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State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

Computers, Materials & Continua 2022年第11期73卷 3149-3164页

作者： areeb khalid Syed Abdul Rahman Kashif Noor Ul Ain Ali Nasir Department of Electrical Engineering University of Engineering and TechnologyLahorePakistan Department of Electrical Engineering University of Central PunjabLahorePakistan

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the *** optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the *** is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery *** major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)*** model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the *** the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell *** validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical *** parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual ***-adaptive characteristic map using a lookup table was also *** nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

关键词： Aging model state of health lithium-ion cells battery management system state of charge battery modeling

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Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

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Computers, Materials & Continua 2023年第11期77卷 1795-1814页

作者： Arsal Javaid areeb Abbas Jehangir Arshad Mohammad khalid Imam Rahmani Sohaib Tahir Chauhdary Mujtaba Hussain Jaffery Abdulbasid S.Banga Department of Electrical and Computer Engineering COMSATS University IslamabadLahore CampusLahore54000Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah211Oman

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this *** addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine *** novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on ***,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted *** proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in *** results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing *** proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

关键词： Posture detection FSR sensor machine learning real-time KNN

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