Name
Title | ||
|---|---|---|
Lithium-ion batteries State-of-charge estimation based on interactive multiple-model Extended Kalman filter |
Abstract | ||
|---|---|---|
In this paper, an accurate algorithm for lithium-ion battery state-of-charge (SOC) estimation is proposed based on the combination of Extended Kalman filter (EKF) and interactive multiple model filter (IMM). Two multiple models are set up to represent the different degree of parameter shift in the Lithium ion battery. Equivalent circuit methodology is used to construct the non-linear battery models. Simulation results indicate that the proposed algorithm is capable of predicting lithium-ion battery State-of-charge. Comparison of accuracy and between the IMM-EKF and standard EKF is made, which prove IMM-EKF is better than standard EKF in estimation of State-of-charge. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IConAC.2016.7604919 | 2016 22nd International Conference on Automation and Computing (ICAC) |
Keywords | Field | DocType |
State-of-charge,Interactive multiple-model,extended Kalman filter,Lithium-ion Batteries | Extended Kalman filter,Control theory,Kalman filter,Control engineering,Engineering,Invariant extended Kalman filter,Battery (electricity),Lithium-ion battery,Lithium,Equivalent circuit,State of charge | Conference |
ISBN | Citations | PageRank |
978-1-5090-2877-1 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohu Xia | 1 | 0 | 0.34 |
| Yun Wei | 2 | 0 | 0.34 |