Name
Abstract | ||
|---|---|---|
The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares-extended Kalman filter (AFFRLS-EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS-EKF based on accurate modeling can effectively improve the estimation accuracy of SOC. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21175698 | SENSORS |
Keywords | DocType | Volume |
battery state of charge, parameter estimation, recursive least square, extended Kalman filtering | Journal | 21 |
Issue | ISSN | Citations |
17 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Li | 1 | 41 | 10.59 |
| Yingjie Zhang | 2 | 6 | 3.92 |
| Zuolei Hu | 3 | 0 | 0.34 |
| Ying Zhang | 4 | 163 | 25.25 |
| Jing Zhang | 5 | 40 | 7.49 |