Development of Automotive Embedded Applications Using the AUTOSAR System Architecture

Abstract

In the automotive industry, embedded systems play a crucial role in operating various electrical systems and subsystems in vehicles. This project focuses on developing a reliable and efficient software system for automotive applications, specifically the predictive battery temperature estimation system. The approach utilizes Simulink models and the Kalman Filter (KF) algorithm to design a robust software system for battery thermal management in electric vehicles. To evaluate the accuracy and efficiency of the KF algorithm, it is compared with the Extended Kalman Filter (EKF) for non-linear battery models. The system’s reliability and performance are ensured through comprehensive testing using MATLAB Simulink in Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Processor-in-the-Loop (PIL) environments. The results indicate that the EKF is recommended for estimating battery temperature in systems with rapid fluctuations and non-linear behavior, where precise estimation is essential. The software system demonstrates scalability, efficiency, and outperforms alternative methods by providing accurate and timely temperature estimates for the battery. The implementation of the software system in vehicles can be achieved by integrating it into the AUTOSAR architecture, ensuring its compatibility and seamless integration with other vehicle systems. By adopting a model-based approach with Simulink models and the KF algorithm, this project contributes to the advancement of reliable and high-performance software systems in the automotive industry, particularly in battery thermal management for electric vehicles.