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Lifetime Expectancy of Li-Ion Batteries used for Residential Solar Storage

Author

Listed:
  • Hector Beltran

    (Department of Industrial Systems Engineering and Design, Jaume I University, Sos Baynat Avenue, Castelló de la Plana, 12071 Castelló, Spain)

  • Pablo Ayuso

    (Department of Industrial Systems Engineering and Design, Jaume I University, Sos Baynat Avenue, Castelló de la Plana, 12071 Castelló, Spain)

  • Emilio Pérez

    (Department of Industrial Systems Engineering and Design, Jaume I University, Sos Baynat Avenue, Castelló de la Plana, 12071 Castelló, Spain)

Abstract

This paper analyses the degradation that is experienced by different types of Li-ion batteries when used as home solar storage systems controlled to minimize the electricity bill of the corresponding household. Simulating the annual operation of photovoltaic (PV) residential systems with batteries at different locations was undertaken to perform the study and it uses actual consumption values and real PV production profiles, as well as validated semi-empirical ageing models of the batteries. Therefore, the work provides a realistic prognosis around the lifetime expectancies for the different Li-ion chemistries.

Suggested Citation

  • Hector Beltran & Pablo Ayuso & Emilio Pérez, 2020. "Lifetime Expectancy of Li-Ion Batteries used for Residential Solar Storage," Energies, MDPI, vol. 13(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:568-:d:312820
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    References listed on IDEAS

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    Cited by:

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    7. Prahaladh Paniyil & Vishwas Powar & Rajendra Singh & Benjamin Hennigan & Pamela Lule & Matthew Allison & John Kimsey & Anthony Carambia & Dhruval Patel & Daniel Carrillo & Zachary Shriber & Truman Baz, 2020. "Photovoltaics- and Battery-Based Power Network as Sustainable Source of Electric Power," Energies, MDPI, vol. 13(19), pages 1-22, September.
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    10. Renos Rotas & Petros Iliadis & Nikos Nikolopoulos & Ananias Tomboulides & Elias Kosmatopoulos, 2022. "Dynamic Simulation and Performance Enhancement Analysis of a Renewable Driven Trigeneration System," Energies, MDPI, vol. 15(10), pages 1-27, May.
    11. Andrea Mannelli & Francesco Papi & George Pechlivanoglou & Giovanni Ferrara & Alessandro Bianchini, 2021. "Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries," Energies, MDPI, vol. 14(8), pages 1-32, April.
    12. George Kyriakarakos & Erika Ntavou & Dimitris Manolakos, 2020. "Investigation of the Use of Low Temperature Geothermal Organic Rankine Cycle Engine in an Autonomous Polygeneration Microgrid," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    13. Perčić, Maja & Frković, Lovro & Pukšec, Tomislav & Ćosić, Boris & Li, Oi Lun & Vladimir, Nikola, 2022. "Life-cycle assessment and life-cycle cost assessment of power batteries for all-electric vessels for short-sea navigation," Energy, Elsevier, vol. 251(C).
    14. Superchi, Francesco & Giovannini, Nathan & Moustakis, Antonis & Pechlivanoglou, George & Bianchini, Alessandro, 2024. "Optimization of the power output scheduling of a renewables-based hybrid power station using MILP approach: The case of Tilos island," Renewable Energy, Elsevier, vol. 220(C).
    15. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).
    16. Secchi, Mattia & Barchi, Grazia & Macii, David & Moser, David & Petri, Dario, 2021. "Multi-objective battery sizing optimisation for renewable energy communities with distribution-level constraints: A prosumer-driven perspective," Applied Energy, Elsevier, vol. 297(C).
    17. Natascia Andrenacci & Mauro Di Monaco & Giuseppe Tomasso, 2022. "Influence of Battery Aging on the Operation of a Charging Infrastructure," Energies, MDPI, vol. 15(24), pages 1-18, December.

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