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Keywords = charge-domain filter

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31 pages, 9207 KB  
Article
A Model Framework for Ion Channels with Selectivity Filters Based on Non-Equilibrium Thermodynamics
by Christine Keller, Manuel Landstorfer, Jürgen Fuhrmann and Barbara Wagner
Entropy 2025, 27(9), 981; https://doi.org/10.3390/e27090981 - 20 Sep 2025
Viewed by 340
Abstract
A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special [...] Read more.
A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special emphasis is placed on the consistent modeling of the selectivity filter within the pore. It is treated as an embedded domain in which the constituents can change their chemical properties and mobility. Using this framework, we achieve good agreement with an experimentally observed current–voltage (IV) characteristic for an L-type selective calcium ion channel for a range of ion concentrations. In particular, we show that the model captures the experimentally observed anomalous mole fraction effect (AMFE). As a result, we find that calcium and sodium currents depend on the surface charge in the selectivity filter, the mobility of ions and the available space in the channel. Our results show that negative charges within the pore have a decisive influence on the selectivity of divalent over monovalent ions, supporting the view that AMFE can emerge from competition and binding effects in a multi-ion environment. Furthermore, the flexibility of the model allows its application in a wide range of channel types and environmental conditions, including both biological ion channels and synthetic nanopores, such as engineered membrane systems with selective ion transport. Full article
(This article belongs to the Special Issue Mathematical Modeling for Ion Channels)
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15 pages, 3176 KB  
Article
SoC Fusion Estimation Based on Neural Network Long and Short Time Series
by Bosong Zou, Wang Fu, Chunxia Yan, Qingshuang Zeng, Zheng Wang, Rong Wang, Wenlong Ding, Xianglong Chen and Qiuju Gao
Batteries 2025, 11(9), 336; https://doi.org/10.3390/batteries11090336 - 9 Sep 2025
Viewed by 713
Abstract
Accurate prediction of state-of-charge (SoC) is critical to ensure battery performance, extend lifetime and ensure safety. Data-driven methods for SoC prediction are highly adaptable and generalizable. However, the current method of estimating SoC using a single model suffers from the difficulty of accommodating [...] Read more.
Accurate prediction of state-of-charge (SoC) is critical to ensure battery performance, extend lifetime and ensure safety. Data-driven methods for SoC prediction are highly adaptable and generalizable. However, the current method of estimating SoC using a single model suffers from the difficulty of accommodating both global variations in the long time domain and local variations in the short time domain, which in turn leads to limited accuracy. Therefore, this paper proposes a dual-model fusion of Transformer and long short-term memory (LSTM) network for SoC estimation. Transformer and LSTM are used to capture the global change features of the battery in the long time domain and the local change features in the short time domain, respectively. First, we employ a single model to obtain separate SoC estimations for the long-term and short-term domains. Then, we fuse these long-term and short-term estimations using a neural network. Finally, we apply Kalman filtering to process the fused data and obtain the final SoC estimation. The proposed method is finally validated under different operating conditions and different temperatures, respectively. The results show that the root mean square error of the fused model is as low as 1.69%. This method can fully combine the advantages of LSTM for short-time sequences and Transformer for long-time sequence capture. The fused model is able to achieve satisfactory estimation accuracy under different temperatures and different working conditions with high accuracy and adaptability. Full article
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30 pages, 3950 KB  
Article
A Modular Hybrid SOC-Estimation Framework with a Supervisor for Battery Management Systems Supporting Renewable Energy Integration in Smart Buildings
by Mehmet Kurucan, Panagiotis Michailidis, Iakovos Michailidis and Federico Minelli
Energies 2025, 18(17), 4537; https://doi.org/10.3390/en18174537 - 27 Aug 2025
Cited by 2 | Viewed by 665
Abstract
Accurate state-of-charge (SOC) estimation is crucial in smart-building energy management systems, where rooftop photovoltaics and lithium-ion energy storage systems must be coordinated to align renewable generation with real-time demand. This paper introduces a novel, modular hybrid framework for SOC estimation, which synergistically combines [...] Read more.
Accurate state-of-charge (SOC) estimation is crucial in smart-building energy management systems, where rooftop photovoltaics and lithium-ion energy storage systems must be coordinated to align renewable generation with real-time demand. This paper introduces a novel, modular hybrid framework for SOC estimation, which synergistically combines the predictive power of artificial neural networks (ANNs), the logical consistency of finite state automata (FSA), and an adaptive dynamic supervisor layer. Three distinct ANN architectures—feedforward neural network (FFNN), long short-term memory (LSTM), and 1D convolutional neural network (1D-CNN)—are employed to extract comprehensive temporal and spatial features from raw data. The inherent challenge of ANNs producing physically irrational SOC values is handled by processing their raw predictions through an FSA module, which constrains physical validity by applying feasible transitions and domain constraints based on battery operational states. To further enhance the adaptability and robustness of the framework, two advanced supervisor mechanisms are developed for model selection during estimation. A lightweight rule-based supervisor picks a model transparently using recent performance scores and quick signal heuristics, whereas a more advanced double deep Q-network (DQN) reinforcement-learning supervisor continuously learns from reward feedback to adaptively choose the model that minimizes SOC error under changing conditions. This RL agent dynamically selects the most suitable ANN+FSA model, significantly improving performance under varying and unpredictable operational conditions. Comprehensive experimental validation demonstrates that the hybrid approach consistently outperforms raw ANN predictions and conventional extended Kalman filter (EKF)-based methods. Notably, the RL-based supervisor exhibits good adaptability and achieves lower error results in challenging high-variance scenarios. Full article
(This article belongs to the Section G: Energy and Buildings)
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15 pages, 8821 KB  
Article
Attofarad-Class Ultra-High-Capacitance Resolution Capacitive Readout Circuits
by Guoteng Ren, Saifei Yuan, Jingjing Peng, Ruitao Liu, Yuhao Feng, Haonan Liu, Wenshuai Lu, Fei Xing, Ting Sun and Shijie Yu
Sensors 2025, 25(8), 2461; https://doi.org/10.3390/s25082461 - 14 Apr 2025
Viewed by 778
Abstract
In order to meet the application requirements for high-precision and low-noise accelerometers in micro-vibration measurement and navigation fields, this paper presents the design and testing of an ultra-high-capacitance resolution capacitive readout circuit with attofarad-level precision. First, a differential charge amplifier circuit is employed [...] Read more.
In order to meet the application requirements for high-precision and low-noise accelerometers in micro-vibration measurement and navigation fields, this paper presents the design and testing of an ultra-high-capacitance resolution capacitive readout circuit with attofarad-level precision. First, a differential charge amplifier circuit is employed for the first stage of capacitance detection. To suppress noise interference in the circuit, a frequency-domain modulation technique is utilized to mitigate low-frequency noise. Subsequently, a differential subtraction circuit is implemented to reduce common-mode noise. Additionally, an improved filtering circuit is designed to suppress noise interference in the final stage. The test results indicate that the designed circuit operates at a carrier frequency of 1 MHz, achieving a capacitance resolution of up to 0.103 aF/Hz1/2 and a noise floor of 25.6 μg/Hz1/2, thereby meeting the requirements for high-precision and low-noise capacitance detection in MEMS accelerometers. Full article
(This article belongs to the Section Sensing and Imaging)
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26 pages, 6088 KB  
Article
A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell
by Kristijan Korez, Dušan Fister and Riko Šafarič
Batteries 2025, 11(1), 1; https://doi.org/10.3390/batteries11010001 - 24 Dec 2024
Cited by 1 | Viewed by 1565
Abstract
Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted [...] Read more.
Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. Full article
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13 pages, 12479 KB  
Article
A Novel Demodulation Algorithm Based on the Spatial-Domain Carrier Frequency Fringes Method
by Chenhaolei Han, Yuan Ju, Zongxu Zhao, Yuni He and Zhan Tang
Photonics 2024, 11(12), 1125; https://doi.org/10.3390/photonics11121125 - 28 Nov 2024
Viewed by 974
Abstract
Structured illumination microscopy (SIM) has attracted much attention from researchers due to its high accuracy, high efficiency, and strong adaptability. In SIM, demodulation is a key point to recovering three-dimensional topography, which directly affects the accuracy and validity of measurement. The traditional demodulation [...] Read more.
Structured illumination microscopy (SIM) has attracted much attention from researchers due to its high accuracy, high efficiency, and strong adaptability. In SIM, demodulation is a key point to recovering three-dimensional topography, which directly affects the accuracy and validity of measurement. The traditional demodulation methods are the phase-shift method and Fourier transform method. The phase-shift method has a high demodulation accuracy, but its time consumption is too long. The Fourier transform method has high efficiency, but its demodulation accuracy is lower due to the loss of high frequency information during the process of filtering. However, in actual measurement, due to the gamma effect of the projector and charge-coupled device (CCD), the phase-shift interval is not strictly equal to the default value, which causes phase-shift error. Therefore, the restored topography contains carrier frequency fringes, which affects the accuracy of the measurement and limits the wide application of SIM. In this paper, a novel demodulation algorithm based on spatial-domain carrier frequency shift is proposed to solve the problem. Through recombining multiple full-period phase-shift images, the error spectrum and the signal spectrum are separated from each other in the frequency domain, so as to eliminate the effect of carrier frequency fringes. Simulations and experiments are carried out to verify the feasibility of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Super-Resolution Optical Microscopy)
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15 pages, 3994 KB  
Proceeding Paper
Novel Sustainable Optimal Marine Microgrid Active-Power Management and Control Including Hybrid Power Generation and Multiple-Energy Storage Devices
by Aimad Boudoucha, Nour El Yakine Kouba, Sara Touhant and Yasmine Saidoune
Eng. Proc. 2024, 67(1), 78; https://doi.org/10.3390/engproc2024067078 - 26 Nov 2024
Viewed by 871
Abstract
This paper deals with the design of an advanced optimal strategy to enhance power management and frequency control in marine microgrids. The investigated system incorporates a mix of renewable energy sources coordinated with hybrid energy storage devices. A new robust optimal PIDN controller [...] Read more.
This paper deals with the design of an advanced optimal strategy to enhance power management and frequency control in marine microgrids. The investigated system incorporates a mix of renewable energy sources coordinated with hybrid energy storage devices. A new robust optimal PIDN controller is employed to tackle the intermittency challenges associated with wind and marine power generation, ensuring precise frequency control via time-domain simulations. A multiple-energy storage system, which includes SMES/batteries/ultra-capacitors (UCs) and fuel cells (FCs), was implemented to manage frequency variations and optimize the charge/discharge cycles of batteries. To further mitigate power fluctuations and extend the life of batteries, a low-pass filter was applied, inspired by optimization techniques for hybrid storage systems. A notable innovation of this study is the introduction of an offshore photovoltaic (PV) array into the system, enhancing the diversity and capacity for renewable energy production in the microgrid. A comprehensive comparative study was conducted, exploring a range of scenarios: with and without energy storage, with the integration of PV energy, excluding the use of diesel, and implementing battery filtering. This approach allowed for an evaluation of the impact of each configuration on the overall performance of the marine microgrid, underscoring significant enhancements in sustainability, efficiency, and a reduction in the dependence on fossil fuels. Preliminary results point to a considerable improvement in the energy management of isolated marine environments, showcasing the potential of this strategy for future marine microgrid applications. This research makes a significant contribution to the advancement of renewable energy management systems, presenting a viable and sustainable option for powering marine microgrids. Full article
(This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes)
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17 pages, 4904 KB  
Article
A Scalable Joint Estimation Algorithm for SOC and SOH of All Individual Cells within the Battery Pack and Its HIL Implementation
by Yongshan Liu, Di Zhang, Fan Wang, Tengfei Huang, Yuanbin Yu and Fangjie Sun
World Electr. Veh. J. 2024, 15(6), 236; https://doi.org/10.3390/wevj15060236 - 29 May 2024
Viewed by 1449
Abstract
Accurately obtaining the state of charge (SOC) and health (SOH) of all individual batteries in a battery pack can provide support for data acquisition, state estimation, and fault diagnosis. To verify the real-time performance and accuracy of the joint estimation algorithm for high-voltage [...] Read more.
Accurately obtaining the state of charge (SOC) and health (SOH) of all individual batteries in a battery pack can provide support for data acquisition, state estimation, and fault diagnosis. To verify the real-time performance and accuracy of the joint estimation algorithm for high-voltage battery packs composed of 96 individual cells in series, this article applies Simulink to develop a joint estimation algorithm for SOC and SOH based on the first-order RC equivalent circuit model (1RC ECM) and implements the algorithm’s cyclic calling for series nodes, enhancing the algorithm’s scalability. In the algorithm, the recursive least square method with fitting factor (FFRLS) is applied to calculate OCV, R0, and R1 in the time domain, and dual adaptive extended Kalman filter (DAEKF) is applied to joint estimation of SOC and SOH at multiple time scales. Finally, with the help of dSPACE and FASECU controllers, hardware in the loop (HIL) testing was completed in multiple scenarios. The results showed that the algorithm can accurately calculate the state of individual cells in real time, and even under various initial value deviations, it still has good regression performance, laying the foundation for future applications of electric vehicles. Full article
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21 pages, 3884 KB  
Article
Trapping Charge Mechanism in Hv1 Channels (CiHv1)
by Miguel Fernández, Juan J. Alvear-Arias, Emerson M. Carmona, Christian Carrillo, Antonio Pena-Pichicoi, Erick O. Hernandez-Ochoa, Alan Neely, Osvaldo Alvarez, Ramon Latorre, Jose A. Garate and Carlos Gonzalez
Int. J. Mol. Sci. 2024, 25(1), 426; https://doi.org/10.3390/ijms25010426 - 28 Dec 2023
Cited by 6 | Viewed by 1814
Abstract
The majority of voltage-gated ion channels contain a defined voltage-sensing domain and a pore domain composed of highly conserved amino acid residues that confer electrical excitability via electromechanical coupling. In this sense, the voltage-gated proton channel (Hv1) is a unique protein in that [...] Read more.
The majority of voltage-gated ion channels contain a defined voltage-sensing domain and a pore domain composed of highly conserved amino acid residues that confer electrical excitability via electromechanical coupling. In this sense, the voltage-gated proton channel (Hv1) is a unique protein in that voltage-sensing, proton permeation and pH-dependent modulation involve the same structural region. In fact, these processes synergistically work in concert, and it is difficult to separate them. To investigate the process of Hv1 voltage sensor trapping, we follow voltage-sensor movements directly by leveraging mutations that enable the measurement of Hv1 channel gating currents. We uncover that the process of voltage sensor displacement is due to two driving forces. The first reveals that mutations in the selectivity filter (D160) located in the S1 transmembrane interact with the voltage sensor. More hydrophobic amino acids increase the energy barrier for voltage sensor activation. On the other hand, the effect of positive charges near position 264 promotes the formation of salt bridges between the arginines of the voltage sensor domain, achieving a stable conformation over time. Our results suggest that the activation of the Hv1 voltage sensor is governed by electrostatic–hydrophobic interactions, and S4 arginines, N264 and selectivity filter (D160) are essential in the Ciona-Hv1 to understand the trapping of the voltage sensor. Full article
(This article belongs to the Special Issue State-of-the-Art Molecular Biology in Chile, 2nd Edition)
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23 pages, 10002 KB  
Article
MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction
by Laiwei Lu, Hong Zhao, Xiaotong Liu, Chuanlong Sun, Xinyang Zhang and Haixu Yang
Electronics 2023, 12(12), 2642; https://doi.org/10.3390/electronics12122642 - 12 Jun 2023
Cited by 15 | Viewed by 3381
Abstract
Rule-based energy management strategies not only make little use of the efficient area of engines and generators but also need to perform better planning in the time domain. This paper proposed a multi-signal vehicle speed prediction model based on the long short-term memory [...] Read more.
Rule-based energy management strategies not only make little use of the efficient area of engines and generators but also need to perform better planning in the time domain. This paper proposed a multi-signal vehicle speed prediction model based on the long short-term memory (LSTM) network, improving the accuracy of vehicle speed prediction by considering multiple signals. First, various signals were collected by simulating the vehicle model, and a Pearson correlation analysis was performed on the collected multiple signals in order to improve the model’s prediction accurate, and the appropriate signal was selected as the input to the prediction model. The experimental results indicate that the prediction method greatly improves the predictive effect compared with the support vector machine (SVM) vehicle speed prediction method. Secondly, the method was combined with the model predictive control-equivalent consumption strategy (MPC-ECMS) to form a control strategy suitable for power maintenance conditions enabling the equivalent factor to be adjusted adaptively in real-time and the target state of charge (SoC) value to be set. Pontryagin minimum principle (PMP) enables the battery to calculate the range extender output power at each moment. PMP, as the core algorithm of ECMS, is a common real-time optimal control algorithm. Then, taking into account the engine’s operating characteristics, the calculated range extender power was filtered to make the engine run smoothly. Finally, hardware-in-the-loop simulation (HIL) was used to verify the model. The simulation results demonstrate that this method uses less fuel than the equivalent fuel consumption minimum strategy (ECMS) by 1.32%, 9.47% when compared to the power-following control strategy, 15.66% when compared to the SVM-MPC-ECMS, and only 3.58% different from the fuel consumption of the dynamic programming (DP) control algorithm. This shows that this energy management approach can significantly improve the overall vehicle fuel economy. Full article
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9 pages, 5410 KB  
Article
A Gain-Enhanced Low Hardware Complexity Charge-Domain Read-Out Integrated Circuit Using a Sampled Charge Redistribution Technique
by Sung-Hun Jo
Electronics 2022, 11(23), 3846; https://doi.org/10.3390/electronics11233846 - 22 Nov 2022
Viewed by 1426
Abstract
A gain-enhanced low hardware complexity charge-domain read-out integrated circuit is implemented. By adopting a sampled charge redistribution technique, low hardware complexity is achieved, which in turn saves 10% of the die area and provides 33% gain enhancement compared to the conventional topology. In [...] Read more.
A gain-enhanced low hardware complexity charge-domain read-out integrated circuit is implemented. By adopting a sampled charge redistribution technique, low hardware complexity is achieved, which in turn saves 10% of the die area and provides 33% gain enhancement compared to the conventional topology. In particular, a charge-domain discrete-time filter with inherent reconfigurability is a key building block, which can also act as an anti-aliasing filter before the analog-to-digital converter. The measurement results show good agreement with the intended frequency response. The proposed filter is implemented using a 0.11 μm CMOS process and occupies 0.15 mm2. Full article
(This article belongs to the Special Issue CMOS Chips for Sensing and Communication)
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18 pages, 2056 KB  
Article
Digital Impedance Emulator for Battery Measurement System Calibration
by Francesco Santoni, Alessio De Angelis, Antonio Moschitta and Paolo Carbone
Sensors 2021, 21(21), 7377; https://doi.org/10.3390/s21217377 - 6 Nov 2021
Cited by 10 | Viewed by 3791
Abstract
Meaningful information on the internal state of a battery can be derived by measuring its impedance. Accordingly, battery management systems based on electrochemical impedance spectroscopy are now recognized as a feasible solutions for online battery control and diagnostic. Since the impedance of a [...] Read more.
Meaningful information on the internal state of a battery can be derived by measuring its impedance. Accordingly, battery management systems based on electrochemical impedance spectroscopy are now recognized as a feasible solutions for online battery control and diagnostic. Since the impedance of a battery is always changing along with its state of charge and aging effects, it is important to have a stable impedance reference in order to calibrate and test a battery management system. In this work we propose a programmable impedance emulator that in principle could be used for the calibration of any battery management system based on electrochemical impedance spectroscopy. A digital finite-impulse-response filter is implemented, whose frequency response is programmed so as to reproduce exactly the impedance of a real battery in the frequency domain. The whole design process of the filter is presented in detail. An analytical expression for the impedance of real battery in the frequency domain is derived from an equivalent circuit model. The model is validated both through numerical simulations and experimental tests. In particular, the filter is implemented on a low-cost microcontroller unit, and the emulated impedance is measured by means of a custom-made electrochemical impedance spectroscopy measuring system, and verified by using standard commercial bench instruments. Results on this prototype show the feasibility of using the proposed emulator as a fully controllable and low-cost reference for calibrating battery impedance measurement systems. Full article
(This article belongs to the Collection Instrument and Measurement)
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48 pages, 5655 KB  
Article
Parametric and Nonparametric PID Controller Tuning Method for Integrating Processes Based on Magnitude Optimum
by Tomaž Kos, Mikuláš Huba and Damir Vrančić
Appl. Sci. 2020, 10(17), 6012; https://doi.org/10.3390/app10176012 - 30 Aug 2020
Cited by 9 | Viewed by 4936
Abstract
Integrating systems are frequently encountered in power plants, paper-production plants, storage tanks, distillation columns, chemical reactors, and the oil industry. Due to the open-loop instability that leads to an unbounded output from a bounded input, the efficient control of integrating systems remains a [...] Read more.
Integrating systems are frequently encountered in power plants, paper-production plants, storage tanks, distillation columns, chemical reactors, and the oil industry. Due to the open-loop instability that leads to an unbounded output from a bounded input, the efficient control of integrating systems remains a challenging task. Many researchers have addressed the control of integrating processes: Some solutions are based on a single closed-loop controller, while others employ more complex control structures. However, it is difficult to find one solution requiring only a simple tuning procedure for the process. This is the advantage of the magnitude optimum multiple integration (MOMI) tuning method. In this paper, we propose an extension of the MOMI tuning method for integrating processes, controlled with a two-degrees-of-freedom (2-DOF) proportional–integral–derivative (PID) controller. This extension allows for calculations of the controller parameters from either time domain measurements or from a process transfer function of an arbitrary order with a time-delay, when both approaches are exactly equivalent. The user has the option to emphasise disturbance-rejection or tracking with the reference weighting factor b or apply two different reference filters for the best overall response. The proposed extension was also compared to other tuning methods for the control of integrating processes and tested on a charge-amplifier drift-compensation system. All closed-loop responses were relatively fast and stable, all in accordance with the magnitude optimum criteria. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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21 pages, 4398 KB  
Article
Elucidating the Structural Basis of the Intracellular pH Sensing Mechanism of TASK-2 K2P Channels
by Daniel Bustos, Mauricio Bedoya, David Ramírez, Guierdy Concha, Leandro Zúñiga, Niels Decher, Erix W. Hernández-Rodríguez, Francisco V. Sepúlveda, Leandro Martínez and Wendy González
Int. J. Mol. Sci. 2020, 21(2), 532; https://doi.org/10.3390/ijms21020532 - 14 Jan 2020
Cited by 8 | Viewed by 4643
Abstract
Two-pore domain potassium (K2P) channels maintain the cell’s background conductance by stabilizing the resting membrane potential. They assemble as dimers possessing four transmembrane helices in each subunit. K2P channels were crystallized in “up” and “down” states. The movements of [...] Read more.
Two-pore domain potassium (K2P) channels maintain the cell’s background conductance by stabilizing the resting membrane potential. They assemble as dimers possessing four transmembrane helices in each subunit. K2P channels were crystallized in “up” and “down” states. The movements of the pore-lining transmembrane TM4 helix produce the aperture or closure of side fenestrations that connect the lipid membrane with the central cavity. When the TM4 helix is in the up-state, the fenestrations are closed, while they are open in the down-state. It is thought that the fenestration states are related to the activity of K2P channels and the opening of the channels preferentially occurs from the up-state. TASK-2, a member of the TALK subfamily of K2P channels, is opened by intracellular alkalization leading the deprotonation of the K245 residue at the end of the TM4 helix. This charge neutralization of K245 could be sensitive or coupled to the fenestration state. Here, we describe the relationship between the states of the intramembrane fenestrations and K245 residue in TASK-2 channel. By using molecular modeling and simulations, we show that the protonated state of K245 (K245+) favors the open fenestration state and, symmetrically, that the open fenestration state favors the protonated state of the lysine residue. We show that the channel can be completely blocked by Prozac, which is known to induce fenestration opening in TREK-2. K245 protonation and fenestration aperture have an additive effect on the conductance of the channel. The opening of the fenestrations with K245+ increases the entrance of lipids into the selectivity filter, blocking the channel. At the same time, the protonation of K245 introduces electrostatic potential energy barriers to ion entrance. We computed the free energy profiles of ion penetration into the channel in different fenestration and K245 protonation states, to show that the effects of the two transformations are summed up, leading to maximum channel blocking. Estimated rates of ion transport are in qualitative agreement with experimental results and support the hypothesis that the most important barrier for ion transport under K245+ and open fenestration conditions is the entrance of the ions into the channel. Full article
(This article belongs to the Collection Computational Studies of Biomolecules)
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19 pages, 5490 KB  
Article
Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter
by Nicolas Sockeel, John Ball, Masood Shahverdi and Michael Mazzola
Batteries 2018, 4(4), 52; https://doi.org/10.3390/batteries4040052 - 19 Oct 2018
Cited by 12 | Viewed by 5982
Abstract
Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for [...] Read more.
Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared. Full article
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