Research Article
Accurate Estimation on the State-of-Charge of Lithium-Ion Battery Packs
@INPROCEEDINGS{10.1007/978-3-030-93479-8_17, author={Mengying Chen and Fengling Han and Long Shi and Yong Feng and Chen Xue and Chaojie Li}, title={Accurate Estimation on the State-of-Charge of Lithium-Ion Battery Packs}, proceedings={Broadband Communications, Networks, and Systems. 12th EAI International Conference, BROADNETS 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={BROADNETS}, year={2022}, month={1}, keywords={Luenberger observer State-of-charge (SoC) estimation Hysteresis resistor-capacitor model Lithium-ion battery Real-time vehicle interaction}, doi={10.1007/978-3-030-93479-8_17} }- Mengying Chen
Fengling Han
Long Shi
Yong Feng
Chen Xue
Chaojie Li
Year: 2022
Accurate Estimation on the State-of-Charge of Lithium-Ion Battery Packs
BROADNETS
Springer
DOI: 10.1007/978-3-030-93479-8_17
Abstract
Lithium-ion batteries have been extensively used worldwide for energy storage and supply in electric vehicles and other devices. An accurate estimation of their state-of-charge (SoC) is essential to ensure their safety and protect them from the explosion caused by overcharge. Large amounts of training data are required for SoC estimation resulting in a great computational burden. Model-based observation method can effectively estimate battery SoC with a limited amount of data. This study applied a combined model, including a one-state hysteresis model and a resistor-capacitor (RC) model, to diminish the parameter estimation errors caused by the hysteresis phenomenon, increasing the estimation accuracy. The Luenberger observer was designed based on the hysteresis RC battery model and evaluated under dynamic stress test (DST) and federal urban driving schedule (FUDS). Our simulation results have shown that the hysteresis RC model has better performance in terms of SoC estimation accuracy using Luenberger observer. Additionally, after the investigation of communication technologies, 5G cellular network offers feasibility for real-time vehicle interaction.
