Title :
Data-driven SOH prediction for EV batteries
Author :
Gae-won You ; Sangdo Park ; Sunjae Lee
Author_Institution :
Samsung Adv. Inst. of Technol., Samsung Electron., Yongin, South Korea
Abstract :
As electric vehicles (EVs) have been popularized, research on battery management system (BMS) of EVs´ core technology has considerably drawn attention. Among various functions of BMS, predicting state-of-health (SOH) that indexes batteries´ aging is the most crucial to determine replacement time of the battery or to estimate driving mileage. This paper studies how to predict SOH in practical EV environments where the batteries are charged and discharged dynamically.
Keywords :
battery management systems; battery powered vehicles; BMS; EV batteries; battery management system; data-driven SOH prediction; electric vehicles; state-of-health prediction; Aging; Artificial neural networks; Batteries; Data models; Pattern recognition; Predictive models; Training data;
Conference_Titel :
Consumer Electronics (ICCE), 2015 IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4799-7542-6
DOI :
10.1109/ICCE.2015.7066533
