مهدي بحريني

Today, rechargeable batteries are widely used in industrial applications, particularly in electric and hybrid vehicles. Since internal battery parameters such as the state of charge (SoC) and state of health (SoH) are not directly measurable, developing a reliable model and accurately extracting its parameters are essential for optimizing battery performance. In this study, two widely used models, the equivalent circuit model and the electrochemical model, were examined. Experimental tests were conducted on lithium-ion battery cells, and the parameters for both models were extracted using methods such as genetic algorithms and various estimation and optimization tools. The accuracy of the models was eva‎luated in independent tests using the root mean square error (RMSE) criterion. The results showed that for the electrical models, the second-order model provided approximately 20% greater accuracy compared to the first-order model, although its execution time was roughly six times longer. On the other hand, the electrochemical model, with its continuous output, exhibited 76% lower error than the best-case electrical model; however, this higher accuracy came at a computational cost approximately 12 times greater. Accordingly, the electrochemical model is more suitable for applications where high accuracy is critical and computational power is not a limitation, whereas the electrical model is preferable for embedded systems with limited computational resources.

باتري ليتيوم يوني , مدل الكتروشيميايي باتري , استخراج پارامترهاي مدل باتري , ارزيابي مدل باتري , مدل مدار معادل الكتريكي باتري

Lithium-ion battery , Battery Electrochemical Model , Battery Model Parameter Identification , Battery Model eva‎luation , Battery Equivalent Circuit Model

Mahdi Bahreiny

Masoud Dahmardeh