Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound
Document Type
Article
Publication Date
11-1-2022
Publication Title
Energies
Volume
15
Issue
22
Keywords
battery equivalent circuit model, battery internal resistance, battery management system, Cramer–Rao lower bound, least squares estimation
Abstract
Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery.
DOI
10.3390/en15228441
E-ISSN
19961073
Recommended Citation
Pillai, Prarthana; Sundaresan, Sneha; Pattipati, Krishna R.; and Balasingam, Balakumar. (2022). Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound. Energies, 15 (22).
https://scholar.uwindsor.ca/computersciencepub/90