Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review
Lithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy sto...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10857286/ |
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| author | Pedro Lozano Ruiz Nikolaos Damianakis Gautham Ram Chandra Mouli |
| author_facet | Pedro Lozano Ruiz Nikolaos Damianakis Gautham Ram Chandra Mouli |
| author_sort | Pedro Lozano Ruiz |
| collection | DOAJ |
| description | Lithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy storage systems, due to their effect on performance and lifespan, safety, and environmental footprint. Linear degradation models can be inaccurate in capturing the highly nonlinear behavior of LIB degradation caused by multiple simultaneous degradation mechanisms. Hence, this work first analyzes the main different mechanisms, their causes, and their interrelations. Secondly, the various single- and multi-mechanism physics-based (PB) and data-driven (DD) models for LIB degradation and knee-point identification are summarized and compared regarding their prediction performance on degradation and transition from stabilized to saturated aging. While single-mechanism PB models can be effective in the LIB first-life prediction, they can seriously undermine the knee-point and saturated aging. Moreover, the modeling of the different aging mechanisms can significantly increase the complexity of the multi-mechanism PB models. Finally, while DD models for LIB degradation have been developed, a DD model focused on knee-point identification and LIB second-life is still missing from the literature. |
| format | Article |
| id | doaj-art-65811d9909bc4acc82302e5a3bc09f3b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-65811d9909bc4acc82302e5a3bc09f3b2025-02-05T00:00:55ZengIEEEIEEE Access2169-35362025-01-0113211642118910.1109/ACCESS.2025.353591810857286Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A ReviewPedro Lozano Ruiz0Nikolaos Damianakis1https://orcid.org/0009-0005-2735-7362Gautham Ram Chandra Mouli2https://orcid.org/0000-0003-1997-4959Department of Power Management, Sonova, Stäfa, AG, SwitzerlandDepartment of Electrical Sustainable Energy, Delft University of Technology, Delft, The NetherlandsDepartment of Electrical Sustainable Energy, Delft University of Technology, Delft, The NetherlandsLithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy storage systems, due to their effect on performance and lifespan, safety, and environmental footprint. Linear degradation models can be inaccurate in capturing the highly nonlinear behavior of LIB degradation caused by multiple simultaneous degradation mechanisms. Hence, this work first analyzes the main different mechanisms, their causes, and their interrelations. Secondly, the various single- and multi-mechanism physics-based (PB) and data-driven (DD) models for LIB degradation and knee-point identification are summarized and compared regarding their prediction performance on degradation and transition from stabilized to saturated aging. While single-mechanism PB models can be effective in the LIB first-life prediction, they can seriously undermine the knee-point and saturated aging. Moreover, the modeling of the different aging mechanisms can significantly increase the complexity of the multi-mechanism PB models. Finally, while DD models for LIB degradation have been developed, a DD model focused on knee-point identification and LIB second-life is still missing from the literature.https://ieeexplore.ieee.org/document/10857286/Lithium-ion batteries (LIB)degradationdegradation mechanismsknee-pointphysics-baseddata-driven |
| spellingShingle | Pedro Lozano Ruiz Nikolaos Damianakis Gautham Ram Chandra Mouli Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review IEEE Access Lithium-ion batteries (LIB) degradation degradation mechanisms knee-point physics-based data-driven |
| title | Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review |
| title_full | Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review |
| title_fullStr | Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review |
| title_full_unstemmed | Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review |
| title_short | Physics-Based and Data-Driven Modeling of Degradation Mechanisms for Lithium-Ion Batteries—A Review |
| title_sort | physics based and data driven modeling of degradation mechanisms for lithium ion batteries x2014 a review |
| topic | Lithium-ion batteries (LIB) degradation degradation mechanisms knee-point physics-based data-driven |
| url | https://ieeexplore.ieee.org/document/10857286/ |
| work_keys_str_mv | AT pedrolozanoruiz physicsbasedanddatadrivenmodelingofdegradationmechanismsforlithiumionbatteriesx2014areview AT nikolaosdamianakis physicsbasedanddatadrivenmodelingofdegradationmechanismsforlithiumionbatteriesx2014areview AT gauthamramchandramouli physicsbasedanddatadrivenmodelingofdegradationmechanismsforlithiumionbatteriesx2014areview |