Electrical modeling of battery system of an electric vehicle

Today, the world is experiencing rapid technological advancements that affect all sectors, including transportation. Electric vehicles are increasingly being recognized as a promising technology contributing to the reduction of emissions and the dependence on fossil fuels. However, the development of these vehicles largely relies on advancements in battery technology. Lithium-ion batteries are the most widely used type in electric vehicles due to their high energy density, long service life, lightweight design, low discharge rate, cost-effectiveness, and absence of memory effect. Additionally, they are made from environmentally friendly materials, produce no harmful emissions, and offer a high level of safety, making them an ideal choice for meeting the high-performance requirements of electric vehicles. A proper model of these batteries is essential for simulating various tasks such as voltage prediction, state of charge (SOC) estimation, power and degradation analysis, and battery life calculation, as well as for developing control and optimization strategies. Despite extensive research on battery modeling and behavior prediction, several unresolved issues remain. Batteries are considered a critical component in modern electric vehicle systems. This thesis deals with the modeling of electric vehicle batteries. The thesis focuses on the application of advanced methods to improve the accuracy of modeling and predicting battery performance, thereby increasing the efficiency and reliability of batteries. In this context, a simple and effective method is proposed, utilizing a second-order transfer function to estimate the battery voltage and determine the state of charge. This model describes the dynamic behavior of batteries through mathematical relationships between input signals (current) and output responses (voltage). Transfer functions were derived from experimental data and used to create linear models that capture both the transient and static responses of the battery. The performance of the proposed method was compared to another model, the thermal equivalent circuit model. The comparison results show that the proposed method performs well in voltage estimation and is also successful in estimating the state of charge, but it provides slightly lower accuracy compared to voltage estimation. In order to process the state of charge results and obtain more accurate results, the research integrates artificial neural networks, especially artificial neural networks (ANNs)into the modeling framework. ANNs are used to capture nonlinear relationships and complex patterns in battery behavior; such relationships may not be fully captured by traditional transfer functions. The study results demonstrate that the neural network model provides superior accuracy in predicting the battery's state of charge compared to the transfer function model. Finally, significant findings of the study are presented in the methodology and results sections.