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

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  • Xaviery N. Penisa

    (Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines
    Energy Engineering Program, National Graduate School of Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines)

  • Michael T. Castro

    (Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines)

  • Jethro Daniel A. Pascasio

    (Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines)

  • Eugene A. Esparcia

    (Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines)

  • Oliver Schmidt

    (Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK)

  • Joey D. Ocon

    (Laboratory of Electrochemical Engineering (LEE), Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines
    Energy Engineering Program, National Graduate School of Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines)

Abstract

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.

Suggested Citation

  • Xaviery N. Penisa & Michael T. Castro & Jethro Daniel A. Pascasio & Eugene A. Esparcia & Oliver Schmidt & Joey D. Ocon, 2020. "Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model," Energies, MDPI, vol. 13(20), pages 1-18, October.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5276-:d:426233
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    1. Pascasio, Jethro Daniel A. & Esparcia, Eugene A. & Castro, Michael T. & Ocon, Joey D., 2021. "Comparative assessment of solar photovoltaic-wind hybrid energy systems: A case for Philippine off-grid islands," Renewable Energy, Elsevier, vol. 179(C), pages 1589-1607.
    2. Steckel, Tobiah & Kendall, Alissa & Ambrose, Hanjiro, 2021. "Applying levelized cost of storage methodology to utility-scale second-life lithium-ion battery energy storage systems," Applied Energy, Elsevier, vol. 300(C).
    3. Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán & Jose L. Martínez-Ramos, 2021. "Centralized Control of Distribution Networks with High Penetration of Renewable Energies," Energies, MDPI, vol. 14(14), pages 1-13, July.
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    5. Yuriy Bilan & Serhiy Kozmenko & Alex Plastun, 2022. "Price Forecasting in Energy Market," Energies, MDPI, vol. 15(24), pages 1-6, December.
    6. Yifan Wang & Laurence A. Wright, 2021. "A Comparative Review of Alternative Fuels for the Maritime Sector: Economic, Technology, and Policy Challenges for Clean Energy Implementation," World, MDPI, vol. 2(4), pages 1-26, October.
    7. Li, Xiaobin & Sengupta, Tuhin & Si Mohammed, Kamel & Jamaani, Fouad, 2023. "Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods," Resources Policy, Elsevier, vol. 82(C).
    8. Yana Buravleva & Decai Tang & Brandon J. Bethel, 2021. "Incentivizing Innovation: The Causal Role of Government Subsidies on Lithium-Ion Battery Research and Development," Sustainability, MDPI, vol. 13(15), pages 1-16, July.

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