کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6683437 501857 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles
چکیده انگلیسی
A novel online estimation technique for estimating the state of charge (SoC) of a lithium iron phosphate (LiFePO4) battery has been developed. Based on a simplified model, the open circuit voltage (OCV) of the battery is estimated through two cascaded linear filtering stages. A recursive least squares filter is employed in the first stage to dynamically estimate the battery model parameters in real-time, and then, a fading Kalman filter (FKF) is used to estimate the OCV from these parameters. FKF can avoid the possibility of large estimation errors, which may occur with a conventional Kalman filter, due to its capability to compensate any modeling error through a fading factor. By optimizing the value of the fading factor in the set of recursion equations of FKF with genetic algorithms, the errors in estimating the battery's SoC in urban dynamometer driving schedules-based experiments and real vehicle driving cycle experiments were below 3% compared to more than 9% in the case of using an ordinary Kalman filter. The proposed method with its simplified model provides the simplicity and feasibility required for real-time application with highly accurate SoC estimation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 169, 1 May 2016, Pages 40-48
نویسندگان
, , , , , ,