基于ACCA-FCM和SVM-RFE的蓄电池SOH特征选择算法

摘要

In the prediction of the lead-acid battery state of health (SOH), the selection of representative feature set based on small sample plays an important role , considering the various factors resulting in the battery aging and the restriction of the battery aging experiment that the full charge and discharge time and the number of samples are limited .Therefore, based on the analysis of battery characteristics , an SOH feature selection algorithm based on unsupervised ACCA-FCM and supervised SVM-RFE is pro-posed.The algorithm, first, utilizes the improved ant colony clustering algorithm (ACCA) to select the effective eigenvalue clus-tering center from the global feature set , which overcame the clustering center sensitivity and local optimal problem of fuzzy C -means clustering algorithm ( FCM ) , and removes the redundant features by the features correlation; second , according to the SVM-RFE feature sorting algorithm, rules out the non-critical interference (Low predictive) features; and finally, obtains the low-dimensional eigenvector with the largest correlation as well as the minimum redundancy of the test result , and avoids the process of complete discharge under the premise of ensuring the accuracy. The SOH model of the battery is verified by the support vector machine (SVM), which has been improved significant and accurate .%由于铅酸蓄电池老化程度受诸多因素影响,且蓄电池老化实验受完全充放电时间和样本数量限制,使得基于小样本的具有代表性的特征集的选择在蓄电池健康状态(SOH)预测中显得尤为重要.因此在对蓄电池进行特性分析的基础上,提出基于无监督的ACCA-FCM和有监督的SVM-RFE相结合的蓄电池SOH特征选择算法.该算法利用改进的蚁群聚类算法(ACCA)从全局特征集中选取有效的特征值聚类中心,克服模糊C均值聚类算法(FCM)聚类中心敏感和局部最优问题,并根据特征之间相关性排除冗余特征;再通过SVM-RFE特征排序算法剔除非关键干扰(低预测性)特征,最终得到与待测结果最大相关最小冗余的低维特征子集,且在保证精度的前提下,避开了完全放电过程.经基于支持向量机(SVM)的蓄电池SOH预测模型验证,放电初期特征构成的最优特征子集可准确预测铅酸蓄电池的健康状态.