Name
Title | ||
|---|---|---|
Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis. |
Abstract | ||
|---|---|---|
Lithium ion batteries are attracting significant and growing interest, because their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles. In this brief, a data-driven polynomial nonlinear state-space model is proposed for the operating points at the cusp of linear and nonlinear regimes of the battery's electrical operation, based on the thorough nonparametric frequency domain characterization and quantification of the battery's behavior in terms of its linear and nonlinear behavior at different levels of the state of charge. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCST.2016.2616380 | IEEE Transactions on Control Systems Technology |
Keywords | DocType | Volume |
Batteries,Computational modeling,Mathematical model,Analytical models,Integrated circuit modeling,Hidden Markov models,Frequency-domain analysis | Journal | abs/1805.06702 |
Issue | ISSN | Citations |
5 | IEEE Transactions on Control Systems Technology, Volume: 25,
Issue: 5, Sept. 2017 | 2 |
PageRank | References | Authors |
0.39 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rishi Relan | 1 | 5 | 1.28 |
| Yousef Firouz | 2 | 2 | 0.39 |
| Jean-Marc Timmermans | 3 | 2 | 0.73 |
| Johan Schoukens | 4 | 376 | 58.12 |