基于BP神经网络的锂电池SOC在线精确估算
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摘要
文中以4节12 V的串联锂离子电池组模块为研究对象,通过实验采集动力电池充放电时的电压、电流、温度、内阻和放电量数据来估算电池的荷电状态(State Of Charge,SOC),重点考虑内阻对动力电池SOC预测结果的影响。以动力电池的电压、电流、温度和内阻作为输入,SOC作为输出,建立四输入一输出的神经网络仿真模型。实验结果表明SOC的预测精度为1.6%,比未考虑电池内阻的预测精度提高45%左右。本文提出的预测方法,其运行时间为0.27 s左右,比不考虑电池内阻时稍有延长,但完全能满足不同工况动力电池充放电时SOC在线估算的速度要求,从而能实现SOC的在线准确预测。
In this paper,the 4 section 12 V series lithium ion battery module is taken as the research object. The voltage,current,temperature,internal resistance and discharge data are collected to estimate the charge state of the battery(State Of Charge,SOC)by experiment. The influence of internal resistance on the SOC prediction results of the power cell is mainly considered. Taking the voltage,current,temperature and internal resistance of the power battery as input and SOC as output,a neural network simulation model with four inputs and one output is established. The results show that the prediction accuracy of SOC is 1.6%,which is 45% higher than that without considering the internal resistance of battery. The prediction method proposed in this paper has a running time of about 0.27 s,which is a little longer than that without the internal resistance of the battery,but it can fully meet the speed requirements of the SOC on-line estimation of the battery charging and discharging in different working conditions,thus the accurate online prediction of SOC can be realized.
In this paper,the 4 section 12 V series lithium ion battery module is taken as the research object. The voltage,current,temperature,internal resistance and discharge data are collected to estimate the charge state of the battery(State Of Charge,SOC)by experiment. The influence of internal resistance on the SOC prediction results of the power cell is mainly considered. Taking the voltage,current,temperature and internal resistance of the power battery as input and SOC as output,a neural network simulation model with four inputs and one output is established. The results show that the prediction accuracy of SOC is 1.6%,which is 45% higher than that without considering the internal resistance of battery. The prediction method proposed in this paper has a running time of about 0.27 s,which is a little longer than that without the internal resistance of the battery,but it can fully meet the speed requirements of the SOC on-line estimation of the battery charging and discharging in different working conditions,thus the accurate online prediction of SOC can be realized.
引文
[1]冯飞,宋凯,逯仁贵,等.磷酸铁锂电池组均衡控制策略及荷电状态估计算法[J].电工技术学报,2015,30(1):22-29.
[2]杨海学,张继业,张晗.基于改进Sage-Husa的自适应无迹卡尔曼滤波的锂离子电池SOC估计[J].电工电能新技术,2016,35(1):30-35.
[3]于海芳,逯仁贵,朱春波,等.基于安时法的镍氢电池SOC估计误差校正[J].电工技术学报,2012,27(6):12-18.
[4]杨文荣,朱赛飞,陈阳,等.基于改进安时积分法估计锂离子电池组SOC[J].电源技术,2018,42(2):183-184,246.
[5]李哲,卢兰光,欧阳明高.提高安时积分法估算电池SOC精度的方法比较[J].清华大学学报:自然科学版,2010,50(8):1293-1296.
[6]邓晔,胡越黎,滕华强.锂电池开路电压的预估及SOC估算[J].仪表技术,2015(2):21-24.
[7]张利,朱雅俊,刘征宇.锂离子电池SOC与模型参数联合估算研究[J].电子测量与仪器学报,2012,26(4):320-324.
[8]刘艳莉,戴胜,程泽,等.基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J].电工技术学报,2014,29(1):221-228.
[9]荣雅君,杨伟,牛欢,等.基于BP-EKF算法的电动汽车电池管理系统SOC精准估计[J].电工电能新技术,2015,34(9):22-28.
[10]朱晓青,马定寰,李圣清,等.基于BP神经网络的微电网蓄电池荷电状态估计[J].电子测量与仪器学报,2017,31(12):2042-2048.
[11]魏克新,陈峭岩.基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J].中国电机工程学报,2014,34(3):445-452.
[12]赵轩,康留旺,汪贵平,等.基于BP神经网络的SOC估计及铅酸蓄电池特性[J].电源技术,2014,38(5):874-877.
[13]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,2013,37(9):1539-1541.
[14]尹安东,张万兴,赵韩,等.基于神经网络的磷酸铁锂电池SOC预测研究[J].电子测量与仪器学报,2011,25(5):433-437.
[15]王文生,滕青芳,刘特.电动车蓄电池SOC估计的神经网络方法[J].计算机与应用化学,2015,32(6):744-748.
[16]赵钢,孙豪赛,罗淑贞.基于BP神经网络的动力电池SOC估算[J].电源技术,2016,40(4):818-819.
[2]杨海学,张继业,张晗.基于改进Sage-Husa的自适应无迹卡尔曼滤波的锂离子电池SOC估计[J].电工电能新技术,2016,35(1):30-35.
[3]于海芳,逯仁贵,朱春波,等.基于安时法的镍氢电池SOC估计误差校正[J].电工技术学报,2012,27(6):12-18.
[4]杨文荣,朱赛飞,陈阳,等.基于改进安时积分法估计锂离子电池组SOC[J].电源技术,2018,42(2):183-184,246.
[5]李哲,卢兰光,欧阳明高.提高安时积分法估算电池SOC精度的方法比较[J].清华大学学报:自然科学版,2010,50(8):1293-1296.
[6]邓晔,胡越黎,滕华强.锂电池开路电压的预估及SOC估算[J].仪表技术,2015(2):21-24.
[7]张利,朱雅俊,刘征宇.锂离子电池SOC与模型参数联合估算研究[J].电子测量与仪器学报,2012,26(4):320-324.
[8]刘艳莉,戴胜,程泽,等.基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J].电工技术学报,2014,29(1):221-228.
[9]荣雅君,杨伟,牛欢,等.基于BP-EKF算法的电动汽车电池管理系统SOC精准估计[J].电工电能新技术,2015,34(9):22-28.
[10]朱晓青,马定寰,李圣清,等.基于BP神经网络的微电网蓄电池荷电状态估计[J].电子测量与仪器学报,2017,31(12):2042-2048.
[11]魏克新,陈峭岩.基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J].中国电机工程学报,2014,34(3):445-452.
[12]赵轩,康留旺,汪贵平,等.基于BP神经网络的SOC估计及铅酸蓄电池特性[J].电源技术,2014,38(5):874-877.
[13]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,2013,37(9):1539-1541.
[14]尹安东,张万兴,赵韩,等.基于神经网络的磷酸铁锂电池SOC预测研究[J].电子测量与仪器学报,2011,25(5):433-437.
[15]王文生,滕青芳,刘特.电动车蓄电池SOC估计的神经网络方法[J].计算机与应用化学,2015,32(6):744-748.
[16]赵钢,孙豪赛,罗淑贞.基于BP神经网络的动力电池SOC估算[J].电源技术,2016,40(4):818-819.