This paper presents a comparative analysis of Markov chain and K-means clustering techniques for constructing representative drive cycles in the optimisation of powertrain modules for electric vehicles (EVs). As part of the EU funded POWERDRIVE (Power electronics optimisation for next generation electric vehicle components) project, the research focuses on a case study conducted in Dublin, Ireland, aiming to develop an accurate and efficient drive cycle that captures the unique driving patterns and characteristics of the city. The Markov chain technique utilises historical driving data to model the transition probabilities between different driving states, while the K-means clustering technique groups similar driving patterns based on key parameters. The analysis evaluates the effectiveness of both approaches in terms of their ability to accurately represent real-world driving behaviour and their computational efficiency. The results of the study provide insights into the strengths and limitations of each technique, enabling researchers and practitioners to make informed decisions when selecting an appropriate methodology for drive cycle construction. The findings contribute to the ongoing efforts in developing optimised powertrain modules for EVs and advancing the field of electric vehicle technology.