@inproceedings{6b52a8581d624c718cad57c58b8ce76f,
title = "Model-based sensor fault detection for lithium-ion batteries in electric vehicles",
abstract = "Accurate estimation of the state of charge (SOC) of a lithium-ion battery in electric vehicles closely depends on the health status of the current and voltage acquisition sensors. In this paper, a simple and effective model-based sensor fault detection method for lithium-ion batteries is presented. In this method, the SOC of the battery is estimated in real time by the unscented Kalman filter (UKF), and the ampere-time integration method can calculate the cumulative discharge/charge amount for a certain period. Therefore, the ratio of the amount of SOC change to the amount of charge/discharge during a certain period is the estimated capacity. The difference between the capacity used for SOC estimation and the estimated capacity is defined as the residual used to detect whether a sensor failure has occurred. The proposed sensor fault detection method was verified by the dynamic stress test.",
keywords = "Capacity, Lithium-ion battery, SOC estimation, Sensor fault detection, UKF",
author = "Quanqing Yu and Rui Xiong and Cheng Lin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",
year = "2019",
month = apr,
doi = "10.1109/VTCSpring.2019.8746512",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",
address = "United States",
}
