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Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model

1
Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines
2
Energy Engineering Program, National Graduate School of Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines
3
Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK
*
Author to whom correspondence should be addressed.
Energies 2020, 13(20), 5276; https://doi.org/10.3390/en13205276
Received: 3 September 2020 / Revised: 1 October 2020 / Accepted: 2 October 2020 / Published: 11 October 2020
(This article belongs to the Section A5: Smart Grids and Microgrids)
Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment. View Full-Text
Keywords: lithium-ion NMC battery; battery prices; multifactor learning curve lithium-ion NMC battery; battery prices; multifactor learning curve
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MDPI and ACS Style

Penisa, X.N.; Castro, M.T.; Pascasio, J.D.A.; Esparcia, E.A., Jr.; Schmidt, O.; Ocon, J.D. Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model. Energies 2020, 13, 5276. https://doi.org/10.3390/en13205276

AMA Style

Penisa XN, Castro MT, Pascasio JDA, Esparcia EA Jr., Schmidt O, Ocon JD. Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model. Energies. 2020; 13(20):5276. https://doi.org/10.3390/en13205276

Chicago/Turabian Style

Penisa, Xaviery N., Michael T. Castro, Jethro D.A. Pascasio, Eugene A. Esparcia Jr., Oliver Schmidt, and Joey D. Ocon. 2020. "Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model" Energies 13, no. 20: 5276. https://doi.org/10.3390/en13205276

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