Accurate battery model parameter identification using heuristic optimization

• Published in: International Journal of Power Electronics and Drive Systems Vol. 11; no. 1; p. 333
• Main Authors: Jusoh, Mohd Afifi, Daud, Muhamad Zalani
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2020
• Subjects: Accuracy; Algorithms; Computer simulation; Discharge; Genetic algorithms; Heuristic; Human error; Lithium-ion batteries; Mathematical models; Model accuracy; Optimization; Parameter identification; Particle swarm optimization; Rechargeable batteries; Search algorithms
• ISSN: 2088-8694; 2722-256X; 2722-2578; 2722-256X; 2088-8694
• DOI: 10.11591/ijpeds.v11.i1.pp333-341

This paper presents an accurate Lithium-ion battery model representation in Matlab/Simulink. The Tremblay's battery model was used as a BES model platform, where the determination of the model parameters was obtained based on heuristic optimization approach. This approach is simple but more accurate compared to the conventional method. In the classical method, it requires the user to manually select the battery model parameters from relevant points on the manufacturer discharge curves. However, this way of battery parameters extraction normally exposed to the human error and would easily result in an inaccurate selection of battery parameters for the BES simulation studies. Therefore, an easy and accurate approach using heuristic optimization for determining battery model parameters was introduced. The simulation studies utilized three different optimization algorithms for comparison purposes, i.e. 1) Particle Swarm Optimization (PSO), 2) Gravitational Search Algorithm (GSA), and 3) Genetic Algorithm (GA). The performance of BES model discharge accuracy with respect to the test data from three different algorithms was compared and the results showed that the GA approach gives the best results in terms of accuracy and execution time. Finally, the validated results of GA-optimized battery model showed the accuracy of 98% compared to the conventional approach.