Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles

This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraSc...

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Veröffentlicht in:	Programming and computer software Jg. 51; H. 6; S. 373 - 384
Hauptverfasser:	Daisy Merina. R, Saravana Ram. R, Lordwin Cecil Prabhaker Micheal
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.12.2025 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Batteries Computer Science Cost control Decision trees Driving conditions Electric vehicles Emissions Energy consumption Energy efficiency Energy management Field programmable gate arrays Genetic algorithms Lithium Machine learning Neural networks Operating Systems Power efficiency Power management Software Engineering Software Engineering/Programming and Operating Systems State of charge Traffic speed driving pattern recognition using machine learning battery management system (BMS) for electric vehicles long short-term memory (LSTM) networks bidirectional LSTM (Bi-LSTM) architecture decision tree classification state of charge (SoC) estimation algorithms FPGA-based embedded implementation
ISSN:	0361-7688, 1608-3261
Online-Zugang:	Volltext
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