymmetric capacitor voltages. Using nearest-level control, a model predictive control (MPC) approach with a prediction horizon of one time step is chosen to find an optimal switching-state combination among the redundant switching combinations to balance the capacitor voltages as quick as possible. Using the Lyapunov stability criterion, it is shown that an offline calculated optimal switching-state sequence for each discrete output voltage level can be used to operate the inverter without using any voltage sensors for the capacitor voltages. To validate the stability of the approach, a laboratory inverter with a resistive load is operated with the offline calculated optimal switching-state sequences and it is shown that the capacitor voltages converge to their desired reference voltages

Recovery-Stress Response of Blood-Based Biomarkers

The purpose of this study was to investigate blood-based biomarkers and their regulation with regard to different recovery-stress states. A total of 35 male elite athletes (13 badminton, 22 soccer players) were recruited, and two venous blood samples were taken: one in a ‘recovered’ state (REC) after a minimum of one-day rest from exercise and another one in a ‘non-recovered’ state (NOR) after a habitual loading microcycle. Overall, 23 blood-based biomarkers of different physiologic domains, which address inflammation, muscle damage, and tissue repair, were analyzed by Luminex assays. Across all athletes, only creatine kinase (CK), interleukin (IL-) 6, and IL-17A showed higher concentrations at NOR compared to REC time points. In badminton players, higher levels of CK and IL-17A at NOR were found. In contrast, a higher value for S100 calcium-binding protein A8 (S100A8) at REC was found in badminton players. Similar differences were found for BDNF in soccer players. Soccer players also showed increased levels of CK, and IL-6 at NOR compared to REC state. Several molecular markers were shown to be responsive to differing recoverystress states, but their suitability as biomarkers in training must be further validated

12-week combined strength and endurance exercise attenuates CD8+ T-cell differentiation and affects the kynurenine pathway in the elderly: a randomized controlled trial

Background: Age-related accumulation of highly differentiated CD8+ effector memory re-expressing CD45RA (EMRA) T-cells and disruption of the kynurenine (KYN) pathway are associated with chronic inflammation and the development of insulin resistance. In this study the aim was to investigate the effects of 12-week combined strength and endurance exercise on CD8+ T-cell differentiation and KYN pathway metabolites. Ninety-six elderly subjects (f/m, aged 50—70) were randomized to a control (CON) or exercise (EX) group. The EX group completed combined strength and endurance training twice weekly for one hour each time at an intensity of 60% of the one-repetition maximum for strength exercises and a perceived exertion of 15/20 for endurance exercises. The EX group was also randomly subdivided into two groups with or without a concomitant balanced diet intervention in order to examine additional effects besides exercise alone. Before and after the intervention phase, the proportions of CD8+ T-cell subsets and levels of KYN pathway metabolites in peripheral blood were determined. Results: The CD8+ EMRA T-cell subsets increased in the CON group but remained almost unchanged in the EX group (p =.02). Plasma levels of kynurenic acid (KA) increased in the EX group and decreased in the CON group (p =.03). Concomitant nutritional intervention resulted in lower levels of quinolinic acid (QA) compared with exercise alone (p =.03). Overall, there was a slight increase in the QA/KA ratio in the CON group, whereas it decreased in the EX group (p >.05). Conclusions: Combined strength and endurance training seems to be a suitable approach to attenuate CD8+ T-cell differentiation in the elderly and to redirect the KYN pathway towards KA. The clinical relevance of these effects needs further investigation

Capacitor Voltage Balancing of a Grid-Tied, Cascaded Multilevel Converter with Binary Asymmetric Voltage Levels Using an Optimal One-Step-Ahead Switching-State Combination Approach†

This paper presents a novel capacitor voltage balancing control approach for cascaded multilevel inverters with an arbitrary number of series-connected H-Bridge modules (floating capacitor modules) with asymmetric voltages, tiered by a factor of two (binary asymmetric). Using a nearest-level reference waveform, the balancing approach uses a one-step-ahead approach to find the optimal switching-state combination among all redundant switching-state combinations to balance the capacitor voltages as quickly as possible. Moreover, using a Lyapunov function candidate and considering LaSalle\u27s invariance principle, it is shown that an offline calculated trajectory of optimal switching-state combinations for each discrete output voltage level can be used to operate (asymptotically stable) the inverter without measuring any of the capacitor voltages, achieving a novel sensorless control as well. To verify the stability of the one-step-ahead balancing approach and its sensorless variant, a demonstrator inverter with 33 levels is operated in grid-tied mode. For the chosen 33-level converter, the NPC main-stage and the individual H-bridge modules are operated with an individual switching frequency of about 1 kHz and 2 kHz, respectively. The sensorless approach slightly reduced the dynamic system response and, furthermore, the current THD for the chosen operating point was increased from 3.28% to 4.58% in comparison with that of using the capacitor voltage feedback

Design and testing of a novel transcranial magnetic stimulator with adjustable pulse dynamics and high current capability (>2 ka) based on a modular cascaded h-bridge inverter topology

Transcranial magnetic stimulation (TMS) is an important technology in neurological diagnostics and therapy. The limited output voltage shape of modern TM stimulators constrains the research about the targeted stimulation of individual brain parts. This paper introduces a novel TM stimulator based on a cascaded H-bridge inverter topology. Using a large number of sub-modules (e.g., ten), a nearly arbitrary output voltage waveform can be generated. Within the frame of this paper, the design and testing of an individual H-bridge module, using low-voltage MOSFETs, is explained in detail. To achieve a high current capability, the switching waveforms of the paralleled MOSFETs are synchronized by individual time delays introduced by an integrated CPLD. The H-bridge module is used for different experimental pulse tests. Using a DC link voltage of 180 V a maximum current peak of about 2.1 kA is achieved

Data Augmentation and Feature Selection for the Prediction of the State of Charge of Lithium-Ion Batteries Using Artificial Neural Networks

Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data