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A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms

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  • Ashleigh Townsend

    (School of Electrical, Electronic and Computer Engineering, North West University, Potchefstroom 2520, South Africa)

  • Rupert Gouws

    (School of Electrical, Electronic and Computer Engineering, North West University, Potchefstroom 2520, South Africa)

Abstract

As renewable energy sources, such as solar systems, are becoming more popular, the focus is moving into more effective utilization of these energy sources and harvesting more energy for intermittency reduction in this renewable source. This is opening up a market for methods of energy storage and increasing interest in batteries, as they are, as it stands, the foremost energy storage device available to suit a wide range of requirements. This interest has brought to light the downfalls of batteries and resultantly made room for the investigation of ultra-capacitors as a solution to these downfalls. One of these downfalls is related to the decrease in capacity, and temperamentality thereof, of a battery when not used precisely as stated by the supplier. The usable capacity is reliant on the complete discharge/charge cycles the battery can undergo before a 20% degradation in its specified capacity is observed. This article aims to investigate what causes this degradation, what aggravates it and how the degradation affects the usage of the battery. This investigation will lead to the identification of a gap in which this degradation can be decreased, prolonging the usage and increasing the feasibility of the energy storage devices.

Suggested Citation

  • Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4930-:d:856448
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    Cited by:

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    3. Edoardo Lelli & Alessia Musa & Emilio Batista & Daniela Anna Misul & Giovanni Belingardi, 2023. "On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-21, June.

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