Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting
A novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945873/ |
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| author | Sahar Qaadan Aiman Alshare Rami Alazrai Alexander Popp Benedikt Schmuelling |
| author_facet | Sahar Qaadan Aiman Alshare Rami Alazrai Alexander Popp Benedikt Schmuelling |
| author_sort | Sahar Qaadan |
| collection | DOAJ |
| description | A novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage on internal resistance, effectively capturing the non-linear electrochemical behaviors that drive battery degradation. The proposed framework is rigorously validated using two open-source datasets: the Samsung INR21700-50E and the Forklift Battery Degradation datasets. The CPIHL model demonstrates exceptional performance, achieving an R2 score of 0.9994, a mean absolute error of 0.0007, and a root mean square error of 0.0025, outperforming all baseline machine learning and deep learning models, including Random Forest, Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Units. The CPIHL framework exhibits robust, interpretable, and scalable behavior, making it highly effective for predictive maintenance tasks. It provides actionable insights for battery management, enabling the optimization of operational strategies to extend battery life. By improving battery health monitoring, this work contributes to sustainable energy usage, enhancing efficiency and reducing battery disposal waste. |
| format | Article |
| id | doaj-art-91c7a071df5540e1bf649f2089fa450a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-91c7a071df5540e1bf649f2089fa450a2025-08-20T02:12:24ZengIEEEIEEE Access2169-35362025-01-0113617286173910.1109/ACCESS.2025.355631410945873Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health ForecastingSahar Qaadan0https://orcid.org/0000-0002-1956-6376Aiman Alshare1Rami Alazrai2https://orcid.org/0000-0002-1296-0231Alexander Popp3https://orcid.org/0000-0002-9898-9456Benedikt Schmuelling4https://orcid.org/0000-0002-1050-5278Department of Mechatronics Engineering, German Jordanian University, Amman, JordanDepartment of Mechanical and Maintenance Engineering, German Jordanian University, Amman, JordanDepartment of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman, JordanInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyA novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage on internal resistance, effectively capturing the non-linear electrochemical behaviors that drive battery degradation. The proposed framework is rigorously validated using two open-source datasets: the Samsung INR21700-50E and the Forklift Battery Degradation datasets. The CPIHL model demonstrates exceptional performance, achieving an R2 score of 0.9994, a mean absolute error of 0.0007, and a root mean square error of 0.0025, outperforming all baseline machine learning and deep learning models, including Random Forest, Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Units. The CPIHL framework exhibits robust, interpretable, and scalable behavior, making it highly effective for predictive maintenance tasks. It provides actionable insights for battery management, enabling the optimization of operational strategies to extend battery life. By improving battery health monitoring, this work contributes to sustainable energy usage, enhancing efficiency and reducing battery disposal waste.https://ieeexplore.ieee.org/document/10945873/Causality analysisdeep learninghybrid learninglithium-ion battery degradationphysics-infused modelstate of health |
| spellingShingle | Sahar Qaadan Aiman Alshare Rami Alazrai Alexander Popp Benedikt Schmuelling Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting IEEE Access Causality analysis deep learning hybrid learning lithium-ion battery degradation physics-infused model state of health |
| title | Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting |
| title_full | Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting |
| title_fullStr | Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting |
| title_full_unstemmed | Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting |
| title_short | Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting |
| title_sort | causal physics infused hybrid learning cpihl framework for next gen battery health forecasting |
| topic | Causality analysis deep learning hybrid learning lithium-ion battery degradation physics-infused model state of health |
| url | https://ieeexplore.ieee.org/document/10945873/ |
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