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Departure Time Prediction Using Smartphone Data for Delayed-Full Charging BMS Algorithm

delete2023-10-08
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PRE
AI
Y
Yonggeon Lee
W
Woojin Song
J
Juhyun Song
Y
Youngtae Noh *
DOI：10.1145/3594739.3610688delete
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Abstract

Abstract

En 中文
Battery degradation, a gradual loss of capacity and performance due to frequent charging and discharging cycles, is a significant challenge to the widespread adoption of electric vehicles (EVs). This study proposes a BMS algorithm that delays full charging under selective conditions and completes charging immediately just before use to reduce battery degradation rate caused by fully charged state time. Our goal is to predict the charging end time based on an individual's departure time by capturing digital behavioral markers extracted from smartphone data, while minimizing reduction in driving range due to undesired predictions. Preliminary experiment was conducted with 41 subjects to assess the feasibility of the proposed approach. Our results demonstrate that the mobile passive features are capable of learning the departure behavior pattern, achieving an average mean absolute error (MAE) of 0.2336.
Keywords:
Delayed-Full Charging (DFC)
Battery Management System (BMS)
Departure Time Prediction
Digital Phenotyping

Journal

A
Adjunct Proceedings of the IEEE International Symposium on Mixed and Augmented Reality
IF:
0
Papers:
10
Citations:
0

Organization

No organization information available