Accuracy Of Sensor Research Articles

Illuminating the path to high-precision dual-mode optical thermometry: A Mn4+-activated oxyfluoride single crystal for a portable optical fiber thermometric platform

Multifunctional ratiometric and lifetime-technology dual-mode sensing plays a pivotal role in optical thermometry, yet poses substantial challenges in Mn4+-doped oxyfluorides/fluorides. Especially, despite of higher sensitivity, the applicability of lifetime-mode optical thermometers is generally restricted to large volume, highly integrated spectrometers. Here, a Mn4+-doped oxyfluoride single crystal Na2NbOF5:Mn4+ (NNOFM) is demonstrated as accurate dual-mode optical temperature sensor and developed into a portable optical fiber thermometric platform. Homogeneous NNOF shell is grown to improve the moisture resistant for extending operational lifespan. Under blue light excitation, two different temperature-sensitive sharp line vibrational transitions endow ratiometric mode temperature sensor. Importantly, rapidly decaying lifetime of Mn4+ produces high relative sensitivity Sr of 5.36 % K−1 at 448 K among Mn4+-based phosphors. Integrating NNOFM core–shell single crystal into optical fiber and coupling pulsed laser and oscilloscope, a portable optical fiber thermometric platform is realized. Accurate measurement result of boiling water being 373.33 K with quite small relative standard deviation to 0.04 % shows great potential of this fabricated thermometric platform for practical applications. This study not only enlightens the design of high-precision dual-mode optical thermometers in Mn4+-doped oxyfluorides/fluorides, but also illuminates the path to create portable optical fiber thermometric platforms.

An Integrated Approach for Real-Time Monitoring of Knee Dynamics with IMUs and Multichannel EMG.

Measuring human joint dynamics is crucial for understanding how our bodies move and function, providing valuable insights into biomechanics and motor control. Cerebral palsy (CP) is a neurological disorder affecting motor control and posture, leading to diverse gait abnormalities, including altered knee angles. The accurate measurement and analysis of knee angles in individuals with CP are crucial for understanding their gait patterns, assessing treatment outcomes, and guiding interventions. This paper presents a novel multimodal approach that combines inertial measurement unit (IMU) sensors and electromyography (EMG) to measure knee angles in individuals with CP during gait and other daily activities. We discuss the performance of this integrated approach, highlighting the accuracy of IMU sensors in capturing knee joint movements when compared with an optical motion-tracking system and the complementary insights offered by EMG in assessing muscle activation patterns. Moreover, we delve into the technical aspects of the developed device. The presented results show that the angle measurement error falls within the reported values of the state-of-the-art IMU-based knee joint angle measurement devices while enabling a high-quality EMG recording over prolonged periods of time. While the device was designed and developed primarily for measuring knee activity in individuals with CP, its usability extends beyond this specific use-case scenario, making it suitable for applications that involve human joint evaluation.

Large scale indoor occupant tracking using distributed acoustic sensing and machine learning

The recent COVID-19 pandemic has brought attention to the criticality of implementing measures to prevent viral transmission and reduce the risk of infection. It is necessary to identify crowd-gathering hazards and implement appropriate engineering control measures. Consequently, obtaining comprehensive footfall information at a large scale becomes significant. Vibration-based sensors are currently the preferred choice for footfall detection, with geophones being the commonly utilized sensors known for their effective performance. However, the deployment of geophones in large-scale scenarios presents challenges due to the substantial amount of manual labor involved. To address these limitations, the Distributed Acoustic Sensing (DAS) system, a non-intrusive sensor, is introduced as an alternative solution. While DAS has been successfully applied in various long-distance scenarios, such as pipeline surveillance and real-time train tracking, its effectiveness in footfall detection requires further evaluation. Therefore, this study proposes a machine learning method to achieve footstep recognition using DAS, considering the inherent time dependency and spatial continuity present in footfall trajectories. To further evaluate the effectiveness of DAS, geophones are selected as the benchmark sensors. The evaluation process encompasses footstep recognition and additional data implementation. The results demonstrate that the proposed recognition method achieves high accuracy for both sensors, thereby establishing DAS as a more applicable solution for footfall detection compared to geophones. In conclusion, DAS offers the advantage of flexible space resolution settings and adaptable deployment options, rendering it suitable for applications in diverse scenarios. Its capabilities make it suitable for large-scale occupancy monitoring scenarios.

Investigation of the accuracy of BAS-grade CO2 sensors for measuring infiltration rates

Carbon dioxide (CO2) sensors used in building automation systems (BAS) and occupant-generated CO2 allow for a low-cost and environmentally friendly method for continuously monitoring the air leakage of the building envelope in commercial buildings. However, the validity of this method is hindered by the potential inaccuracies of BAS-grade CO2 sensor measurements. This paper presents a comprehensive laboratory study that compares the accuracy of air changes per hour (ACH) measured using CO2 and BAS-grade CO2 sensors to that of SF6 and expensive lab-grade equipment (photoacoustic gas monitor). The results show that CO2 is a suitable alternative to SF6 when using lab-grade equipment. On average, ACH measured with lab-grade CO2 equipment were ±4.5 % of those measured with lab-grade SF6 measurements and 90 % of measurements were within ±10 %. However, the results also indicated that methods to correct for sensor drift are required to improve ACH measurement accuracy when using BAS-grade CO2 sensors that have not undergone routine calibration. By applying a method to correct for sensor drift, ACH measured with BAS-grade CO2 sensors were improved from ±10.9 % to ±5.1 % (on average) and the percentage of measurements within ±10 % increased from 48 % to 83 %. In addition, an environmental impact assessment of the tracer gas tests conducted in this study provides quantitative evidence to support the need to transition away from SF6-based tracer gas tests. The assessment showed that in this study releasing SF6 was ∼177 times worse for the environment than releasing CO2.

Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator

<p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p>

Low-cost air quality portable sensors and their potential use in respiratory health.

Air pollution is an environmental risk for the general population and for patients with various diseases, particularly respiratory diseases. Little data are available on personal exposure, but the recent emergence of low-cost air quality sensors (LCSs) should enable a better understanding of the health impacts of air pollution at the individual level. However, the reliability and accuracy of most sensors in the market have not been established, and a thorough understanding of their strengths and limitations is needed. We therefore conducted a review to address the following questions: 1) What is an LCS and what is the extent of its possible application? 2) Is the data obtained a reliable indicator of exposure? 3) What are the advantages and disadvantages of LCSs? 4) Could LCSs be useful in investigating the impact of air pollution on respiratory health? Further studies are needed to promote the use of LCS in research settings and among respiratory patients. This will allow us to monitor exposure levels, provide alerts and study the respiratory effects of individual-level air pollution.

A Method to Estimate Evapotranspiration in Greenhouse Conditions by Artificial Neural Networks Using Limited Climate Parameters

Precise estimation of evapotranspiration (ET) within greenhouse environments assumes pivotal significance in the context of effective agricultural water resource management. It has an important influence on rational irrigation management and water conservation. The present study estimates evapotranspiration by artificial neural networks (ANNs) using limited climate parameters with data from Oct.2016 to Nov.2017 from an experimental greenhouse. Using a sigmoid transfer function, two ANN models, 2-5-1 structure and 4-9-1 structure, were established through the algorithm of multi-layer feed-forward and back-propagation. At the same time, moisture sensors installed at different depths of substrate were also used to calculate the amount of evapotranspiration. The standard reference evapotranspiration was provided by a microlysimeter system, which used an electronic weighing scale to continuously sample the amount of water supplied and lost during the experiment. The ANN (2-5-1) model estimated ET with an RMSE of 0.0915 L/d and an R2 0.9201, with two neurons in the input layer corresponding to the daily mean temperature and daily mean humidity, five neurons in the hidden layer and one neuron in the output layer corresponding to the reference evapotranspiration. The ANN (4-9-1) model estimated ET with an RMSE of 0.0592 L/d and an R2 of 0.9622, with four input climate parameters: daily maximum temperature, daily minimum temperature, daily mean temperature, and daily mean humidity, but it had nine neurons for the hidden layer. The results of linear regression analyses of ET estimation between moisture sensors and actual measurement show that the accuracy of moisture sensors is less than the ANN models (RMSE, 0.1129 L/d; R2, 0.8749). Therefore, these results confirmed the ability of ET estimation by ANN models using limited climate parameters in greenhouse conditions.

A Sensitive Electrochemical DNA Biosensor for Determination of Anti-Cancer Drug Gemcitabine Based on an AuNPs/MWCNTs/Carbon Paste Electrode

GMB is a common anticancer drug that needs careful monitoring because of its variable and toxic effects. Detecting GMB in biological samples can help adjust the dosage and enhance the treatment outcomes. In this study, a novel electrochemical sensor was developed based on a carbon paste electrode modified with single stranded DNA, gold nanoparticles and multi-walled carbon nanotubes. We characterized the unmodified (bare CPE) and modified (ss-DNA/AuNPs/MWCNTs/CPE) electrodes using scanning electron microscopy (SEM), EDX analysis and cyclic voltammetry (CV) techniques. The oxidation peak current was linearly proportional to the GMB concentration in two linear ranges: 1–10 μM and 10–50 μM, with a detection limit of 0.52 μM and a limit of quantification (LOQ) of 1.75 μM. The lower linear concentration range (1–10 μM) showed a sensitivity of 5.68 A.M−1.cm−2, while the higher range (10–50 μM) showed a sensitivity of 1.34 A.M−1.cm−2. We also investigated the repeatability, applicability and reproducibility of this method for human serum samples. We tested the selectivity and practical ability of the ss-DNA/AuNPs/MWCNTs/CPE for the determination of GMB in the presence of various interfering species and human serum samples. The results indicated that the ss-DNA/AuNPs/MWCNTs/CPE was a selective, reliable and accurate electrochemical sensor for GMB.

INTERNET OF THINGS (IoT) BASED SCALES FOR DYNAMIC LOADS WITH WEIGHT FORECASTING FEATURE

A scale is a measuring instrument used to measure the weight of an object. The current weighing system has not been able to identify the item being weighed and has not been able to save the weighting data automatically, in this study a weighing system was developed that can identify the load using RFID and can store the weighing data in a web browser so that there is no manipulation of the weighing data. and the system is able to predict the results of weighing the load with a linear regression method to determine the growth of the weight object. The microcontroller used is Arduino Uno and the weight sensor used is a half bridge type load cell. From the sensor accuracy test with a load weight of 0.90 Kg, 12.95 Kg, 55.42 Kg, 125.95 Kg, the highest error occurred when the load was 0.90 Kg while the lowest error was at 55.45 Kg. the object of a moving load is difficult to produce the same weight in the first measurement and so on. Testing the accuracy of predictions is evidenced by the MSE value of 0.0017 and MAPE of 0.0742%, which means that the prediction using the linear regression method is classified as very good.

Effects of Milking System Operating Conditions on the Milk-Fat-Percentage Measuring Accuracy of an Inline Light-Scattering Sensor

Contamination of the inner surface of milk hoses of milking systems with milk residues, water residues and pathogenic microflora has a negative impact on milk quality and can affect the accuracy of light-scattering sensors measuring milk quality parameters. A three-stage study of the influence of milking system operating conditions on the accuracy of a light-scattering sensor is presented. The growth of pathogenic microflora on the inner surface of milk hoses was studied, the flow of milk and milk hoses rinsing was simulated and the accuracy of the light-scattering sensor was tested under conditions of low-quality rinsing of the milking system. A significant difference in the total microbial count was detected between the milk residues of healthy animals and the milk residues of animals with subclinical mastitis (1.2 × 104 CFU/mL and 9.2 × 104 CFU/mL). The presence of Staphylococcus (aureus and epidermidis) was detected in quantities much lower than those that could interfere with the analysis of milk quality parameters. Simulation modeling allowed us to prove that even when rinsing with hot water (80 °C) without chemical solutions, pathogenic microflora in milk hoses of the milking system will be suppressed. Modeling of the milk flow in the measuring chamber of the device showed the stable replacement of milk in the measuring chamber and provided actual measurements of milk quality parameters. It has been determined that low-quality rinsing and water residues in milk hoses lead to a significant increase in measurement error up to 15–20% during the first 30–40 s after the milking system starts operating. The study results confirm that the light-scattering sensors are resistant to such factors of the milking system operation as contamination by pathogenic microflora (S. aureus and S. epidermidis) and low-quality rinsing of milk hoses.

Assessment of skin pigmentation-related bias in pulse oximetry readings among adults

PurposeRecent reports that pulse oximeters may overestimate oxygen saturation in individuals with darker skin pigmentation have prompted concerns from regulatory authorities regarding racial bias. We investigated the performance of TruSignal SpO2 sensors (GE Healthcare, Helsinki, Finland) in adults with varying skin pigmentation.MethodsA retrospective study was conducted using a set of pooled assessments of SpO2/SaO2 measurements from nine studies to assess bias, accuracy (Arms), and precision of TruSignal sensors in healthy adults under induced hypoxia. Subgroup analyses were performed based on oxygen saturation levels (band 1, ≥ 70 and ≤ 80%; band 2, > 80 and ≤ 90%; band 3, > 90 and ≤ 100%).ResultsOf the 10,800 data points from 131 individuals, 8,202 (75.9%) and 2,598 (24.1%) were assigned to the light and dark pigment groups, respectively. Bias was 0.14% overall and less than 1% across oxygenation bands. The difference in bias between dark and light pigment groups was statistically significant at the low oxygenation band with SpO2 ≥ 70 and ≤ 80% (+ 0.58% and + 0.30% respectively; p = 0.0035). Throughout the saturation range, Arms was 1.64% in the light and 1.71% in the dark pigment group, within device specifications and regulatory requirements. Oxygenation was the dominating factor in stepwise ANOVA modeling. The mixed model also showed that bias was strongly affected by the oxygenation range.ConclusionTruSignal sensors demonstrated higher bias at lower oxygen saturation, with less than 0.5% difference between pigment groups. These findings raise new questions, such as ways to improve pulse oximetry measurements during challenging clinical conditions, including low perfusion.

A universal CRISPR-Cas14a responsive triple-sensitized upconversion photoelectrochemical sensor

It has recently been discovered that, like other members of the Cas family (12a and 13a), the clustered regularly interspaced short palindrome repeat CRISPR-Cas14a system not only mediates high-sensitivity detection with exceptionally strong gene editing ability but is also generally useful for DNA detection via fluorescence. Photoelectrochemical (PEC) sensors have been widely applied as efficient analytical tools. Measuring electrical signals is more cost-effective and the necessary equipment is more easily portable than fluorescence signal detectors, but their stability still needs to be improved. The high base resolution of CRISPR-Cas14a can compensate for such shortcomings. Therefore, electrical signals and fluorescence signals were combined, and the development of a universal CRISPR-Cas14a-responsive ultrasensitive upconversion PEC sensor is described in this paper. Moreover, strand displacement amplification (SDA) and a near-infrared (NIR) light source were utilized to further improve the stability and sensitivity of the photoelectric signals. At the same time, the modified working electrode (UCNPs-ssDNA-CdS@Au/ITO) on the three-electrode disposable sensor was used as the reporter probe, which cooperates with the trans-cleavage activity of Cas14a endonuclease. To verify the universality of this sensor, the UCNPs-Cas14a-based PEC sensor was applied for the detection of the small-molecule toxin T2 and protein kinase PTK7. Here, we report that the limit of detection of this reagent was within the fg range, successfully applied to the detection of T2 in oats and PTK7 in human serum. We propose that by combining PEC and CRISPR-14a, UCNPs-Cas14a-based PEC sensors could become powerful drivers for the extensive development of ultrasensitive, accurate and cost-effective universal sensors for detection and diagnosis.

Measurement of vitreous humor pressure in vivo using an optic fiber pressure sensor

We conducted a study to assess the pressure difference between the aqueous and vitreous humors in rabbit eyes using a direct intraocular pressure (IOP) measurement method. A micro-optic-fiber pressure sensor was utilized for this purpose. Preliminary experiments with enucleated porcine eyes confirmed the sensor's accuracy in measuring both aqueous and vitreous humor pressure. The main study involved six healthy albino rabbits, where the sensor measured the pressure in the anterior chamber (aIOP) and posterior vitreous-cavity (pIOP). These measurements were compared to aIOP values obtained through rebound tonometry. Additionally, pre- and postoperative pressure comparisons were made after performing a vitrectomy. Results revealed a significant disparity between aqueous and vitreous humor pressures. Prior to vitrectomy, pIOP was 22.8 mmHg, over twice as high as aIOP (11.0 mmHg), but decreased to a similar level following the procedure. Comparison between the sensor measurements and rebound tonometry showed agreement in aIOP values. In conclusion, our study demonstrates that vitreous humor pressure is consistently higher than aqueous humor pressure, reaching the upper limit of normal IOP. Furthermore, vitrectomy effectively reduces pIOP, aligning it with aIOP. These findings contribute valuable insights into intraocular pressure dynamics and have implications for clinical interventions targeting ocular pressure regulation.

Role of losses and coupling in bright-dark metasurfaces mimicking non-Hermitian parity-time symmetric systems

All real systems are essentially non-conservative in nature, which is non-Hermitian in the fundamental premises of the quantum realm. Such open systems obeying parity-time (PT) symmetry can offer intriguing physics, which has sparked much attention in recent years. The thoughtful interplay of loss and gain in PT-symmetric systems can tune the eigenstates from real to complex space passing through a singular point known as an exceptional point (EP) with concurrently coalescing the discrete eigenstates. In this regard, artificially engineered near-field coupled metasurfaces provide a remarkable podium to introduce as well as control loss and coupling strength simultaneously, manifesting a practical channel to attain EP. Motivated by this notion, we have theoretically explored near-field coupled dark-bright-mode assisted terahertz metasurfaces imitating a typical non-Hermitian PT-symmetric system. Structurally, such metasurfaces are realized by utilizing a pair of orthogonally twisted near-field coupled split-ring resonators (bright-dark-resonators). We have meticulously examined such a system with two distinctive approaches, i.e., the loss parameter and inter-resonator coupling strength. Eventually, our exploration demonstrates that judicious modulation of the loss parameter can invoke an undemanding route to approach the other wisely elusive exceptional point. We believe that this comprehensive study of non-Hermitian quantum systems in a photonic context can lead to the development of accurate and precise ultra-sensitive sensors besides other futuristic photonic devices.

Self-healable stretchable printed electronic cryogels for in-vivo plant monitoring

A key challenge in bioelectronics is to establish and improve the interface between electronic devices and living tissues, enabling a direct assessment of biological systems. Sensors integrated with plant tissue can provide valuable information about the plant itself as well as the surrounding environment, including air and soil quality. An obstacle in developing interfaces to plant tissue is mitigating the formation of fibrotic tissues, which can hinder continuous and accurate sensor operation over extended timeframes. Electronic systems that utilize suitable biocompatible materials alongside appropriate fabrication techniques to establish plant-electronic interfaces could provide for enhanced environmental understanding and ecosystem management capabilities. To meet these demands, this study introduces an approach for integrating printed electronic materials with biocompatible cryogels, resulting in stable implantable hydrogel-based bioelectronic devices capable of long-term operation within plant tissue. These inkjet-printed cryogels can be customized to provide various electronic functionalities, including electrodes and organic electrochemical transistors (OECTs), that exhibit high electrical conductivity for embedded conducting polymer traces (up to 350 S/cm), transconductance for OECTs in the mS range, a capacitance of up to 4.2 mF g−1 in suitable structures, high stretchability (up to 330% strain), and self-healing properties. The biocompatible functionalized cryogel-based electrodes and transistors were successfully implanted in plant tissue, and ionic activity in tomato plant stems was collected for over two months with minimal scar tissue formation, making these cryogel-based printed electronic devices excellent candidates for continuous, in-situ monitoring of plant and environmental status and health.

Modeling the Tokamak Exhaust Processing System in a Commercial Simulator for Process Monitoring Purposes

Nuclear fusion depends on tritium breeding and self-sufficiency. Tritium represents a hazard due to its radioactivity and migration properties. Because of these difficulties, ITER, the largest fusion experiment so far, relies on a conservative static procedure to monitor the tritium inventory. Future commercial fusion plants can avoid operation halts if a dynamic monitoring strategy proves itself valid. Tritium plant models have been developed for this kind of monitoring and analysis task, but sensor accuracy and reliability are an issue still to be addressed, and the path to dynamic monitoring remains unclear. The present work shows the modeling procedure of the Tokamak Exhaust Processing system in a commercial simulator, Aspen HYSYS, to reproduce the inventories, streams, process conditions, and compositions of this subsystem during operation. The model is verified in a steady-state scenario using data from the available literature. A demonstration of such a tritium plant subsystem shows meaningful value for several reasons. First, this process has not been modeled before in commercial dynamic simulators, which are typically used in the process industry. It will also allow new stakeholders to participate in future fusion-related projects. Second, it will play a key role in industry-like tritium process monitoring, in which the new model will act as a digital twin of the plant. Data-driven diagnostics can be fueled by model data, helping engineers to generate additional data that could otherwise be expensive to get directly from the plant. For these reasons, models will represent an essential part of a dynamic monitoring system, necessary for feasible fusion projects.

Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors

Abstract. A pre-deployment calibration and a field validation of two low-cost (LC) stations equipped with O3 and NO2 metal oxide sensors were addressed. Pre-deployment calibration was performed after developing and implementing a comprehensive calibration framework including several supervised learning models, such as univariate linear and non-linear algorithms, and multiple linear and non-linear algorithms. Univariate linear models included linear and robust regression, while univariate non-linear models included a support vector machine, random forest, and gradient boosting. Multiple models consisted of both parametric and non-parametric algorithms. Internal temperature, relative humidity, and gaseous interference compounds proved to be the most suitable predictors for multiple models, as they helped effectively mitigate the impact of environmental conditions and pollutant cross-sensitivity on sensor accuracy. A feature analysis, implementing dominance analysis, feature permutations, and the SHapley Additive exPlanations method, was also performed to provide further insight into the role played by each individual predictor and its impact on sensor performances. This study demonstrated that while multiple random forest (MRF) returned a higher accuracy than multiple linear regression (MLR), it did not accurately represent physical models beyond the pre-deployment calibration dataset, so a linear approach may overall be a more suitable solution. Furthermore, as well as being less computationally demanding and generally more suitable for non-experts, parametric models such as MLR have a defined equation that also includes a few parameters, which allows easy adjustments for possible changes over time. Thus, drift correction or periodic automatable recalibration operations can be easily scheduled, which is particularly relevant for NO2 and O3 metal oxide sensors. As demonstrated in this study, they performed well with the same linear model form but required unique parameter values due to intersensor variability.

Photocurable Polymer-Based 3D Printing: Advanced Flexible Strain Sensors for Human Kinematics Monitoring.

Vat photopolymerization-based additive manufacturing (AM) is critical in improving solutions for wearable sensors. The ability to add nanoparticles to increase the polymer resin's mechanical, electrical, and chemical properties creates a strong proposition for investigating custom nanocomposites for the medical field. This work uses a low-cost biocompatible polymer resin enhanced with multi-walled carbon nanotubes (MWCNTs), and a digital light processing-based AM system to develop accurate strain sensors. These sensors demonstrate the ability to carry a 244% maximum strain while lasting hundreds of cycles without degradation at lower strain ranges. In addition, the printing process allows for detailed prints to be accomplished at a sub-30 micron spatial resolution while also assisting alignment of the MWCNTs in the printing plane. Moreover, high-magnification imagery demonstrates uniform MWCNT dispersion by utilizing planetary shear mixing and identifying MWCNT pullout at fracture locations. Finally, the proposed nanocomposite is used to print customized and wearable strain sensors for finger motion monitoring and can detect different amounts of flexion and extension. The 3D printed nanocomposite sensors demonstrate characteristics that make it a strong candidate for the applications of human kinematics monitoring and sensing.

Application of Artificial Neural Networks and Adaptive Neuro‐Fuzzy Inference Systems for Attenuating Effects of Temperature and Humidity on Sensor Accuracy

AbstractThe novelty of this work is the correction of measurement errors in a galvanic cell sensor, which might occur due to temperature and humidity variations, using artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS). The training process for the ANN and ANFIS employed 540 data sets consisting of voltage responses of the galvanic cell to known oxygen concentrations, temperatures, and relative humidities. The trained ANFIS was embedded within a central processing unit (CPU) and tested on another 144 data sets with various oxygen concentrations, temperatures, and relative humidities. The deviations for the sensor with ANFIS were less than 0.5 % for 109 data points (75 %), and there was no deviation greater than 2.5 %.

State-of-charge estimation of Lithium-ion batteries using an adaptive dual unscented Kalman filter based on a reduced-order model

It is critical to accurately estimate the state of charge (SOC) of Lithium-ion batteries for the efficient, reliable, and safe operation of Electric Vehicles (EVs). Therefore, it is crucial to improve the methods for SOC determination by finding a beneficial trade-off between higher accuracy and increasing complexity within the Battery Management System (BMS). Physics-based models outperform the Equivalent Circuit Model (ECM) in terms of inherent electrochemical representation. This paper develops a new model based on the derivation of the reduced-order battery model in a control-oriented state space model to incorporate with the adaptive unscented Kalman filter (ADUKF). This allows the BMS to use a physics-based model with a low number of battery parameters resulting in a complexity deduction and much less effort in electrochemical parameter identification for online co-estimation of SOC and the battery model parameters. Moreover, an adaptive dual spherical UKF is implemented with the forgetting factor to reduce the computational capacity required for the traditional UKF and also to improve the covariance matching of measurement and system process noises. The proposed method is validated against driving cycle profiles, considering various conditions such as erroneous initial SOC, inaccurate battery capacity, and current sensor inaccuracy. Further, the robustness of the proposed method is verified against the impact of different temperatures. Lastly, the result is compared to ADUKF based on ECMs in terms of accuracy and computational cost.