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Accessible Battery Model with Aging Dependency

Author

Listed:
  • Christophe Savard

    (Mainate Labs, rue Notre Dame de l’Oratoire, 43270 Allègre, France)

  • Emiliia Iakovleva

    (General Electrical Engineering Department Saint Petersburg Mining University, 2, 21st Line, 199106 St Petersburg, Russia)

  • Daniil Ivanchenko

    (General Electrical Engineering Department Saint Petersburg Mining University, 2, 21st Line, 199106 St Petersburg, Russia)

  • Anton Rassõlkin

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

Designed to store and discharge electrical energy, rechargeable batteries consist of elementary storage cell assemblies. Aging is affected by various aggravating factors, mainly temperature. There are many electric or electrochemical models which describe their operation. Most standard models do not consider the aging phenomena of batteries and their consequences, while batteries deteriorate when used or stored. Precisely, most battery models do not simulate the influence of cell aging on other cells. The model presented in this paper incorporates aging and the effects of mutual interactions between cells. The model can be established based on four measurement points on the cell characteristic curve and allows the simulation of a single cell’s or multiple coupled cells’ behavior. The model can then be easily implemented in simulation software like Matlab.

Suggested Citation

  • Christophe Savard & Emiliia Iakovleva & Daniil Ivanchenko & Anton Rassõlkin, 2021. "Accessible Battery Model with Aging Dependency," Energies, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3493-:d:573846
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    References listed on IDEAS

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    1. Ajaykumar Unagar & Yuan Tian & Manuel Arias Chao & Olga Fink, 2021. "Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning," Energies, MDPI, vol. 14(5), pages 1-12, March.
    2. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Farrell, Troy W. & Tran, Ngoc Tham & Teague, Joseph, 2019. "Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies," Applied Energy, Elsevier, vol. 248(C), pages 512-525.
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    Cited by:

    1. Daud Mustafa Minhas & Josef Meiers & Georg Frey, 2022. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets," Energies, MDPI, vol. 15(5), pages 1-29, February.
    2. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    3. Xiaoyu Li & Chuxin Wu & Chen Fu & Shanpu Zheng & Jindong Tian, 2022. "State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning," Energies, MDPI, vol. 15(16), pages 1-19, August.

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