基于遗传算法的BP神经网络蓄电池寿命预测研究
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摘要
论文基于电网蓄电池的充放电实验数据,建立了一种基于遗传算法的神经网络模型,实现了蓄电池寿命预测,提升了预测准确度5.97%,分析了蓄电池最佳运行温度和型号差异,指导了蓄电池运维检修和状态监控,有效支撑了电池系统的安全运行监控、健康状态管理。
Based on the data of charging and discharging of the grid,a neural network model based on genetic algorithm is established to realize the battery life prediction and improve the prediction accuracy of 5.97%. The optimal running temperature and model difference of the battery are analyzed. Dimensional maintenance and status monitoring is guided,the safe operation of the battery system monitoring,health management is supported effectively.
Based on the data of charging and discharging of the grid,a neural network model based on genetic algorithm is established to realize the battery life prediction and improve the prediction accuracy of 5.97%. The optimal running temperature and model difference of the battery are analyzed. Dimensional maintenance and status monitoring is guided,the safe operation of the battery system monitoring,health management is supported effectively.
引文
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[15]杨翼.电动运输车蓄电池使用寿命影响因素及使用维护方法[J].工程机械与维修,2016,17(05):66-67.YANG Yi.Influencing factors and maintenance methods of battery life of electric vehicle[J].Construction Machinery and Maintenance,2016,17(05):66-67.
[2]刘大同,周建宝,郭力萌,等.锂离子电池健康评估和寿命预测综述[J].仪器仪表学报,2015,23(01):1-16.LIU Datong,ZHOU Jianbao,GUO Limeng,et al.Lithium-ion battery health assessment and life prediction[J].Instrumentation,2015,23(01):1-16.
[3]吕秀英.阀控式铅酸蓄电池的应用和寿命预测[J].蓄电池,1997,14(03):22-27.LV Xiuying.Application and life prediction of VRLA batteries[J].Storage batteries,1997,14(03):22-27)
[4]沈稼丰,董艳杰,周美兰,等.铅蓄电池的容量测试和寿命预测[J].哈尔滨电工学院学报,1996,6(04):433-436.SHEN Jiafeng,DONG Yanjie,ZHOU Meilan,et al.Study on Capacity Test and Life Prediction of Lead-acid Battery[J].Journal of Harbin Institute of Electric Power,1996,6(04):433-436.
[5]许参,李杰,王超,等.一种锂离子蓄电池寿命的预测模型[J].应用科学学报,2006,18(04):368-371.XU Shen,LI Jie,WANG Chao,et al.A Prediction Model for Lithium-ion Battery Life[J].Journal of Applied Sciences,2006,18(04):368-371.
[6]张金,魏影,韩裕生,等.一种改进的锂离子电池剩余寿命预测算法[J].电子技术应用,2015,33(08):110-116.ZHANG Jin,WEI Ying,HAN Yusheng,et al.An Improved Lifetime Prediction Algorithm for Lithium Ion Batteries[J].Electronic Technology Applications,2015,33(08):110-116.
[7]梅成林,王文强,刘波峰,等.小波神经网络预测变电站VRLA电池工作寿命[J].电池,2014,24(06):351-354.MEI Chenglin,WANG Wenqiang,LIU Bofeng,et al.Prediction of Working Life of VRLA Battery in Substation Based on Wavelet Neural Network[J].Battery,2014,24(06):351-354.
[8]史丽萍,龚海霞,李震,等.基于BP神经网络的电池SOC估算[J].电源技术,2013,2(09):1539-1541.SHI Liping,GONG Haixia,LI Zhen,et al.Battery SOCEstimation Based on BP Neural Network[J].Power Technology,2013,2(09):1539-1541.
[9]张金国,王小君,朱洁,等.基于MIV的BP神经网络磷酸铁锂电池寿命预测[J].电源技术,2016,11(01):50-58.ZHANG Jinguo,WANG Xiaojun,ZHU Jie,et al.Based on MIV BP neural network lithium iron phosphate battery life prediction[J].Power Technology,2016,11(01):50-58.
[10]王莉,杨永辉,詹益,等.基于最小二乘支持向量机阀控式铅酸蓄电池寿命预测[J].大连交通大学学报,2017,21(03):116-179.WANG Li,YANG Yonghui,ZHAN Yi,et al.Based on Least Squares Support Vector Machine[J].Journal of Dalian Jiaotong University,2017,21(03):116-179.
[11]范红军,陈友龙,李勋章,等.灰色系统在蓄电池健康管理中的应用研究[J].蓄电池,2014,5(04):176-188.FAN Hongjun,CHEN Youlong,LI Xunzhang,et al.Application of Gray System in Battery Health Management[J].Battery,2014,5(04):176-188.
[12]李立伟,原明亭,包书哲,等.基于改进灰色模型的蓄电池剩余容量预测[J].电源技术,2006,8(12):1006-1018.LI Liwei,YUAN Mingting,BAO Shuzhe,et al.Period capacity prediction of battery based on improved gray model[J].Power Technology,2006,8(12):1006-1018.
[13]刘丹烨,张玉朋.基于粒子滤波的锂离子电池寿命预测[J].科技创新与应用,2015,28(21):15-24.LIU Danye,ZHANG Yupeng.Life life prediction of lithium ion battery based on particle filter[J].Science and Technology Innovation and Application,2015,28(21):15-24.
[14]马兹林,冒晓建,王俊席,等.修正RC模型混合动力车用氢镍蓄电池SOC预测[J].电源技术,2012,3(07):966-978.MA Jianlin,SHANG Xiaojian,WANG Junshi,et al.Prediction of SOC Prediction of Hydrogen-nickel Battery for RC Model Hybrid Electric Vehicle[J].Power Technology,2012,3(07):966-978.
[15]杨翼.电动运输车蓄电池使用寿命影响因素及使用维护方法[J].工程机械与维修,2016,17(05):66-67.YANG Yi.Influencing factors and maintenance methods of battery life of electric vehicle[J].Construction Machinery and Maintenance,2016,17(05):66-67.