摘要
健康状态(SOH)预测作为锂离子电池管理系统(BMS)的关键功能之一,对于保证电池安全可靠运行、降低电池系统维护成本具有重要意义。为了提高锂电池SOH预测精度,提出一种基于改进的蚁狮优化算法和支持向量回归(IALO-SVR)的SOH预测方法,首先从电池充电数据中提取与电池容量相关的特征因子并进行相关性分析,选取相关度高的3个作为模型特征输入,再导入样本数据,通过改进的蚁狮优化算法(IALO)对SVR模型的关键参数进行寻优,建立最终预测模型。在NASA公开数据集上与现有的遗传算法-支持向量回归(GA-SVR)和改进粒子群算法-支持向量回归(IPSO-SVR)进行对比实验,结果表明IALO-SVR方法拥有更高的预测精度与拟合度,预测误差基本保持在1%以内,验证了预测方法的可行性。
State of health(SOH)prediction,as one of the key functions of lithium ion battery management system(BMS),is of great significance to ensure the safe and reliable operation of batteries and reduce the maintenance cost of battery system.In order to improve the prediction accuracy of lithium battery SOH,a SOH prediction method based on improved ant-lion optimization algorithm and support vector regression(IALO-SVR)is proposed.Firstly,the characteristic factors related to battery capacity are extracted from the battery charging data,and the correlation analysis is carried out.The three features with high correlation are selected as the model feature inputs,and then the sample data is imported.The key parameters of SVR model are optimized by the IALO algorithm,and the final prediction model is established.Compared with the existing GA-SVR and IPSO-SVR,the results show that IALO-SVR method NASA has higher prediction accuracy and fitting degree,and the prediction error is basically kept within 1%,which verifies the feasibility of the prediction method.
作者
李强龙
孙建瑞
赵坤
王凯
Li Qianglong;Sun Jianrui;Zhao Kun;Wang Kai(College of Electrical Engineering,Qingdao University,Qingdao 266071,China;Shandong Guangyu Technology,Dongying 257000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第1期204-211,共8页
Journal of Electronic Measurement and Instrumentation
基金
山东省自然科学基金(ZR2020QE212)
山东省自然科学基金重点项目(ZR2020KF020)
青岛大学2020年创新型教学实验室研究项目(CXSYYB202003)资助
关键词
锂离子电池
健康状态
改进的蚁狮优化算法
支持向量回归
lithium-ion battery
state of health
improved antlion optimization algorithm
support vector regression
