兵工自动化2011，Vol.30Issue(12)：44-48,5.DOI:10.3969/j.issn.1006-1576.2011.12.013
基于Blackfin的锂电池管理系统
Lithium Battery Management System Based on Blackfin
摘要
Abstract
Aiming at the low accuracy and poor scalability problem of SOC estimation for lithium battery management system presently, designed the lithium battery management system based on Blackfin digital signal. This system implements the function of real-time monitoring of lithium battery, remaining power estimation, extending groups of lithium battery by the CAN bus communication, the danger alarm and automatic protection of the lithium battery, etc. With respect to the remaining power estimation algorithm, this paper proposes a BP neural network method which is optimized by genetic algorithm and the ant algorithm (abbreviated to GAAA algorithm). Experiment result shows that the algorithm has higher SOC accuracy estimated precision and faster operating speed than the BP neural network based on the genetic algorithm.关键词
电池管理系统/锂电池/荷电状态/CAN总线/遗传算法/蚂蚁算法/BP神经网络Key words
battery management system/ lithium battery/ battery charged state/ CAN bus/ genetic algorithm/ ant algorithm/ BP neural network分类
信息技术与安全科学引用本文复制引用
陈任,邓清勇,邝利丹,李凤姣..基于Blackfin的锂电池管理系统[J].兵工自动化,2011,30(12):44-48,5.基金项目
第三批教育部“大学生创新性实验计划”项目“基于Blackfin DSP的锂电池管理系统设计”(101053028) （101053028）
