Research on Life Attenuation of Lithium-Ion Batteries Based on IC Curves and Voltage Hybrid Evaluation Algorithm
Due to the gradual degradation of lithium batteries during use, simple and accurate evaluation of their performance is crucial for the optimization of Battery Management Systems. Traditional battery performance evaluations often struggle to accurately reflect the actual state of battery degradation...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10807211/ |
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| Summary: | Due to the gradual degradation of lithium batteries during use, simple and accurate evaluation of their performance is crucial for the optimization of Battery Management Systems. Traditional battery performance evaluations often struggle to accurately reflect the actual state of battery degradation due to the indistinct features of voltage and current data. To overcome this issue, it is proposed a hybrid input method that combines IC curves with voltage data, and employs a Long Short-Term Memory (LSTM) deep learning model to assess battery degradation. To improve evaluation accuracy, utilized the LSTM model, leveraging its capability to process time-series data, effectively capturing the dynamic characteristics of battery performance as it changes over time. By integrating a mixed input approach with voltage data, this method demonstrated higher accuracy in a series of validation tests, with evaluation precision controlled within 2%. This finding offers a simple and efficient technical solution for optimizing battery management systems, promising to enhance battery performance monitoring and management processes. |
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| ISSN: | 2169-3536 |