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刘柱,姜媛媛,罗慧,周利华.基于最优权阈值ELM算法的锂离子电池RUL预测[J].电源学报,2018,16(4):168-173
基于最优权阈值ELM算法的锂离子电池RUL预测
Prediction of Lithium-ion Battery RUL Based on Optimal Weight and Threshold Using ELM Algorithm
投稿时间:2016-04-29  修订日期:2018-03-30
DOI:10.13234/j.issn.2095-2805.2018.4.168
中文关键词:  极限学习机(ELM)  锂离子电池  遗传蚂蚁算法(GAAA)
英文关键词:extreme learning machine(ELM)  lithium-ion battery  genetic algorithm-ant algorithm(GAAA)
基金项目:国家自然科学基金资助项目(51604011);安徽省自然科学基金资助项目(1708085QF135);安徽省高校自然科学研究资助项目(KJ2017A077);安徽省高校优秀青年骨干人才国外访学研修资助项目(gxfx2017025)
作者单位E-mail
刘柱 安徽理工大学电气信息与工程学院, 淮南 232001  
姜媛媛 安徽理工大学电气信息与工程学院, 淮南 232001
南京航空航天大学自动化学院, 南京 210016 
jyyll672@163.com 
罗慧 南京农业大学工学院, 南京 210031  
周利华 安徽理工大学电气信息与工程学院, 淮南 232001  
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中文摘要:
      针对锂离子电池剩余使用寿命RUL(remaining useful life)预测结果不准确及极限学习机ELM(extre-me learning machine)权阈值随机选取等问题,提出利用ELM模型间接预测锂离子电池RUL的方法,并利用遗传蚂蚁算法GAAA(genetic algorithm ant algorithm)选取ELM的最优权值与阈值,建立基于等压降放电时间间接寿命特征参数的最优GAAA-ELM锂离子电池RUL预测模型。基于NASA锂离子电池数据集预测和评估锂离子电池的RUL,并与BP模型预测方法、ELM模型预测方法和GA-ELM模型预测方法相比较,结果表明该方法能够更准确有效地实现锂离子电池RUL预测。
英文摘要:
      Since the predictions of lithium-ion battery remaining useful life(RUL) are inaccurate and the selection of weights and thresholds for an extreme learning machine(ELM) is random, an indirect prediction method for lithium-ion ba-ttery RUL is proposed based on ELM model. Moreover, the optimal weight and threshold of ELM are selected using genetic algorithm-ant algorithm(GAAA), and the prediction model for lithium-ion battery RUL using GAAA-ELM is established based on the time interval to equal discharging voltage which is a kind of indirect life feature character. Finally, the lithium-ion battery RUL is predicted and assessed based on NASA data sets of lithium-ion battery, which is further compared with that obtained using BP, ELM, and GA-ELM prediction model methods, showing that the proposed method can accurately and effectively predict the lithium-ion battery RUL.
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