Performance Comparison of the General, the Dual, and the Joint Sigma Point Kalman Filters on State Estimation of Li-Ion Battery Cells for BMSs
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Tamás Horváth, Dénes Fodor
Institution: Széchenyi István University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404108454