基于Blackfin的锂电池管理系统

Lithium Battery Management System Based on Blackfin

针对目前锂电池管理系统的荷电状态(SOC)估算精度低、可扩展性差的问题,设计基于Blackfin数字信号处理器的电池管理系统。该系统实现了锂电池数据实时监测、剩余电量估计、通过CAN总线通信扩展多组锂电池、锂电池危险状态报警和自动保护等功能。在剩余电量估计算法上,提出一种遗传算法与蚂蚁算法相结合的GAAA算法优化BP神经网络的方法。实验结果表明:该算法比基于遗传算法的BP神经网络具有更高的SOC估算精度和更快的运算速度。

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.