Authors:
Ayse Cisel Aras
and
Emre Yonel
Affiliation:
AVL Research and Engineering, Turkey
Keyword(s):
Electrical Battery Model, Parameter Identification, DC-IR Data, Curve Fitting, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
System Identification
;
System Modeling
Abstract:
Parameter identification of an electrical battery model is significant for the analysis of the performance of a battery. In order to obtain an accurate electrical battery model, a series of cell characterization tests should be conducted which will take a considerable amount of time. In this study, in order to identify the parameters of the electrical battery model in a short amount of time with an acceptable accuracy, DC-IR data is used. DC-IR test will take less time compared to the cell characterization tests. For the parameter identification, one of the most commonly used evolutionary algorithm (EA), Genetic Algorithm (GA) is used for the curve fitting problem and its performance is compared with the Levenberg-Marquardt algorithm.