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.