A Rayleigh Quotient-Based Recursive Total-Least-Squares Online Maximum Capacity Estimation for Lithium-Ion Batteries

被引：50

作者：

Kim, Taesic ^{[1
]}

Wang, Yebin ^{[1
]}

Sahinoglu, Zafer ^{[1
]}

Wada, Toshihiro ^{[2
]}

Hara, Satoshi

Qiao, Wei ^{[3
]}

机构：

[1] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA

[2] Mitsubishi Electr Corp, Adv Technol R&D Ctr, Amagasaki, Hyogo 6618661, Japan

[3] Univ Nebraska, Dept Elect & Comp Engn, Lincoln, NE 68588 USA

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2015年 / 30卷 / 03期

关键词：

Lithium-ion battery; online capacity estimation; Rayleigh quotient; recursive total least squares; state of health; EXTENDED KALMAN FILTER; MANAGEMENT-SYSTEMS; HEALTH DETERMINATION; SOH ESTIMATION; PART; STATE; MODEL; ALGORITHM; MATRIX; PACKS;

D O I：

10.1109/TEC.2015.2424673

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The maximum capacity, the amount of maximal electric charge that a battery can store, not only indicates the state of health, but also is required in numerous methods for state-of-charge estimation. This paper proposes an alternative approach to perform online estimation of the maximum capacity by solving the recursive total-least-squares (RTLS) problem. Different from prior art, the proposed approach poses and solves the RTLS as a Rayleigh quotient optimization problem. The Rayleigh quotient-based approach can be readily generalized to other parameter estimation problems including impedance estimation. Compared with other capacity estimation methods, the proposed algorithm enjoys the advantages of existing RTLS-based algorithms for instance, low computation, simple implementation, and high accuracy, and thus is suitable for use in real-time embedded battery management systems. The proposed method is compared with existing methods via simulations and experiments.

引用

页码：842 / 851

页数：10