TY - GEN
T1 - Remaining useful life estimation of lithium-ion battery using exemplar-based conditional particle filter
AU - Liu, Zhenbao
AU - Sun, Gaoyuan
AU - Bu, Shuhui
AU - Zhang, Chao
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - Since lithium-ion batteries have been used in a wide range of fields, such as transportation industry, household appliances, and national defence industry. In order to avoid the unnecessary loss resulting from its sudden failure, it is necessary to timely predict the remaining useful life (RUL) of lithium-ion battery. In this paper, we present a novel remaining useful life estimation method for lithium-ion batteries, which depends on exemplar-based conditional particle filter (EC-PF). Differently from traditional particle filter, in the update phase, exemplar-based conditional particle filter combines historical data of multiple batteries with filtering stage of a single battery to compute the weights with respect to particles. This method can make the weights of particles more accurate, which results in improving the prediction accuracy. To verify the effectiveness and efficiency of the proposed method, a public data set is selected for validating prediction accuracy of RUL of battery. The results show that the proposed method improves the performance of the traditional particle filter method.
AB - Since lithium-ion batteries have been used in a wide range of fields, such as transportation industry, household appliances, and national defence industry. In order to avoid the unnecessary loss resulting from its sudden failure, it is necessary to timely predict the remaining useful life (RUL) of lithium-ion battery. In this paper, we present a novel remaining useful life estimation method for lithium-ion batteries, which depends on exemplar-based conditional particle filter (EC-PF). Differently from traditional particle filter, in the update phase, exemplar-based conditional particle filter combines historical data of multiple batteries with filtering stage of a single battery to compute the weights with respect to particles. This method can make the weights of particles more accurate, which results in improving the prediction accuracy. To verify the effectiveness and efficiency of the proposed method, a public data set is selected for validating prediction accuracy of RUL of battery. The results show that the proposed method improves the performance of the traditional particle filter method.
KW - historical data exemplars
KW - lithium-ion battery
KW - remaining useful life estimation
KW - weight computation
UR - http://www.scopus.com/inward/record.url?scp=84957899467&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2015.7245046
DO - 10.1109/ICPHM.2015.7245046
M3 - 会议稿件
AN - SCOPUS:84957899467
T3 - 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015
BT - 2015 IEEE Conference on Prognostics and Health Management
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Prognostics and Health Management, PHM 2015
Y2 - 22 June 2015 through 25 June 2015
ER -
