流形正则化框架下的极限学习机预测锂电池SOC方法
|
摘要
为提高锂电池荷电状态建模预测的精度及泛化能力,提出一种流行正则化框架下的极限学习机建模预测方法。首先,为了解决极限学习机建立预测模型存在过拟合泛化能力弱的问题,以流形假设为依据,在数据输入空间构建图拉普拉斯算子,在其框架内求解极限学习机隐层和输出层之间的权重,达到正则优化目的。其次,针对正则化参数难以选择的问题,提出将差分进化算法融入基于流形正则化框架的极限学习机中以优化其正则化参数。最后,利用采集到的锂电池数据进行了实验验证。结果表明:该方法建立的预测模型预测锂电池SOC精度高,泛化能力强,为锂电池SOC的预测建模提供一种新方法。
In order to improve the accuracy and generalization ability of modeling and prediction for the SOC of lithium batteries,an ELM modeling and prediction method based on MR framework was proposed. Firstly,in order to solve the problem of over-fitting and weak generalization ability in the prediction model of the ELM,based on the manifold hypothesis,a graph-Laplacian operator is constructed in the data input space,and the weights between the hidden layer and the output layer of the ELM are solved within the framework of the operator to achieve the goal of regular optimization. Secondly,in order to optimize the regularization parameters,a DE algorithm is introduced into the ELM based on MR framework. Finally,the data collected from lithium battery were used for experimental verification. The experimental results show that the prediction model established by this method has high accuracy and strong generalization ability for lithium battery SOC prediction,which provides a new method for lithium battery SOC prediction modeling.
In order to improve the accuracy and generalization ability of modeling and prediction for the SOC of lithium batteries,an ELM modeling and prediction method based on MR framework was proposed. Firstly,in order to solve the problem of over-fitting and weak generalization ability in the prediction model of the ELM,based on the manifold hypothesis,a graph-Laplacian operator is constructed in the data input space,and the weights between the hidden layer and the output layer of the ELM are solved within the framework of the operator to achieve the goal of regular optimization. Secondly,in order to optimize the regularization parameters,a DE algorithm is introduced into the ELM based on MR framework. Finally,the data collected from lithium battery were used for experimental verification. The experimental results show that the prediction model established by this method has high accuracy and strong generalization ability for lithium battery SOC prediction,which provides a new method for lithium battery SOC prediction modeling.
引文
[1]宋绍剑,王志浩,林小峰.基于极限学习机的磷酸铁锂电池SOC估算研究[J].电源技术,2018,42(6):806-808,881.
[2]姜长泓,王闯.基于人群搜索的极限学习机荷电状态估计[J].长春工业大学学报,2017,38(5):468-471.
[3]罗宏远,王德运,潘雯雯,等.基于DE-ELM的电池SOC预测研究[J].数学的实践与认识,2016,46(22):236-243.
[4]陈福光,刘溪,李锐,等.基于改进极限学习机的锂离子电池SOC预测[J].移动电源与车辆,2016(1):18-22.
[5]王琪,孙玉坤,倪福银,等.一种混合动力电动汽车电池荷电状态预测的新方法[J].电工技术学报,2016,31(9):189-196.
[6]王琪,孙玉坤,倪福银,等.混合动力汽车电池内部状态预测的贝叶斯极限学习机方法[J].中国机械工程,2016(22):3118-3123.
[7]孙玉坤,李曼曼,黄永红.基于改进在线核极限学习机的蓄电池SOC预测[J].系统仿真学报,2018,30(3):969-975.
[8] Feng X,Liang Y, Shi X, et al. Overfitting reduction of text classification based on AdaBELM[J]. Entropy, 2017, 19(7):330.
[9]邓万宇,郑庆华,陈琳,等.神经网络极速学习方法研究[J].计算机学报,2010,33(2):279-287.
[10]徐嘉明,张卫强,杨登舟,等.基于流形正则化极限学习机的语种识别系统[J].自动化学报,2015,41(9):1680-1685.
[11] Guan N,Tao D,Luo Z,et al. Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent[J]. IEEE Transactions on Image Processing,2011,20(7):2030-2048.
[12]朱顺涛,卢先领,于丹石.一种基于流形正则化的半监督指纹定位算法[J].测绘通报,2018(2):11-15,20.
[13]赵立杰,王海龙,陈斌.基于流形正则化半监督学习的污水处理操作工况识别方法[J].化工学报,2016,67(6):2462-2468.
[14]王萍,王迪,冯伟.基于流形正则化的在线半监督极限学习机[J].上海交通大学学报(自然科学版),2015,49(8):1153-1158.
[15]王璐.电动汽车锂电池剩余电量估计方法的研究[D].北京:北京交通大学,2011.
[2]姜长泓,王闯.基于人群搜索的极限学习机荷电状态估计[J].长春工业大学学报,2017,38(5):468-471.
[3]罗宏远,王德运,潘雯雯,等.基于DE-ELM的电池SOC预测研究[J].数学的实践与认识,2016,46(22):236-243.
[4]陈福光,刘溪,李锐,等.基于改进极限学习机的锂离子电池SOC预测[J].移动电源与车辆,2016(1):18-22.
[5]王琪,孙玉坤,倪福银,等.一种混合动力电动汽车电池荷电状态预测的新方法[J].电工技术学报,2016,31(9):189-196.
[6]王琪,孙玉坤,倪福银,等.混合动力汽车电池内部状态预测的贝叶斯极限学习机方法[J].中国机械工程,2016(22):3118-3123.
[7]孙玉坤,李曼曼,黄永红.基于改进在线核极限学习机的蓄电池SOC预测[J].系统仿真学报,2018,30(3):969-975.
[8] Feng X,Liang Y, Shi X, et al. Overfitting reduction of text classification based on AdaBELM[J]. Entropy, 2017, 19(7):330.
[9]邓万宇,郑庆华,陈琳,等.神经网络极速学习方法研究[J].计算机学报,2010,33(2):279-287.
[10]徐嘉明,张卫强,杨登舟,等.基于流形正则化极限学习机的语种识别系统[J].自动化学报,2015,41(9):1680-1685.
[11] Guan N,Tao D,Luo Z,et al. Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent[J]. IEEE Transactions on Image Processing,2011,20(7):2030-2048.
[12]朱顺涛,卢先领,于丹石.一种基于流形正则化的半监督指纹定位算法[J].测绘通报,2018(2):11-15,20.
[13]赵立杰,王海龙,陈斌.基于流形正则化半监督学习的污水处理操作工况识别方法[J].化工学报,2016,67(6):2462-2468.
[14]王萍,王迪,冯伟.基于流形正则化的在线半监督极限学习机[J].上海交通大学学报(自然科学版),2015,49(8):1153-1158.
[15]王璐.电动汽车锂电池剩余电量估计方法的研究[D].北京:北京交通大学,2011.