State-of-Charge and State-of-Health Lithium-Ion Batteries' Diagnosis According to Surface Temperature Variation

被引：159

作者：

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

Oukaour, Amrane ^{[1
]}

Chaoui, Hicham ^{[3
]}

Gualous, Hamid ^{[1
]}

Sabor, Jalal ^{[2
]}

Slamani, Youssef ^{[1
]}

机构：

[1] Univ Caen Normandie, LUSAC Lab, F-14032 Caen, 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 INDUSTRIAL ELECTRONICS | 2016年 / 63卷 / 04期

基金：

日本学术振兴会;

关键词：

Adaptive observer; extended Kalman filter (EKF); lithium-ion batteries; Lyapunov stability; parameters estimation; state-of-charge (SOC); state-of-health (SOH); EXTENDED KALMAN FILTER; OPEN-CIRCUIT VOLTAGE; MANAGEMENT-SYSTEM; MODEL; CELL;

D O I：

10.1109/TIE.2015.2509916

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

This paper presents a hybrid state-of-charge (SOC) and state-of-health (SOH) estimation technique for lithium-ion batteries according to surface temperature variation (STV). The hybrid approach uses an adaptive observer to estimate the SOH while an extended Kalman filter (EKF) is used to predict the SOC. Unlike other estimation methods, the closed-loop estimation strategy takes into account the STV and its stability is guaranteed by Lyapunov direct method. In order to validate the proposed method, experiments have been carried out under different operating temperature conditions and various discharge currents. Results highlight the effectiveness of the approach in estimating SOC and SOH for different aging conditions.

引用

页码：2391 / 2402

页数：12

共 51 条

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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].