Online Supercapacitor Diagnosis for Electric Vehicle Applications

被引：33

作者：

El Mejdoubi, Asmae ^{[1
,2
]}

Oukaour, Amrane ^{[1
]}

Chaoui, Hicham ^{[3
]}

Slamani, Youssef ^{[1
]}

Sabor, Jalal ^{[2
]}

Gualous, Hamid ^{[1
]}

机构：

[1] Univ Caen Lower Normandie, Cherbourg Univ Lab Appl Sci LUSAC, F-50130 Cherbourg, France

[2] Univ Moulay Ismail, ENSAM, Meknes 50000, Morocco

[3] Tennessee Technol Univ, Dept Elect & Comp Engn, Ctr Mfg Res, Cookeville, TN 38505 USA

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2016年 / 65卷 / 06期

关键词：

Aging indicators; extended Kalman filter (EKF); parameter estimation; state-of-health (SoH); supercapacitors; supercapacitor diagnosis; DOUBLE-LAYER CAPACITORS; EXTENDED KALMAN FILTER; STATE ESTIMATION; ENERGY MANAGEMENT; CHARGE ESTIMATION; PERFORMANCE; VOLTAGE; SYSTEMS; HYBRID; OPTIMIZATION;

D O I：

10.1109/TVT.2015.2454520

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents an online diagnosis method for the supercapacitors' aging problem. State-of-health (SoH) estimation is an important feature since aging introduces degradation in the supercapacitors' performance, which might eventually lead to their failure. SoH is usually measured by electrochemical impedance spectroscopy. However, this has to be performed offline and requires interruption of the system's operation. Unlike this method, this paper presents an online diagnosis technique for supercapacitors using an extended Kalman observer as a well-known tool for its particularities and performances to study nonlinear parameter estimation. The main objective of this paper is the online SoH diagnosis based on the supercapacitors' aging indicator estimation. Moreover, the proposed online estimation strategy requires only voltage and current measurements, which reduces the number of sensors with respect to other methods. The effectiveness of the proposed online observer is shown through experimental results, and robustness to noise is also studied.

引用

页码：4241 / 4252

页数：12

共 39 条

[1] Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electric vehicles [J].