AN EVALUATION OF THE MOVING HORIZON ESTIMATION ALGORITHM FOR ONLINE ESTIMATION OF BATTERY STATE OF CHARGE AND STATE OF HEALTH

被引：0

作者：

Pattel, Bibin ^{[1
]}

Borhan, Hoseinali ^{[1
]}

Anwar, Sohel ^{[2
]}

机构：

[1] Cummins Inc, Columbus, IN 47201 USA

[2] IUPUI, Purdue Sch Engn & Technol, Indianapolis, IN USA

来源：

ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2014, VOL 4B | 2015年

关键词：

Moving Horizon Estimation; Optimization based State Estimation; Online Battery System Identification; State of Charge; State of Health; OF-CHARGE; IMPEDANCE MEASUREMENTS; MANAGEMENT-SYSTEMS; PACKS;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Moving Horizon Estimation (MHE) has emerged as a powerful technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances. In this paper, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SoC) and State of Health (SoH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that NINE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations.

引用

页数：11

共 20 条

[1]

[Anonymous], IEEE T VEHICULAR TEC

[2] Impedance measurements on lead-acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles [J].