Name
Abstract | ||
|---|---|---|
Prognosis of the remaining battery life is an important and practical research area of rechargeable battery and smart grid. It has promising application prospect in such area as grid energy storage systems, electrical vehicles etc. In this paper, by analysing the lithium-ion battery information, the most influencing factors of lifetime are collected. Based on this, a novel system is proposed to predict the battery capacity loss using a model based on multivariate adaptive regression splines (MARS) method by an iterative technique. Unlike static models the proposed system is designed to overcome the problem of data sparseness at the beginning in application. It implements a reliable forecast of the battery life by using newly gained data iteratively, which increases the prediction accuracy noticeably. Experiments prove that the solution can predict battery life with high precision, and the prediction results meet the accuracy and stability requirements of practical application. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISGT.2014.6816399 | ISGT |
Keywords | Field | DocType |
battery capacity loss,lithium-ion battery information,grid energy storage systems,prediction accuracy,mars,remaining useful life,multivariate adaptive regression splines,iterative technique,battery life prediction,smart remaining battery life prediction,li,reliable forecast,smart power grids,smart grid,energy storage,secondary cells,electrical vehicles,rechargeable battery,electric vehicles,lithium-ion battery,reliability,history,fading | Multivariate adaptive regression splines,Battery capacity,Mars Exploration Program,Smart grid,Simulation,Fading,Grid energy storage,Engineering,Battery (electricity),Reliability engineering | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Xia | 1 | 2 | 1.47 |
| Weida Xu | 2 | 1 | 1.03 |
| XinXin Bai | 3 | 11 | 4.92 |
| Xiaoguang Rui | 4 | 87 | 7.59 |
| Haifeng Wang | 5 | 0 | 0.34 |
| Jan Forster | 6 | 0 | 0.34 |
| Yin-ming Wang | 7 | 2 | 0.76 |
| Xihui Zhao | 8 | 0 | 0.34 |
| Xiangfu Kong | 9 | 0 | 0.34 |
| Tingting Liang | 10 | 0 | 0.34 |