Data-driven SOH prediction for EV batteries

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 replacemen...

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Bibliographic Details
Main Authors: Gae-won You, Sangdo Park, Sunjae Lee
Format: Conference Proceeding
Language:English
Subjects:
Aging
Artificial neural networks
Batteries
Data models
Pattern recognition
Predictive models
Training data
Online Access:Request full text
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Summary: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.
ISSN:2158-3994
2158-4001
DOI:10.1109/ICCE.2015.7066533