Energy technology cost reductions are the result of many innovation trends in the energy system. The energy technology innovation system is increasingly well understood at an aggregate level and using qualitative concepts. However, the quantification of the multiple drivers of energy technology cost reduction trends remains poorly understood. This paper addresses this knowledge gap by presenting a systematic review of current practices. Despite their simplifications, one-factor learning curves (i.e. using a single driver) remain the most popular method for quantitative modelling of energy technology innovation. The role of multiple drivers on cost reductions has been cited in previous studies. This review enriches our understanding of these multiple drivers by examining their impact along different stages of technology development. The review quantifies the variation in these drivers and shows that the development of multi-factor learning curve models and bottom-up cost models are still in their infancy. With a focus on onshore wind and solar PV technologies, the review finds that most of the published multi-factor learning curve analyses are focused on addressing the impact of drivers related to i) manufacturing process improvements (i.e. learning by-doing) and ii) technology feature improvements (i.e. learning by-researching). This means that the other learning drivers such as market dynamics and learning by-interacting across different stakeholders and geographical areas are still poorly quantified, despite their impact on cost reduction being recognised in the innovation literature. There is a danger that misinformed policies are currently being developed in the absence of a good understanding of these multiple drivers.