Performance Comparison of the General, the Dual, and the Joint Sigma Point Kalman Filters on State Estimation of Li-Ion Battery Cells for BMSs
Li-ion batteries, known for high energy and power density, are widely used in electromobility and stationary applications. In these applications a Battery Management System (BMS) ensures safety and longevity by performing functions like cell balancing and protecting against overcharge and over-disch...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/79/1/45 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Li-ion batteries, known for high energy and power density, are widely used in electromobility and stationary applications. In these applications a Battery Management System (BMS) ensures safety and longevity by performing functions like cell balancing and protecting against overcharge and over-discharge. Advanced BMSs estimate the battery’s State of Charge (SOC), crucial for determining remaining operating time and safe range. This study compares three Kalman Filter (KF)-based SOC estimation techniques: the general Sigma Point KF (SPKF), the joint SPKF, and the dual SPKF, for state and parameter estimation of a Samsung 18650INR13L Li-ion battery. |
|---|---|
| ISSN: | 2673-4591 |