Estimation of SOC for Battery in Electric Vehicle Based on STUKF Algorithm

DOI

10.2991/masta-19.2019.57How to use a DOI?

Keywords

SOC, STUKF, UKF, PNGV model

Abstract

Lithium-ion (Li-on) battery state of charge (SOC) estimation is important for electric vehicles (EVs). To eliminate the effects of colored noise on SOC estimation, a new estimation method that based on Unscented Kalman Filter (UKF) Algorithm is proposed for high-power Li-ion batteries. First of all, based on the battery chemical properties, this paper established the improved PNGV battery model and identified the battery parameters. Then, accuracy of the model was verified under UDDS working condition. Finally, according to the influence of colored noise on estimating SOC of battery by the Unscented Kalman Filter (UKF) Algorithm, this paper proposed the Strong Tracking Unscented Kalman Filter (STUKF) Algorithm and introduced the fading factor. which forces the innovation sequence to be orthogonal and strengthens the correction of the state estimation by the new data. The result of simulation shows the STUKF Algorithm has batter tracking characteristic on estimating SOC of battery.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ming-xuan Gong
AU  - Xing-cheng Wang
AU  - Dan Liu
PY  - 2019/07
DA  - 2019/07
TI  - Estimation of SOC for Battery in Electric Vehicle Based on STUKF Algorithm
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 339
EP  - 346
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.57
DO  - 10.2991/masta-19.2019.57
ID  - Gong2019/07
ER  -