ZEBRA battery SOC estimation using PSO-optimized hybrid neural model considering aging effect

被引：16

作者：

Gharavian, Davood ^{[1
]}

Pardis, Reza ^{[2
]}

Sheikhan, Mansour ^{[2
]}

机构：

[1] Shahid Abbaspour Univ Technol, EE Dept, Tehran, Iran

[2] Islamic Azad Univ, S Tehran Branch, EE Dept, Tehran, Iran

来源：

IEICE ELECTRONICS EXPRESS | 2012年 / 9卷 / 13期

关键词：

hybrid neural networks; state of charge; estimation; PSO algorithm; LEAD-ACID-BATTERIES; ELECTRIC VEHICLES; CHARGE ESTIMATION; STATE; NETWORK; CONTROLLER;

D O I：

10.1587/elex.9.1115

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：

The state of charge (SOC) estimation for electric vehicles (EVs) is important and helps to optimize the utilization of the battery energy storage in EVs. In this way, aging is also a key parameter impacting the performance of batteries. In this paper, a hybrid neural model is proposed for the SOC estimation of ZEBRA (Zero Emission Battery Research Activities) battery considering the aging effect through the state of health (SOH) and the discharge efficiency (DE) parameters. The number of hidden nodes in neural modules is also optimized using particle swarm optimization (PSO) algorithm. The SOC estimation error of the proposed system is 1.7% when compared with the real SOC obtained from a discharge test.

引用

页码：1115 / 1121

页数：7

共 21 条

[1]

Calcada D., 2010, P IEEE C EV COMP BAR, P1

[2] The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles [J].

Chan, CC ;

Lo, EWC ;

Shen, WX .

JOURNAL OF POWER SOURCES, 2000, 87 (1-2) :201-204

[3] State of charge estimation based on evolutionary neural network [J].

Cheng Bo ;

Bai Zhifeng ;

Cao Binggang .

ENERGY CONVERSION AND MANAGEMENT, 2008, 49 (10) :2788-2794

[4] Ni-MH batteries state-of-charge prediction based on immune evolutionary network [J].

Cheng Bo ;

Zhou Yanlu ;

Zhang Jiexin ;

Wang Junping ;

Cao Binggang .

ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (12) :3078-3086

[5]

Dustmann C. H., 2002, SWISS ZEBRA BATTERY

[6] Pruning product unit neural networks [J].

Ismail, A ;

Engelbrecht, AP .

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, :257-262

[7]

Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968

[8] Constructive hidden nodes selection of extreme learning machine for regression [J].

Lan, Yuan ;

Soh, Yeng Chai ;

Huang, Guang-Bin .

NEUROCOMPUTING, 2010, 73 (16-18) :3191-3199

[9]

Larminie J., 2003, ELECT VEHICLE TECHNO, P304

[10] State-of-charge estimation for electric scooters by using learning mechanisms [J].

Lee, Der-Tsai ;

Shiah, Shaw-Ji ;

Lee, Chien-Ming ;

Wang, Ying-Chung .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2007, 56 (02) :544-556

← 1 2 3 →