Optimization and Integration of Model-Based Algorithms for the Evaluation and Management of Dynamic Parameters of Lithium-Ion Batteries, Deployed on a Custom Automotive BMS

The efficient management of lithium-ion (Li-ion) battery packs is essential for ensuring the performance, longevity, and safety of modern electric vehicles (EVs). This thesis focuses on the development, integration, and optimization of model-based algorithms within an automotive Battery Management System (BMS) to accurately estimate battery parameters and manage the power limits. The research emphasizes the role of software algorithms, particularly the Extended Kalman Filter (EKF) for state-of-charge (SOC) estimation and the PI controller-based Power Limits algorithms for power management. A model-based approach was employed to evaluate the dynamic parameters of Li-ion batteries, leveraging MATLAB’s Simulink for modelling and simulation, as well as Embedded Coder for code generation, optimization and deployment on a custom BMS platform. Optimization techniques were applied to improve the execution efficiency of the algorithms, leading to a significant reduction in computation time. The results demonstrate that the automated code generation, optimization and integration of model-based algorithms by the help of MATLAB/Simulink not only enhances algorithm performance but also simplifies integration into embedded systems. This research highlights the benefits of advanced battery management techniques and provides a foundation for further improvements in BMS software, ensuring safer and more efficient energy management solutions for automotive applications.