Cycling Operation of a LiFePO<sub>4</sub> Battery and Investigation into the Influence on Equivalent Electrical Circuit Elements
This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO<sub>4</sub>)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/11/6/211 |
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| Summary: | This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO<sub>4</sub>)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge–discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models. |
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| ISSN: | 2313-0105 |