has been cited by the following article(s):
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[1]
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Application of Machine Learning Techniques for Predicting the State of Health of Lithium-Ion Batteries
2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH),
2024
DOI:10.1109/INFOTEH60418.2024.10495936
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[2]
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Application of Machine Learning Techniques for Predicting the State of Health of Lithium-Ion Batteries
2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH),
2024
DOI:10.1109/INFOTEH60418.2024.10495936
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[3]
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Forecasting material quantity using machine learning and times series techniques
Journal of Electrical Engineering,
2024
DOI:10.2478/jee-2024-0029
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[4]
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Fuzzy Logic Controller-based Efficient Li-ion Battery Charging System for Electrical Vehicles
2024 International Conference on Future Technologies for Smart Society (ICFTSS),
2024
DOI:10.1109/ICFTSS61109.2024.10691357
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[5]
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Hybrid Machine Learning Model for EV Battery SoC and SoH Prediction
2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT),
2024
DOI:10.1109/ICEEICT61591.2024.10718590
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[6]
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The State of Health Estimation of Lithium-ion Battery based Support Vector Regression-Particle Swarm Optimization Model
2024 IEEE 10th Information Technology International Seminar (ITIS),
2024
DOI:10.1109/ITIS64716.2024.10845424
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[7]
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Application of Intelligent Digital Technology in Load Forecasting of New Power Systems
2023 International Conference on Network, Multimedia and Information Technology (NMITCON),
2023
DOI:10.1109/NMITCON58196.2023.10276027
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[8]
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Models for Battery Health Assessment: A Comparative Evaluation
Energies,
2023
DOI:10.3390/en16020632
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