The shift towards electric transportation on a global scale is being primarily driven by regulatory requirements and market demand. The impact of the COVID-19 pandemic on air pollution, energy demand, and CO2 emissions has further accelerated this transition. This transformation necessitates the development of efficient electric propulsion systems, particularly for commercial vehicles. These systems not only have a positive environmental impact but also offer significant financial advantages to fleet owners due to lower overall costs. One of the major challenges in this transition is the design and calibration of regenerative braking strategies, especially for commercial vehicles that exhibit significant variations in weight. This weight difference between curb and gross vehicle weight is a common scenario in the commercial vehicle sector. This thesis introduces the Adaptive One-Pedal Drive (A-OPD) strategy, which is specifically tailored for electric commercial vehicles with varying weight profiles and lacking advanced drive-by-wire braking systems. The thesis focuses on the development and accurate assessment of a model-centric approach for electrified propulsion systems. This approach establishes a strong correlation between the model and physical data, demonstrating its reliability in estimating critical variables such as battery state-of-charge, battery terminal voltage, system high-voltage DC, and wheel torque, even under diverse driving conditions. This model-centric approach serves as a valuable tool for optimizing design and conducting tradeoff analyses, enabling efficient evaluation of energy efficiency and drivability. Selecting the most suitable electrified propulsion system architecture is a crucial decision. The thesis categorizes electrified propulsion system architectures based on their impact on vehicle performance, energy consumption, and total cost of ownership. This selection process involves a multidisciplinary approach that takes into account both technical and business requirements. The central research focus of this thesis centers on regenerative braking systems. It compares series and parallel configurations, traditional one-pedal-drive (OPD), and introduces an innovative Adaptive One-Pedal Drive (A-OPD). The A-OPD relies on vehicle running mass identification using the Recursive Least Square Filter (RLS) and weight classification. This A-OPD strategy significantly enhances energy efficiency in urban traffic scenarios, even when vehicles are partially loaded. It outperforms parallel regenerative braking systems by up to 50% while maintaining performance levels similar to the series regenerative braking strategy. This innovation represents a significant leap in energy efficiency for electric commercial vehicles without the need for complex electronic braking systems. In summary, this thesis advances our understanding of optimizing the performance of electric commercial vehicles. The A-OPD strategy proves to be a practical and valuable tool for enhancing energy efficiency, particularly in dense urban traffic, and it outperforms parallel regenerative braking systems. Utilizing model-in-the-loop and driver-in-the-loop simulations, this thesis offers a comprehensive framework for designing efficient electrified propulsion system architectures. / Thesis / Doctor of Philosophy (PhD)