@inproceedings{e280c714725f4e05babdd8135fc848ab,
title = "Condition Monitoring and Effective Capacity Improvement for Lithium Polymer Batteries",
abstract = "This paper proposes a model-based identification method for online monitoring of a state of charge (SOC) and state of health (SOH) of lithium polymer (Li-Po) batteries, which allows a runtime prediction and improves a useful capacity of the aged battery by an internal voltage compensation. For this, the internal resistance of the battery model is frequently updated with the latest estimated data. The algorithm utilizes the measured input current and the battery terminal voltage, where the Sigma-point Kalman filter (SKF) is utilized as an estimation tool. As a result, this scheme offers robustness and high accuracy for estimation under varying operating conditions of the battery. The feasibility of the proposed scheme is validated by the simulation and experimental results. It is shown that the estimation error for the SOC is about 4%.",
keywords = "Condition monitoring, lithium polymer batteries, Sigma-point Kalman filter, SOC, SOH",
author = "Nguyen, {Thanh Hai} and {Al Hosani}, Khalifa",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 ; Conference date: 14-10-2019 Through 17-10-2019",
year = "2019",
month = oct,
doi = "10.1109/IECON.2019.8926740",
language = "British English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "2330--2336",
booktitle = "Proceedings",
address = "United States",
}
