Lithium-ion Battery Model Parameter Identification Using Modified Adaptive Forgetting Factor-Based Recursive Least Square Algorithm

A proficient battery management system (BMS) is constantly expected to make an electric vehicle (EV) more dependable. The battery states like state of charge (SOC) and state of health (SOH) estimation are one of the significant functions of BMS. However, the accuracy of the model-based state estimat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International Conference on Power Electronics (Print) S. 2169 - 2174
Hauptverfasser: Shrivastava, Prashant, Soon, Tey Kok, Yamani Bin Idris, Mohd, Mekhilef, Saad
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 24.05.2021
Schlagworte:
Adaptation models
Batteries
Battery Management System
Battery Modeling
Computational modeling
Forgetting Factor
Heuristic algorithms
Lithium-ion Battery
Parameter estimation
Recursive Least Square
State of charge
Temperature
ISSN:2150-6086
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A proficient battery management system (BMS) is constantly expected to make an electric vehicle (EV) more dependable. The battery states like state of charge (SOC) and state of health (SOH) estimation are one of the significant functions of BMS. However, the accuracy of the model-based state estimation strategy is profoundly affected by the exhibition of the battery modeling approach. Particularly, in a continuous application, it is constantly needed to utilize a precise online battery model parameters identification algorithm. In this study, a modified adaptive forgetting factor-based recursive least square (MAFF-RLS) algorithm is proposed. Under which, the forgetting factor values are adaptively updated based on the model voltage error. To implement the proposed algorithm, the first-order RC battery model is utilized. The dynamic load profiles suitable for the EV environment are used for the validation of the proposed algorithm. Besides, to demonstrate the predominance of the MAFF-RLS algorithm over the RLS, and FFRLS algorithms, the estimated voltage errors such as Max AE, MAE ad RMSE are analyzed. The results demonstrated that the value of the estimated voltage RMSEs using the MAFF-RLS algorithm is lesser than of the voltage RMSEs using RLS and FFRLS algorithms.
ISSN:2150-6086
DOI:10.1109/ECCE-Asia49820.2021.9479079