Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(4)
Опубликована: Март 29, 2025
ABSTRACT This research presents a comprehensive assessment and comparison of various battery technologies employed in EVs, including lithium‐ion, nickel‐metal hydride, solid‐state, lithium iron phosphate, sodium‐ion batteries. A novel approach integrating IoT sensors machine learning is proposed to monitor analyze performance under real‐world driving conditions, with strong emphasis on fire prevention safety. Through an extensive literature review, the inherent characteristics, advantages, limitations each type are explored. deployed EVs can collect real‐time data important factors, such as voltage, current, temperature, state charge (SoC). Machine algorithms process this realize degradation patterns, optimize management strategies, enhance charging protocols. By leveraging data‐driven insights, aims improve efficiency, extend lifespan, mitigate hazards. The achieves prediction accuracy 99.4%, reduces risk by 72%, improves overall efficiency 18.6% compared conventional methods. Ultimately, findings will contribute development safer more sustainable EV technologies, shaping future eco‐friendly mobility.
Язык: Английский