📅 30 April 2024
DOI: 10.61132/ijmecie.v1i2.72
A Comparative Study on Electric Vehicle Battery Management Systems Using Machine Learning for Enhanced Safety and Longevity
International Journal of Mechanical, Electrical and Civil Engineering
Asosiasi Riset Ilmu Teknik Indonesia (ARITEKIN)
📄 Abstract
This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.
🔖 Keywords
#Electric Vehicles; Battery Management System; Machine Learning; State of Charge; State of Health; Thermal Runaway
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 April 2024
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2024
📝 HOW TO CITE
David Alexander Lee; Jessica Ann Smith; Emily Rose Johnson, "A Comparative Study on Electric Vehicle Battery Management Systems Using Machine Learning for Enhanced Safety and Longevity," International Journal of Mechanical, Electrical and Civil Engineering, vol. 1, no. 2, Apr. 2024.
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