Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries
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2025
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| author | Eric L. Pereira Damilola Ogun Davi M. Soares |
| author_facet | Eric L. Pereira Damilola Ogun Davi M. Soares Eric L. Pereira Damilola Ogun Davi M. Soares |
| collection | Wiley Open Access |
| contents | Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries Eric L. Pereira Damilola Ogun Davi M. Soares ChemElectroChem Lithium‐ion batteries (LIBs) are widely used for energy storage in various industries due to their high energy density and long lifespan. However, degradation mechanisms may lead to hazardous conditions, such as thermal runaway. Solely relying on capacity changes for state of health (SOH) assessment is insufficient, given the complexity of LIBs. This work introduces comprehensive real‐time insights for state of health prediction (CRISP), a novel framework for comprehensive SOH assessment and degradation mechanisms identification. Using data from commercial LIBs, CRISP runs on a low‐cost Raspberry Pi with remote monitoring capabilities, generating aged anode half‐cell voltages for each reference performance test (RPT) as references for lithium plating assessment. CRISP processed the data of each RPT in ≈2.9 s and outputs multiple physical quantities for SOH evaluation. Results show that LIBs cycled at 100% depth of discharge (DOD) exhibited greater cathode material loss compared to those cycled at narrower DODs. However, no dendritic lithium deposition is detected by evaluating and correlating the physical parameters provided by CRISP. In summary, this study highlights the importance of multi‐parameter SOH assessment, demonstrating that single‐parameter methods (e.g., capacity‐based) fail to capture the full scope of LIBs health. 10.1002/celc.202400708 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/celc.202400708 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_celc_202400708 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries Eric L. Pereira Damilola Ogun Davi M. Soares ChemElectroChem Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries Eric L. Pereira Damilola Ogun Davi M. Soares ChemElectroChem Lithium‐ion batteries (LIBs) are widely used for energy storage in various industries due to their high energy density and long lifespan. However, degradation mechanisms may lead to hazardous conditions, such as thermal runaway. Solely relying on capacity changes for state of health (SOH) assessment is insufficient, given the complexity of LIBs. This work introduces comprehensive real‐time insights for state of health prediction (CRISP), a novel framework for comprehensive SOH assessment and degradation mechanisms identification. Using data from commercial LIBs, CRISP runs on a low‐cost Raspberry Pi with remote monitoring capabilities, generating aged anode half‐cell voltages for each reference performance test (RPT) as references for lithium plating assessment. CRISP processed the data of each RPT in ≈2.9 s and outputs multiple physical quantities for SOH evaluation. Results show that LIBs cycled at 100% depth of discharge (DOD) exhibited greater cathode material loss compared to those cycled at narrower DODs. However, no dendritic lithium deposition is detected by evaluating and correlating the physical parameters provided by CRISP. In summary, this study highlights the importance of multi‐parameter SOH assessment, demonstrating that single‐parameter methods (e.g., capacity‐based) fail to capture the full scope of LIBs health. 10.1002/celc.202400708 http://creativecommons.org/licenses/by/4.0/ |
| title | Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries |
| topic | ChemElectroChem |
| url | https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/celc.202400708 |