Abstract
Li-ion battery internal impedance is a crucial pa-rameter for state of health estimation. In this paper, a 3RC equivalent circuit model of the battery is considered. This model is also able to simulate the aging of the battery by extracting the battery parameter dependencies with aging. By observing the changes in the model parameters, it is possible to extract the battery state of heath. This means that starting from an impedance measurement it is possible to estimate the state of health of the battery. This could be a very attractive solution for the battery swapping industry, where keeping track of the aging can help identify early signs of degradation or potential safety hazards, thereby increasing customer satisfaction, optimizing operational efficiency and maximizing the return of investment. In this work, a neural network for state of health estimation from internal resistance is developed and it is able to correctly estimate state of health with a mean absolute relative error of 4.25%.
Original language | English |
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Title of host publication | 2024 IEEE International Communications Energy Conference, INTELEC 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | 2024 |
Article number | 10678967 |
ISBN (Print) | 979-8-3503-7058-4 |
ISBN (Electronic) | 9798350370577 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Communications Energy Conference, INTELEC 2024 - Bengaluru, India Duration: 4 Aug 2024 → 7 Aug 2024 |
Conference
Conference | 2024 IEEE International Communications Energy Conference, INTELEC 2024 |
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Country/Territory | India |
City | Bengaluru |
Period | 04/08/2024 → 07/08/2024 |
Series | INTELEC, International Telecommunications Energy Conference (Proceedings) |
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ISSN | 0275-0473 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- battery aging
- battery modelling
- Equivalent circuit model
- state of health estimation