Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors
Continuous monitoring of the State of Health (SOH) in Lithium-ion (Li-ion) batteries is crucial for ensuring operational reliability and safety in powered devices. This paper presents a novel Classifier-Pursued Maintenance Index Scheme (CPMI) that leverages Fiber Bragg Grating (FBG) sensor measureme...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Measurement: Sensors |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917425001618 |
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| author | Taher M. Ghazal Ali Q. Saeed Mosleh M. Abualhaj Taj-Aldeen Naser Abdali Munir Ahmad |
| author_facet | Taher M. Ghazal Ali Q. Saeed Mosleh M. Abualhaj Taj-Aldeen Naser Abdali Munir Ahmad |
| author_sort | Taher M. Ghazal |
| collection | DOAJ |
| description | Continuous monitoring of the State of Health (SOH) in Lithium-ion (Li-ion) batteries is crucial for ensuring operational reliability and safety in powered devices. This paper presents a novel Classifier-Pursued Maintenance Index Scheme (CPMI) that leverages Fiber Bragg Grating (FBG) sensor measurements for sustainable SOH monitoring and maintenance scheduling. The CPMI framework processes real-time temperature and strain measurements from strategically placed FBG sensors during charge-discharge cycles to estimate battery capacity degradation and determine maintenance requirements. The proposed system employs a support vector-based classification algorithm that categorizes operational states based on FBG sensor data streams, identifying deviations from optimal temperature and voltage ranges. This classification approach generates a quantitative maintenance index that enables systematic assessment scheduling rather than arbitrary inspections. Experimental validation over 200 charge-discharge cycles demonstrates the CPMI system's effectiveness, achieving a maintenance state identification accuracy of 0.95, 75 % classification success rate, classification latency of 0.1 s, precision exceeding 0.95, and an assessment reliability of 0.98. Integrating FBG sensors with the CPMI framework provides a robust Li-ion battery SOH monitoring solution, enabling predictive maintenance strategies and enhanced power management capabilities. The proposed system demonstrates significant potential for improving battery lifecycle management and operational reliability in various applications. |
| format | Article |
| id | doaj-art-948bc7ee438643cea85ddb0da4c7f299 |
| institution | Kabale University |
| issn | 2665-9174 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj-art-948bc7ee438643cea85ddb0da4c7f2992025-08-20T03:44:11ZengElsevierMeasurement: Sensors2665-91742025-08-014010196710.1016/j.measen.2025.101967Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensorsTaher M. Ghazal0Ali Q. Saeed1Mosleh M. Abualhaj2Taj-Aldeen Naser Abdali3Munir Ahmad4Department of Network and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan; Corresponding author.Computer Center, Northern Technical University, Ninevah, IraqDepartment of Network and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, JordanMathematics Department, College of Basic Education, University of Misan, Misan, IraqThe University College, Korea University, Seoul, 02841, Republic of Korea; Corresponding author.Continuous monitoring of the State of Health (SOH) in Lithium-ion (Li-ion) batteries is crucial for ensuring operational reliability and safety in powered devices. This paper presents a novel Classifier-Pursued Maintenance Index Scheme (CPMI) that leverages Fiber Bragg Grating (FBG) sensor measurements for sustainable SOH monitoring and maintenance scheduling. The CPMI framework processes real-time temperature and strain measurements from strategically placed FBG sensors during charge-discharge cycles to estimate battery capacity degradation and determine maintenance requirements. The proposed system employs a support vector-based classification algorithm that categorizes operational states based on FBG sensor data streams, identifying deviations from optimal temperature and voltage ranges. This classification approach generates a quantitative maintenance index that enables systematic assessment scheduling rather than arbitrary inspections. Experimental validation over 200 charge-discharge cycles demonstrates the CPMI system's effectiveness, achieving a maintenance state identification accuracy of 0.95, 75 % classification success rate, classification latency of 0.1 s, precision exceeding 0.95, and an assessment reliability of 0.98. Integrating FBG sensors with the CPMI framework provides a robust Li-ion battery SOH monitoring solution, enabling predictive maintenance strategies and enhanced power management capabilities. The proposed system demonstrates significant potential for improving battery lifecycle management and operational reliability in various applications.http://www.sciencedirect.com/science/article/pii/S2665917425001618Battery healthClassifier learningMaintenance indexSOH |
| spellingShingle | Taher M. Ghazal Ali Q. Saeed Mosleh M. Abualhaj Taj-Aldeen Naser Abdali Munir Ahmad Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors Measurement: Sensors Battery health Classifier learning Maintenance index SOH |
| title | Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors |
| title_full | Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors |
| title_fullStr | Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors |
| title_full_unstemmed | Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors |
| title_short | Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors |
| title_sort | pioneering cpmi framework for accurate state of health assessment in lithium ion battery power management using fbg sensors |
| topic | Battery health Classifier learning Maintenance index SOH |
| url | http://www.sciencedirect.com/science/article/pii/S2665917425001618 |
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