dc.contributor.author | Xue, Q | |
dc.contributor.author | Shen, S | |
dc.contributor.author | Li, G | |
dc.contributor.author | Zhang, Y | |
dc.contributor.author | Chen, Z | |
dc.contributor.author | Liu, Y | |
dc.date.accessioned | 2020-12-18T11:58:54Z | |
dc.date.available | 2020-12-18T11:58:54Z | |
dc.date.issued | 2020-01-01 | |
dc.identifier.issn | 0018-9545 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/69427 | |
dc.description.abstract | IEEE Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL prediction based on the capacity estimation and the Box-Cox transformation (BCT). Firstly, the effective aging features (AFs) are extracted from electrical and thermal characteristics of lithium-ion batteries and the variation in terms of the cyclic discharging voltage profiles. The random forest regression (RFR) is then employed to achieve dependable capacity estimation based on only one cells degradation data for model training. Secondly, the BCT is exploited to transform the estimated capacity data and to construct a linear model between the transformed capacities and cycles. Next, the ridge regression algorithm (RRA) is adopted to identify the parameters of the linear model. Finally, the identified linear model based on the BCT is employed to predict the battery RUL, and the prediction uncertainties are investigated and the probability density function (PDF) is calculated through the Monte Carlo (MC) simulation. The experimental results demonstrate that the proposed method can not only estimate capacity with errors of less than 2%, but also accurately predict the battery RUL with the maximum error of 127 cycles and the maximum spans of 95% confidence of 37 cycles in the whole cycle life. | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE Transactions on Vehicular Technology | |
dc.title | Remaining Useful Life Prediction for Lithium-ion Batteries Based on Capacity Estimation and Box-Cox Transformation | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/TVT.2020.3039553 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Hierarchical optimal energy management of electric vehicles::European Commission | en_US |
qmul.funder | Hierarchical optimal energy management of electric vehicles::European Commission | en_US |