This article presents a two-factor learning curve model to analyze the impact of innovation and deployment policies on the cost of energy storage technologies. The study uses patent activity, production output capacity, and historical global average prices to track learning rates of battery energy storage technologies. The model incorporates logarithmized production volumes and innovation activity represented by cumulative international Patent Cooperation Treaty (PCT) patents during each year, with the logarithmized price as the dependent variable. The two-factor model shows a learning rate of 16.9% for economies of scale and a decrease in prices of 2.0% per 100 PCT patents. The model explains the recent plunge of battery prices better than conventional models using economies of scale or a classic experience curve approach. The study also highlights the importance of innovation in reducing costs and the need for increased public R&D spending to drive innovation and cost reductions in energy storage technologies. The findings suggest that a combination of innovation and deployment policies is necessary to achieve cost-effective low-carbon electricity. The study also discusses the potential for lithium-ion batteries to achieve necessary cost targets to push intermittent renewable systems with storage past conventional fossil-fuel-based generators. The authors argue for further application of the two-factor model in research compared to traditional one-factor learning curves. The study concludes that public R&D spending is crucial for promoting energy R&D and achieving a clean energy transition.This article presents a two-factor learning curve model to analyze the impact of innovation and deployment policies on the cost of energy storage technologies. The study uses patent activity, production output capacity, and historical global average prices to track learning rates of battery energy storage technologies. The model incorporates logarithmized production volumes and innovation activity represented by cumulative international Patent Cooperation Treaty (PCT) patents during each year, with the logarithmized price as the dependent variable. The two-factor model shows a learning rate of 16.9% for economies of scale and a decrease in prices of 2.0% per 100 PCT patents. The model explains the recent plunge of battery prices better than conventional models using economies of scale or a classic experience curve approach. The study also highlights the importance of innovation in reducing costs and the need for increased public R&D spending to drive innovation and cost reductions in energy storage technologies. The findings suggest that a combination of innovation and deployment policies is necessary to achieve cost-effective low-carbon electricity. The study also discusses the potential for lithium-ion batteries to achieve necessary cost targets to push intermittent renewable systems with storage past conventional fossil-fuel-based generators. The authors argue for further application of the two-factor model in research compared to traditional one-factor learning curves. The study concludes that public R&D spending is crucial for promoting energy R&D and achieving a clean energy transition.