Estimation Strategies for the Condition Monitoring of a Battery Systemin a Hybrid Electric Vehicle

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.Comment: 18 pages, 16 figures, ISRN Signal Processing, 201

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes

Deep learning detection of types of water-bodies using optical variables and ensembling

Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification

Space Cooling Using Geothermal Single-Effect Water/Lithium Bromide Absorption Chiller

Abstract This research is proposed to fully investigate the performance of a single‐effect water/lithium bromide absorption chiller driven by geothermal energy. Since absorption cycles are considered as low‐grade energy cycles, this innovative idea of rejecting fluid from a single‐flash geothermal power plant with low‐grade energy would serve as efficient, economical, and promising technology. In order to examine the feasibility of this approach, a residential building which is located in Sharjah, UAE, considered to evaluate its cooling capacity of 39 kW which is calculated using MATLAB software. Based on the obtained cooling load, modeling of the required water/lithium bromide single‐effect absorption chiller machine is implemented and discussed. A detailed performance analysis of the proposed model under different conditions is performed using Engineering Equation Solver software (EES). Based on the obtained results, the major factors in the design of the proposed system are the size of the heat exchangers and the input heat source temperature. The results are presented graphically to find out the geofluid temperature and mass flow and solution heat exchanger effectiveness effects on the chiller thermal performance. Moreover, the effects of the size of all components of the absorption chiller on the cooling load to meet the space heating are presented. The thermal efficiency of the single‐flash geothermal power plant is about 13% when the power plant is at production well temperature 250℃, separator pressure 0.24 MPa, and condenser pressure 7.5 kPa. The results show that the coefficient of performance (COP) reaches about 0.87 at solution heat exchanger effectiveness of 0.9, when the geofluid temperature is 120℃

Modeling of Employee Recruitment Process through UML

Nowadays the organizations are functioning in a very competitive and aggressive environment. We have seen that every organization needed to recruit the employee for their work. It is well known that the organizations are facing the problem to keep their employee with their organizations because demand of employee higher salary or left organization or some other circumstances like change organization, low performance in the work or death of employee etc. The objective of this paper is to design a Unified Modeling Language (UML) model for recruiting the employee in the organization in a simple, systematic and effective way and also proposed the sequence and activity diagram for the above model. Author also evaluates the model through the employee recruitment data

Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles

To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery’s available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model’s parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery’s SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles