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Improvement of The Battery State of Charge Estimation Using Recursive Least Square Based Adaptive Extended Kalman Filter | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 6، دوره 7، شماره 2، مرداد 2024، صفحه 141-151 اصل مقاله (1.88 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2024.47863.1537 | ||
| نویسندگان | ||
| Ramezan Havangi* ؛ Fatemeh Karimi | ||
| University of Birjand | ||
| چکیده | ||
| Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, navigation and industrial applications. These facts implies on the significance of SOC estimation from battery measurements that is the matter of the literature through the recent years. Due to the dependency of the EKF to the system model, the change in the battery parameters and noise information cause losing performance in the SOC estimation over the time. In this paper, we assume that the battery parameters including internal resistance and capacitor and also the noise information are varying over the time. To solve that, two separate on-line identification algorithms for parameters and noise information are introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify the resistance and capacitor values. Moreover, the process and measurement noise covariance are estimated based on iterative noise information identification algorithm. Then all of the updated values are used in the EKF algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model parameters. To address the challenge of uncertain model parameters, RLS is introduced. | ||
| کلیدواژه‌ها | ||
| Battery charge؛ State of the Charge (SOC)؛ Kalman Filter؛ Estimation | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 222 تعداد دریافت فایل اصل مقاله: 258 |
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