联合精确估算电池的健康状态
|
摘要
以动力电池的电压、电流、温度和内阻作为输入,荷电状态作为输出,建立四输入一输出的神经网络仿真模型预测电池的荷电状态。再以荷电状态为基础,改进电池健康状态的估算方法,分别利用改进型容量法、改进型内阻法和电压法3种方法分别估算出电池的健康状态,并利用遗传神经网算法建立了3种方法联合在一起的电池健康状态估算模型。以4节12 V的串联锂离子电池组模块为研究对象分别进行了Simulink仿真和实验研究,通过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,测试了电池的荷电状态和健康状态。实验结果表明电池荷电状态的预测精度为1.6%,仿真模型运行和实验结果显示联合法估算健康状态的最大误差为1.5%,高于其他3种单独的方法。本文提出的健康状态预测方法,省略了传统神经网络算法估算健康状态寻找健康因子的复杂步骤,同时也避免现有电池的健康状态估计单一参量判定方法的局限性。
Taking the voltage, current, temperature and internal resistance of the power battery as input and the state of charge as output, a neural network simulation model with four inputs and one output was established to predict the state of charge of the battery. Then, based on the state of charge, the health state of the battery is estimated by the improved capacity method, the improved internal resistance method and the voltage method, respectively. The health state of the battery is estimated by the genetic neural network algorithm. By Simulink simulation and experimental study were carried out on four 12 V series lithium ion battery packs. The charge state and healthy state of the battery were tested by collecting voltage, current, temperature, internal resistance and discharge quantity data during charging and discharging. The experimental results show that the prediction accuracy of the battery state is 1.6%. The results of simulation and experiment show that the maximum error of the combined method is 1.5%, which is higher than the other three methods. In this paper, the health state prediction method is proposed, which omits the complex steps of finding health factors by traditional neural network algorithm, and avoids the limitation of single parameter judgment method for battery health state estimation.
Taking the voltage, current, temperature and internal resistance of the power battery as input and the state of charge as output, a neural network simulation model with four inputs and one output was established to predict the state of charge of the battery. Then, based on the state of charge, the health state of the battery is estimated by the improved capacity method, the improved internal resistance method and the voltage method, respectively. The health state of the battery is estimated by the genetic neural network algorithm. By Simulink simulation and experimental study were carried out on four 12 V series lithium ion battery packs. The charge state and healthy state of the battery were tested by collecting voltage, current, temperature, internal resistance and discharge quantity data during charging and discharging. The experimental results show that the prediction accuracy of the battery state is 1.6%. The results of simulation and experiment show that the maximum error of the combined method is 1.5%, which is higher than the other three methods. In this paper, the health state prediction method is proposed, which omits the complex steps of finding health factors by traditional neural network algorithm, and avoids the limitation of single parameter judgment method for battery health state estimation.
引文
[1] 姚芳,田家益,黄凯.锂电池组健康状态计算方法综述[J].电源技术,2018,42(1):135-138.
[2] 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.
[3] 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.
[4] 刘苗. 车载锂离子动力电池荷电状态与健康状态估计研究[D].济南:山东大学,2017.
[5] 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.
[6] 程泽,杨磊,孙幸勉.基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J].中国电机工程学报,2018,38(8):2384-2393,2548.
[7] 颜湘武,郭琪,杨漾,等.动力电池组健康状态评价方法的研究[J].湖南大学学报(自然科学版),2015,42(2):93-99.
[8] 吴盛军,袁晓冬,徐青山,等.锂电池健康状态评估综述[J].电源技术,2017,41(12):1788-1791.
[9] 宾洋,于静美,朱英凯,等.实时雨流计数法及其在钴酸锂电池健康状态建模中的应用[J].中国电机工程学报,2017,37(12):3627-3635,3692.
[10] 董汉成,凌明祥,王常虹,等.锂离子电池健康状态估计与剩余寿命预测[J].北京理工大学学报,2015,35(10):1074-1078.
[11] 钟国彬,刘新天,何耀,等.变电站用铅酸电池SOH估计[J].电源技术, 2016,40(12):2407-2410.
[12] 孙冬,许爽.梯次利用锂电池健康状态预测[J].电工技术学报,2018,33(9):2121-2129.
[13] 刘丽娜.基于遗传优化BP神经网络算法的土壤含水量反演研究[D].成都:电子科技大学,2011.
[14] 刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(7):1573-1580.
[15] 孙一睿,李钰鑫,陈磊,等.基于遗传算法优化神经网络的SCR催化剂失效预测[J].中国电机工程学报,2016,36(S1):112-120.
[2] 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.
[3] 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.
[4] 刘苗. 车载锂离子动力电池荷电状态与健康状态估计研究[D].济南:山东大学,2017.
[5] 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.
[6] 程泽,杨磊,孙幸勉.基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J].中国电机工程学报,2018,38(8):2384-2393,2548.
[7] 颜湘武,郭琪,杨漾,等.动力电池组健康状态评价方法的研究[J].湖南大学学报(自然科学版),2015,42(2):93-99.
[8] 吴盛军,袁晓冬,徐青山,等.锂电池健康状态评估综述[J].电源技术,2017,41(12):1788-1791.
[9] 宾洋,于静美,朱英凯,等.实时雨流计数法及其在钴酸锂电池健康状态建模中的应用[J].中国电机工程学报,2017,37(12):3627-3635,3692.
[10] 董汉成,凌明祥,王常虹,等.锂离子电池健康状态估计与剩余寿命预测[J].北京理工大学学报,2015,35(10):1074-1078.
[11] 钟国彬,刘新天,何耀,等.变电站用铅酸电池SOH估计[J].电源技术, 2016,40(12):2407-2410.
[12] 孙冬,许爽.梯次利用锂电池健康状态预测[J].电工技术学报,2018,33(9):2121-2129.
[13] 刘丽娜.基于遗传优化BP神经网络算法的土壤含水量反演研究[D].成都:电子科技大学,2011.
[14] 刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(7):1573-1580.
[15] 孙一睿,李钰鑫,陈磊,等.基于遗传算法优化神经网络的SCR催化剂失效预测[J].中国电机工程学报,2016,36(S1):112-120.