This study presents a novel methodology for developing a digital twin of a lithium-ion coin cell battery (Graphite-NMC622), accurately replicating the average discharge behaviour of various laboratory-tested batteries and characterizing degradation phenomena through cyclic ageing experiments. Given the anticipated rise in electric vehicle adoption, this work is particularly relevant for addressing the growing demand for lithium-ion batteries. The experimental characterization identified the minimum requirements for battery modelling, with tests conducted up to a C/5 current. Degradation behaviours were analysed through cycle ageing tests at two State-Of-Charge (SOC) ranges (100 %–0 % and 90 %–10 %), establishing a robust foundation for modelling degradation trends. While further calendar ageing tests could enhance the degradation modelling, they would require extensive data and time. Despite these constraints, the virtual coin cell model developed using GT-AutoLion, an industry-standard CAE software, demonstrated excellent accuracy, achieving an RRMSE of less than 2.0 % and R2 greater than 0.95. This work is significant as it provides a reliable framework for battery modelling that can assist companies in optimizing battery design and performance.