Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (12)

Search Parameters:
Keywords = online calibration status

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
14 pages, 1789 KiB  
Article
Development and Validation of a Predictive Nomogram for Venous Thromboembolism Risk in Multiple Myeloma Patients: A Single-Center Cohort Study in China
by Haolin Zhang, Xi Zhang, Xiaosheng Li, Qianjie Xu, Yuliang Yuan, Zuhai Hu, Yulan Zhao, Yao Liu, Yunyun Zhang and Haike Lei
Biomedicines 2025, 13(4), 770; https://doi.org/10.3390/biomedicines13040770 - 21 Mar 2025
Viewed by 607
Abstract
Objectives: Venous thromboembolism (VTE) is a significant complication in patients with multiple myeloma (MM) that adversely affects morbidity, mortality, and treatment outcomes. This study aimed to develop and validate a predictive nomogram for assessing VTE risk in MM patients using clinicopathological factors. Methods: [...] Read more.
Objectives: Venous thromboembolism (VTE) is a significant complication in patients with multiple myeloma (MM) that adversely affects morbidity, mortality, and treatment outcomes. This study aimed to develop and validate a predictive nomogram for assessing VTE risk in MM patients using clinicopathological factors. Methods: Clinical data, including 25 candidate risk factors, were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for VTE. The nomogram was constructed using these variables, and its performance was evaluated by plotting receiver operating characteristic (ROC) curves, calculating the area under the curve (AUC), and conducting calibration and decision curve analysis (DCA). Additionally, an online calculator was developed for clinical use. Results: In total, 148 patients (17.5%) developed VTE in this study. The independent risk factors included age, Karnofsky performance status (KPS), anticoagulation therapy, erythropoietin use, and hemoglobin (Hb), platelet (PLT), calcium (Ca), activated partial thromboplastin time (APTT), and D-dimer levels. The nomogram demonstrated robust discriminative ability, with a C-index of 0.811 in the training cohort and 0.714 in the validation cohort. The calibration curves exhibited a high level of agreement between the predicted and observed probabilities. DCA confirmed the nomogram’s clinical utility across various threshold ranges, outperforming the “treat all” and “treat none” strategies. Conclusions: This study successfully developed and validated a nomogram for predicting VTE risk in MM patients, demonstrating substantial predictive accuracy and clinical applicability. The nomogram and accompanying online calculator provide valuable tools for individualized VTE risk assessment and informed clinical decision-making. Full article
(This article belongs to the Special Issue Pathogenesis, Diagnosis and Treatment of Hematologic Malignancies)
Show Figures

Figure 1

22 pages, 10897 KiB  
Article
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance
by Alexandre Martins, Inácio Fonseca, José Torres Farinha, João Reis and António J. Marques Cardoso
Sensors 2023, 23(5), 2402; https://doi.org/10.3390/s23052402 - 21 Feb 2023
Cited by 14 | Viewed by 5018
Abstract
Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected [...] Read more.
Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor. Full article
Show Figures

Figure 1

15 pages, 1964 KiB  
Article
Web-Based Dynamic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Breast Cancer Patients with Lung Metastases
by Kangtao Wang, Yuqiang Li, Dan Wang and Zhongyi Zhou
J. Pers. Med. 2023, 13(1), 43; https://doi.org/10.3390/jpm13010043 - 26 Dec 2022
Cited by 9 | Viewed by 2729
Abstract
Background: 60–70% of patients who die from breast cancer have lung metastases. However, there is a lack of readily available tools for accurate risk stratification in patients with breast cancer lung metastases (BCLM). Therefore, a web-based dynamic nomogram was developed for BCLM to [...] Read more.
Background: 60–70% of patients who die from breast cancer have lung metastases. However, there is a lack of readily available tools for accurate risk stratification in patients with breast cancer lung metastases (BCLM). Therefore, a web-based dynamic nomogram was developed for BCLM to quickly, accurately, and intuitively assess overall and cancer-specific survival rates. Methods: Patients diagnosed with BCLM between 2004 and 2016 were extracted from the Surveillance, Epidemiology, and Final Results (SEER) database. After excluding incomplete data, all patients were randomly assigned to training and validation cohorts (2:1). Patients’ basic clinical information, detailed pathological staging and treatment information, and sociological information were included in further analysis. Nomograms were constructed following the evaluations of the Cox regression model and verified using the concordance index (C-index), calibration curves, time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Web-based dynamic nomograms were published online. Results: 3916 breast cancer patients with lung metastases were identified from the SEER database. Based on multivariate Cox regression analysis, overall survival (OS) and cancer-specific survival (CSS) are significantly correlated with 13 variables: age, marital status, race, grade, T stage, surgery, chemotherapy, bone metastatic, brain metastatic, liver metastatic, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). These are included in the construction of the nomogram of OS and CSS. The time-dependent receiver operating characteristic curve, decision curve analysis, consistency index, and calibration curve prove the distinct advantages of the nomogram. Conclusions: Our web-based dynamic nomogram effectively integrates patient molecular subtype and sociodemographic characteristics with clinical characteristics and guidance and can be easily used. ER-Negative should receive attention in diagnosing and treating BCLM. Full article
Show Figures

Figure 1

15 pages, 5359 KiB  
Article
Heat Transfer Model and Soft Sensing for Segmented Fluidized Bed Dryer
by Mengke Lu, Patrick Kranz, Andrew Salmon, Sam Wilkinson and Rok Sibanc
Processes 2022, 10(12), 2609; https://doi.org/10.3390/pr10122609 - 6 Dec 2022
Cited by 4 | Viewed by 2577
Abstract
The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the [...] Read more.
The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequently, a soft sensor is established by combining data sources, a real-time data communication method, and the developed drying model, and it shows the capability of predicting real-time LOD, where the error of end-point LOD is within 0.5 wt.%. The work provides detailed steps and applicable tools for developing a soft sensor, and the online deployment of the soft sensor could support continuous production in the Seg-FBD by enabling visualization of process status and determination of process end point. Full article
(This article belongs to the Section Pharmaceutical Processes)
Show Figures

Figure 1

17 pages, 3936 KiB  
Article
Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement
by Yafei Wang, Xueyan Ding, Guoliang Yuan and Xianping Fu
Sensors 2022, 22(6), 2326; https://doi.org/10.3390/s22062326 - 17 Mar 2022
Cited by 15 | Viewed by 4116
Abstract
The human eye gaze plays a vital role in monitoring people’s attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These [...] Read more.
The human eye gaze plays a vital role in monitoring people’s attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These systems usually tend to have poor generalization performance during the online gaze prediction, which is caused by the estimation bias between the training domain and the deployment domain, making the predicted gaze points shift from their correct location. To solve this problem, a novel driver’s eye gaze tracking method with non-linear gaze point refinement is proposed in a monitoring system using two cameras, which eliminates the estimation bias and implicitly fine-tunes the gaze points. Supported by the two-stage gaze point clustering algorithm, the non-linear gaze point refinement method can gradually extract the representative gaze points of the forward and mirror gaze zone and establish the non-linear gaze point re-mapping relationship. In addition, the Unscented Kalman filter is utilized to track the driver’s continuous status features. Experimental results show that the non-linear gaze point refinement method outperforms several previous gaze calibration and gaze mapping methods, and improves the gaze estimation accuracy even on the cross-subject evaluation. The system can be used for predicting the driver’s attention. Full article
(This article belongs to the Topic Intelligent Transportation Systems)
Show Figures

Figure 1

16 pages, 4707 KiB  
Article
A Survey on the Use of Spirometry in Small Animal Anaesthesia and Critical Care
by Mathieu Raillard, Olivier Levionnois and Martina Mosing
Animals 2022, 12(3), 239; https://doi.org/10.3390/ani12030239 - 19 Jan 2022
Cited by 6 | Viewed by 2818
Abstract
The objective was to document the use of spirometry and ventilation settings in small animal anaesthesia and intensive care through a descriptive, open, online, anonymous survey. The survey was advertised on social media and via email. Participation was voluntary. The google forms platform [...] Read more.
The objective was to document the use of spirometry and ventilation settings in small animal anaesthesia and intensive care through a descriptive, open, online, anonymous survey. The survey was advertised on social media and via email. Participation was voluntary. The google forms platform was used. It consisted of eight sections in English: demographic information, use of spirometry in spontaneously ventilating/mechanically ventilated dogs, need for spirometry, equipment available and calibration status, ventilation modes, spirometry displays, compliance (CRS) and resistance (RRS) of the respiratory system. Simple descriptive analyses were applied. There were 128 respondents. Respondents used spirometry more in ventilated dogs than during spontaneous breathing. Over 3/4 of the respondents considered spirometry essential in “selected” (43%) or “most” cases (33%). Multiple devices and technologies were used. The majority of the respondents were not directly involved in or informed about the calibration of their equipment. Of all displays, pressure-volume loops were the most common. Values of CRS and RRS were specifically monitored in more than 50% of cases by 44% of the respondents only. A variety of ventilation modes was used. Intensivists tend to use smaller VT than anaesthetists. More information on reference intervals of CRS and RRS and technical background on spirometers is required Full article
(This article belongs to the Special Issue Respiratory Mechanics in Veterinary Anaesthesia)
Show Figures

Figure 1

37 pages, 17832 KiB  
Article
Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning
by Jichang Ma, Hui Xie, Kang Song and Hao Liu
Symmetry 2022, 14(1), 31; https://doi.org/10.3390/sym14010031 - 27 Dec 2021
Cited by 14 | Viewed by 5326
Abstract
The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, [...] Read more.
The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h). Full article
Show Figures

Figure 1

17 pages, 5322 KiB  
Article
Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
by Yang Chen, Zhentao Wang, Zhao Li, Hongquan Zheng and Jingmin Dai
Appl. Sci. 2021, 11(24), 12149; https://doi.org/10.3390/app112412149 - 20 Dec 2021
Cited by 30 | Viewed by 3881
Abstract
The type and concentration of dissolved gases in transformer insulating oil are used to assess transformer conditions. In this paper, an online detection setup for measuring the concentration of multicomponent gases dissolved in transformer insulating oil is developed, which consists of an oil-gas [...] Read more.
The type and concentration of dissolved gases in transformer insulating oil are used to assess transformer conditions. In this paper, an online detection setup for measuring the concentration of multicomponent gases dissolved in transformer insulating oil is developed, which consists of an oil-gas separation system and an optical system for acquiring the transformer status in real time. The oil-gas separation system uses low pressure, constant temperature, and low-frequency stirring as working conditions for degassing large-volume oil samples based on modified headspace degassing. The optical system uses tunable diode laser absorption spectroscopy (TDLAS) to determine the gas concentration. Six target gases (methane, ethylene, ethane, acetylene, carbon monoxide, and carbon dioxide) were detected by three near-infrared lasers (1569, 1684, and 1532 nm). The stability of the optical system was improved by the common optical path formed by time-division multiplexing (TDM) technology. The calibration experiments show that the second harmonics and the concentrations of the six gases are linear. A comparison experiment with gas chromatography (GC) demonstrates that the error of acetylene reaches the nL/L level, while the other gases reach the μL/L level. The data conforms to the power industry testing standards, and the state of the transformer is analyzed by the detected six characteristic gases. The setup provides an effective basis for the online detection of dissolved gas in transformer insulating oil. Full article
Show Figures

Figure 1

12 pages, 1212 KiB  
Article
Measurement of Exhaled Nitric Oxide in 456 Lung Cancer Patients Using a Ringdown FENO Analyzer
by Jing Li, Qingyuan Li, Xin Wei, Qing Chen, Meixiu Sun and Yingxin Li
Metabolites 2021, 11(6), 352; https://doi.org/10.3390/metabo11060352 - 31 May 2021
Cited by 4 | Viewed by 3171
Abstract
The objective of this study was to investigate the clinical value of exhaled nitric oxide (NO) for diagnosing lung cancer patients by using a relatively large sample. An online and near-real-time ringdown exhaled NO analyzer calibrated by an electrochemical sensor at clinical was [...] Read more.
The objective of this study was to investigate the clinical value of exhaled nitric oxide (NO) for diagnosing lung cancer patients by using a relatively large sample. An online and near-real-time ringdown exhaled NO analyzer calibrated by an electrochemical sensor at clinical was used for breath analysis. A total of 740 breath samples from 284 healthy control subjects (H) and 456 lung cancer patients (LC) were collected. The recorded data included exhaled NO, medications taken within the last half month, demographics, fasting status and smoking status. The LC had a significantly higher level of exhaled NO than the H (H: 21.0 ± 12.1 ppb vs. LC: 34.1 ± 17.2 ppb). The area under the receiver operating characteristic curve for exhaled NO predicting LC and H was 0.728 (sensitivity was 0.798; specificity was 0.55). There was no significant difference in exhaled NO level between groups divided by different types of LC, tumor node metastasis (TNM) stage, sex, smoking status, age, body mass index (BMI) or fasting status. Exhaled NO level alone is not a useful clinical tool for identifying lung cancer, but it should be considered when developing a diagnosis model of lung cancer by using breath analysis. Full article
(This article belongs to the Special Issue Non-Invasive Monitoring of Human Metabolism)
Show Figures

Figure 1

14 pages, 11963 KiB  
Article
Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production
by Giovani Preza-Fontes, Junming Wang, Muhammad Umar, Meilan Qi, Kamaljit Banger, Cameron Pittelkow and Emerson Nafziger
Sustainability 2021, 13(10), 5649; https://doi.org/10.3390/su13105649 - 18 May 2021
Cited by 4 | Viewed by 2702
Abstract
Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt [...] Read more.
Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits. Full article
(This article belongs to the Special Issue Smart Farming and Sustainability)
Show Figures

Figure 1

8 pages, 265 KiB  
Article
Survey Methods of the 2018 International Tobacco Control (ITC) Japan Survey
by Mary E. Thompson, Christian Boudreau, Anne C.K. Quah, Janine Ouimet, Grace Li, Mi Yan, Yumiko Mochizuki, Itsuro Yoshimi and Geoffrey T. Fong
Int. J. Environ. Res. Public Health 2020, 17(7), 2598; https://doi.org/10.3390/ijerph17072598 - 10 Apr 2020
Cited by 7 | Viewed by 5269
Abstract
This paper describes the methods of the Wave 1 (2018) International Tobacco Control (ITC) Japan Survey. The respondents were adults aged 20 years and older in one of four user groups: (1) cigarette-only smokers who smoked at least monthly and used heated tobacco [...] Read more.
This paper describes the methods of the Wave 1 (2018) International Tobacco Control (ITC) Japan Survey. The respondents were adults aged 20 years and older in one of four user groups: (1) cigarette-only smokers who smoked at least monthly and used heated tobacco products (HTPs) not at all or less than weekly, (2) HTP-only users who used HTPs at least weekly and smoked cigarettes not at all or less than monthly, (3) cigarette-HTP dual users who smoked at least monthly and used HTPs at least weekly, and (4) non-users who had never smoked or who smoked less than monthly and used HTPs less than weekly. Eligible respondents were recruited by a commercial survey firm from its online panel. Respondents were allocated proportionally to sample strata based on demographic, geographic, and user type specifications benchmarked to a national reference. Survey weights, accounting for smoking/HTP use status, sex, age, education, and geography, were calibrated to benchmarks from a nationally representative survey in Japan. Response rate was 45.1% and cooperation rate was 96.3%. The total sample size was 4615 (3288 cigarette smokers, 164 exclusive HTP users, 549 cigarette-HTP dual users, and 614 non-users). The 2018 ITC Japan Survey sampling design and survey data collection methods will allow analyses to examine prospectively the use of cigarettes and HTPs in Japan and factors associated with the use of both products and of transitions between them. Full article
10 pages, 3158 KiB  
Article
State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries
by Tsung-Hsi Wu and Chin-Sien Moo
Energies 2017, 10(7), 987; https://doi.org/10.3390/en10070987 - 13 Jul 2017
Cited by 29 | Viewed by 7764
Abstract
This research is focused on state-of-charge (SOC) estimation with state-of-health (SOH) calibration for lithium-ion batteries on the basis of the coulomb counting method. The proposed approach intends to present an easy-to-use solution with high accuracy for estimating battery statuses [...] Read more.
This research is focused on state-of-charge (SOC) estimation with state-of-health (SOH) calibration for lithium-ion batteries on the basis of the coulomb counting method. The proposed approach intends to present an easy-to-use solution with high accuracy for estimating battery statuses without the need for demanding calculations or hard-earned databases. To estimate the SOC of an aged battery more accurately, the degradation of its full capacity has to be taken into account. By scheduling the battery’s charging/discharging current and monitoring the battery’s status, the existing full capacity can be updated regularly by regular calibration or occasionally by partial calibration, in which the charging/discharging rates are normalized with the latest updated full capacity to agree with the battery’s statuses. To exclude the misestimation caused by current measuring error, the SOC is reset to 0% when the battery is exhausted and 100% for a fully charged battery. With an updated SOH, the battery C-rate is re-scaled accordingly. Experimental tests are carried out to demonstrate that the proposed approach can provide an accurate online indication of batteries’ SOCs. With an implanted error of 0.3% in current measuring, the SOC estimation error can always be less than 1.905% after a number of SOH calibrations. Full article
Show Figures

Figure 1

Back to TopTop