.png){kind=icon}
.png){kind=icon}
Return
Current Estimation of Lithium Polymer Battery Using Recursive Least Square Algorithm
.png){kind=icon}
OA
AI
DOI：10.1063/1.4958524.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Current information on a battery is paramount important in a battery management system (BMS). In many systems, the current sensor is used to get the current value. In spite of its importance, a current sensor is expensive. To overcome this issue, this research aims to design a current estimation algorithm which is based on a current sensorless method where the battery is modeled in a Thevenin equivalent circuit model. The Thevenin model is then formed into autoregressive exogenous (ARX) model and the parameters are extracted by using Recursive Least Square (RLS) algorithm. This research uses lithium polymer battery with a capacity of 2200 mAh and the tests conducted in this research are constant pulse load test and variation load test to learn the performance of the algorithm. The results show that the current estimation has an error of 0.0863A RMSE in pulse load test and 0.6916A RMSE in variation load test.
Keywords:
ION BATTERIES
STATE
MODELS
CHARGE
VARIABLES
CELL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
1.9
Papers:
33
Citations:
562