The Library
Two layer Markov model for prediction of future load and end of discharge time of batteries
Tools
Tools
Tools
Faraji Niri, Mona, Marco, James, Dinh, Quang Truong and Yu, Tung Fai (2019) Two layer Markov model for prediction of future load and end of discharge time of batteries. In: International Conference on Mechatronics Technology , Salerno, Italy, 23-26 Dec 2019
|
PDF
WRAP-two-layer-Markov-model-future-load-batteries-Marco-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2002Kb) | Preview |
Abstract
To predict the remaining discharge energy of a battery, it is significant to have an accurate prediction of its end of discharge time (EoDT). In recent studies, the EoDT is predicted by assuming that the battery load profile (current or power) is a priori known. However, in real-world applications future load on a battery is typically unknown with high dynamics and transients. Therefore, predicting battery EoDT in an online manner can be very challenging. The purpose of this paper is to derive a load prediction method for capturing historical charge/discharge behaviour of a battery to generate the most probable future usage of it, enabling an accurate EoDT prediction. This method is based on a two layer Markov model for the load extrapolation and iterative model-based estimation. To develop the proposed concept, lithium-ion batteries are selected and the numerical simulation results show an improvement in terms of the accuracy of the EoDT prediction compared to methods presented in the literature.
| Item Type: | Conference Item (Paper) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QC Physics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Markov processes | ||||||||||||
| Publisher: | IEEE | ||||||||||||
| Official Date: | 15 August 2019 | ||||||||||||
| Dates: |
|
||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2019 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. | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| RIOXX Funder/Project Grant: |
|
||||||||||||
| Conference Paper Type: | Paper | ||||||||||||
| Title of Event: | International Conference on Mechatronics Technology | ||||||||||||
| Type of Event: | Conference | ||||||||||||
| Location of Event: | Salerno, Italy | ||||||||||||
| Date(s) of Event: | 23-26 Dec 2019 | ||||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
