Battery choice and management for new-generation electric vehicles

被引:305
作者
Affanni, A [1 ]
Bellini, A
Franceschini, G
Guglielmi, P
Tassoni, C
机构
[1] Univ Parma, Dept Informat Engn, I-43100 Parma, Italy
[2] Politecn Torino, Dept Elect Engn, I-10129 Turin, Italy
关键词
batteries; electric vehicles (EVs);
D O I
10.1109/TIE.2005.855664
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the. introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending (in cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a big capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa.
引用
收藏
页码:1343 / 1349
页数:7
相关论文
共 17 条
[1]   EV battery state of charge: Neural network based estimation [J].
Affanni, A ;
Bellini, A ;
Concari, C ;
Franceschini, G ;
Lorenzani, E ;
Tassoni, C .
IEEE IEMDC'03: IEEE INTERNATIONAL ELECTRIC MACHINES AND DRIVES CONFERENCE, VOLS 1-3, 2003, :684-+
[2]  
[Anonymous], 2000, 15 ANN BATTERY C APP, DOI DOI 10.1109/BCAA.2000.838403
[3]  
[Anonymous], P 15 ANN BATT C APPL
[4]  
CASASSO P, 2003, P PCIM 03 NUR GERM M
[5]  
Casasso P., 2003, P IEEE IECON 03 NOV, V2, P1649
[6]   Energy gauge for lead-acid batteries in electric vehicles [J].
Caumont, O ;
Le Moigne, P ;
Rombaut, C ;
Muneret, X ;
Lenain, P .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2000, 15 (03) :354-360
[7]  
FRATTA A, 2000, P IEEE ISIE 00, V1, P230
[8]   THEORY AND APPLICATIONS OF NEURAL NETWORKS FOR INDUSTRIAL CONTROL-SYSTEMS [J].
FUKUDA, T ;
SHIBATA, T .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1992, 39 (06) :472-489
[9]  
*INF TECHN AG, 2004, BSP 772 T SMART POW
[10]  
Koehler U, 1997, IECEC-97 - PROCEEDINGS OF THE THIRTY-SECOND INTERSOCIETY ENERGY CONVERSION ENGINEERING CONFERENCE, VOLS 1-4, P93, DOI 10.1109/IECEC.1997.659166