李震, 史丽萍, 戴广剑. 矿井移动救生舱中蓄电池健康度预测模型[J]. 煤矿安全, 2012, 43(8): 113-114,115.
    引用本文: 李震, 史丽萍, 戴广剑. 矿井移动救生舱中蓄电池健康度预测模型[J]. 煤矿安全, 2012, 43(8): 113-114,115.
    LI Zhen, SHI Li-Ping, DAI Guang-Jian. Prediction Model of SOH of Battery in Mine Movable Rescue Capsule[J]. Safety in Coal Mines, 2012, 43(8): 113-114,115.
    Citation: LI Zhen, SHI Li-Ping, DAI Guang-Jian. Prediction Model of SOH of Battery in Mine Movable Rescue Capsule[J]. Safety in Coal Mines, 2012, 43(8): 113-114,115.

    矿井移动救生舱中蓄电池健康度预测模型

    Prediction Model of SOH of Battery in Mine Movable Rescue Capsule

    • 摘要: 为了准确地对矿井用可移动式救生舱蓄电池的健康度进行预测,采用Elman神经网络方法对电池健康度预测建立模型,并通过遗传算法对预测模型中的初始权值和阈值进行优化,根据浅度放电的测量数据进行蓄电池健康度的预测。结果表明:模型达到了对蓄电池健康度准确预测的目的,具有较高实用性和良好的应用前景。

       

      Abstract: For predicting the State of Health(SOH)of battery in mine movable rescue capsule precisely,this paper establishes the model to predict the SOH of Lithium Iron Phosphate(LFP) battery by Elman neural network,optimizes its original weights and threshold by Genetic Algorithm(GA),and calculates SOH by shallow discharge testing data.The results show that the model has reached the purpose of accurate prediction,and has high practicability and good application prospects.

       

    /

    返回文章
    返回