基于改进灰色模型的蓄电池剩余容量预测  

Residual capacity prediction for battery based on improved grey model

作　　者：李立伟[1] 原明亭[1] 包书哲[2] 

机构地区：[1]青岛大学自动化工程学院,山东青岛266071 [2]大连民族学院计算机科学与工程学院,辽宁大连116600

出　　处：《电源技术》2006年第12期1006-1008,共3页Chinese Journal of Power Sources

摘　　要：蓄电池作为直流系统交流停电时的后备电源,其剩余容量直接影响了直流系统的安全运行。在对现有灰色预测模型进行深入研究的基础上,将遗传算法引入到GM(1,1)模型中,对此加以改进,提出了一种新的基于遗传算法的蓄电池剩余容量灰色预测模型。预测实例表明,基于遗传算法的蓄电池剩余容量改进灰色预测模型比传统的GM(1,1)预测模型具有更高的模型精度,能够满足工程需要。该方法可减少传统的电池容量放电实验次数,从而延长了蓄电池的使用寿命。As backup power supply of the DC system, the residual capacity of battery effected safe operation of DC system directly when AC power failure occurs. Genetic algorithm was introduced into the model of GM （1,1） based on many in-depth studies of existing grey forecast model, and GA-based battery residual capacity grey forecast model was presented. Comparing with the traditional GM （1,1） forecast model,the improved GA-based grey forecast model of the battery residual capacity has higher model accuracy, and can satisfy the engineering requirements. This method can reduce the times of the traditional discharge test for battery capacity, thus prolongs the service life of the battery.

关 键 词：直流系统 遗传算法 灰色预测 GM(1 1)模型 

分 类 号：TM912.1[电气工程—电力电子与电力传动]