Towards State-of-Charge Estimation for Battery Packs: Reducing Computational Complexity by Optimising Model Sampling Time and Update Frequency of the Extended Kalman Filter

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Downloads (Pure)

Abstract

Accurate State-of-Charge (SoC) estimation remains a challenge for large battery packs. This paper aims to reduce the computational complexity of single-cell estimation, which already achieves satisfactory performance, such that it can be more easily scaled to large arrays of cells inside battery packs. This is done by experimenting with a range of sampling times for the models used in an Extended Kalman Filter (EKF) and by adjusting the update frequency of this estimator. The EKF is tested with linear time-invariant and linear parameter-varying models and also in joint state-parameter estimation form. Results show that adjusting the sampling time and update frequency can result in a significant reduction of computational complexity, around a factor of 148, while only suffering a minor increase in SoC estimation error. This means that a relatively small micro-controller can be employed to estimate the SoC of an entire battery pack.
Original languageEnglish
Title of host publication2021 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers
Pages3120-3125
Number of pages6
ISBN (Electronic)978-1-6654-4197-1
DOIs
Publication statusPublished - 28 Jul 2021
Event2021 American Control Conference, ACC 2021 - Virtual, Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021
http://acc2021.a2c2.org/

Conference

Conference2021 American Control Conference, ACC 2021
Abbreviated titleACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Period25/05/2128/05/21
Internet address

Fingerprint

Dive into the research topics of 'Towards State-of-Charge Estimation for Battery Packs: Reducing Computational Complexity by Optimising Model Sampling Time and Update Frequency of the Extended Kalman Filter'. Together they form a unique fingerprint.

Cite this