Estimation of the battery state of charge: a switching Markov state-space model

International audienceAn efficient estimation of the State of Charge (SoC) of a battery is a challenging issue in the electric vehicle domain. The battery behavior depends on its chemistry and uncontrolled usage conditions, making it very difficult to estimate the SoC. This paper introduces a new model for SoC estimation given instantaneous measurements of current and voltage using a Switching Markov State-Space Model. The unknown parameters of the model are batch learned using a Monte Carlo approximation of the EM algorithm. Validation of the proposed approach on an electric vehicle real data is encouraging and shows the ability of this new model to accurately estimate the SoC for different usage conditions

Теорія та практика менеджменту безпеки

У збірнику подано тези доповідей та виступів учасників Міжнародної науково-практичної конференції, присвяченої питанням теорії менеджменту безпеки, безпеки особистості, прикладним аспектам забезпечення соціальної, екологічної, економічної безпеки підприємств, питанням механізму забезпечення соціоекологоекономічної безпеки регіону, проблемам забезпечення національної безпеки

Estimation en temps réel des flux origines-destinations dans un carrefour à feux par fusion de données multicapteurs

COMPIEGNE-BU (601592101) / SudocSudocFranceF

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

International audienceAn efficient estimation of the State of Charge (SoC) of an electrical battery in a real-time context is essential for the development of an intelligent management of the battery energy. The main performance limitations of a SoC estimator originate in limited Battery Management System hardware resources as well as in the battery behavior cross-dependence on the battery chemistry and its cycling conditions. This paper presents a review of methods and models used for SoC estimation and discusses their concept, adaptability and performances in real-time applications. It introduces a novel classification of SoC estimation methods to facilitate the identification of aspects to be improved to create an ideal SoC model. An ideal model is defined as the model that provides a reliable SoC for any battery type and cycling condition , online. The benefits of the machine learning methods in providing an online adaptive SoC estimator are thoroughly detailed. Remaining challenges are specified, through which the characteristics of an ideal model can emerge

Optimization of E-nose technology for detecting nonanal: a COVID-19 biomarker in exhaled breath

International audienceA low-cost, rapid and non-invasive diagnosis of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection is needed for the prevention and control of the pandemic. Coronavirus disease 2019 (COVID-19) mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In this work, an experimental protocol using an electronic nose (“e-nose”) for identifying a specific respiratory imprint in COVID-19 patients was optimized. The analytical performances of the Cyranose ® , a commercial e-nose device, were characterized by using a gas rig. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. We also evaluated whether the Cyranose ® could distinguish between expired air from five healthy patients spiked or not with nonanal, identified as one putative COVID-19 biomarker. Electrical resistance variation of 32 sensors was recorded in real-time by using the PC-nose software during all tests. A statistical data analysis was applied to e-nose sensor response using a software called “Enair” developed on purpose and using a built-in optimized algorithm.Cyranose® reveals a possible detection of low concentrations of nonanal (5 ppb) in breath and a significant discrimination from others Volatile Organic Compounds (VOCs) of healthy patients

Assessment of an e-nose performance for the detection of COVID-19 specific biomarkers

International audienceEarly, rapid and non-invasive diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is needed for the prevention and control of coronavirus disease 2019 (COVID-19). COVID-19 mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In the current study, an experimental protocol using an electronic-nose (‘e-nose’) for attempting to identify a specific respiratory imprint in COVID-19 patients was optimized. Thus the analytical performances of the Cyranose ® , a commercial e-nose device, were characterized under various controlled conditions. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. A statistical data analysis was applied to e-nose sensor response using common statistical analysis algorithms in an attempt to demonstrate the possibility to detect the presence of low concentrations of spiked acetone and nonanal in the breath samples of a healthy volunteer. Cyranose ® reveals a possible detection of low concentrations of these two compounds, in particular of 25 ppm nonanal, a possible marker of SARS-CoV-2 in the breath

Development and characterization of electronic noses for the rapid detection of COVID-19 in exhaled breath

International audienceNon-invasive and rapid approach is potentially needed for diagnosis of COVID-19. In this work, exhaled breath analysis using e-Nose, is presented as an innovative technique to identify the COVID-19 specific VOCs. The analytical performances of Cyranose®, a commercial e-Nose device, were investigated under controlled conditions. Sensitivity, limit of detection and reproducibility of standardized VOCs existing in the breath was assessed. In addition, the effect of various experimental conditions on sensor response was evaluated, including temperature, relative humidity, flow and sampling time, aiming to select the optimal parameters and to validate it in clinical trials to detect the COVID-19 biomarkers. Cyranose® exhibits high sensitivity and reproducible response towards acetone and nonanal, with a limit of detection of 63 ppb and 20 ppb respectively. Furthermore, results show that the variability of relative humidity, temperature and flow sampling, induced a significant sensors response variation, whereas, varying the sampling time does not affect significantly the sensor response

Optimization of E-nose technology for detecting nonanal: a COVID-19 biomarker in exhaled breath

International audienceA low-cost, rapid and non-invasive diagnosis of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection is needed for the prevention and control of the pandemic. Coronavirus disease 2019 (COVID-19) mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In this work, an experimental protocol using an electronic nose (“e-nose”) for identifying a specific respiratory imprint in COVID-19 patients was optimized. The analytical performances of the Cyranose ® , a commercial e-nose device, were characterized by using a gas rig. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. We also evaluated whether the Cyranose ® could distinguish between expired air from five healthy patients spiked or not with nonanal, identified as one putative COVID-19 biomarker. Electrical resistance variation of 32 sensors was recorded in real-time by using the PC-nose software during all tests. A statistical data analysis was applied to e-nose sensor response using a software called “Enair” developed on purpose and using a built-in optimized algorithm.Cyranose® reveals a possible detection of low concentrations of nonanal (5 ppb) in breath and a significant discrimination from others Volatile Organic Compounds (VOCs) of healthy patients

Development and characterization of electronic noses for the rapid detection of COVID-19 in exhaled breath

International audienceNon-invasive and rapid approach is potentially needed for diagnosis of COVID-19. In this work, exhaled breath analysis using e-Nose, is presented as an innovative technique to identify the COVID-19 specific VOCs. The analytical performances of Cyranose®, a commercial e-Nose device, were investigated under controlled conditions. Sensitivity, limit of detection and reproducibility of standardized VOCs existing in the breath was assessed. In addition, the effect of various experimental conditions on sensor response was evaluated, including temperature, relative humidity, flow and sampling time, aiming to select the optimal parameters and to validate it in clinical trials to detect the COVID-19 biomarkers. Cyranose® exhibits high sensitivity and reproducible response towards acetone and nonanal, with a limit of detection of 63 ppb and 20 ppb respectively. Furthermore, results show that the variability of relative humidity, temperature and flow sampling, induced a significant sensors response variation, whereas, varying the sampling time does not affect significantly the sensor response