Diagnosis and prognosis of degradation in lithium-ion batteries
<p>Lithium-ion (Li-ion) batteries are the most popular energy storage technology in consumer electronics and electric vehicles and are increasingly applied in stationary storage systems. Yet, concerns about safety and reliability remain major obstacles, which must be addressed in order to impr...
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| Format: | Thèse |
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2017
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| _version_ | 1826281130822729728 |
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| author | Birkl, C |
| author2 | Howey, D |
| author_facet | Howey, D Birkl, C |
| author_sort | Birkl, C |
| collection | OXFORD |
| description | <p>Lithium-ion (Li-ion) batteries are the most popular energy storage technology in consumer electronics and electric vehicles and are increasingly applied in stationary storage systems. Yet, concerns about safety and reliability remain major obstacles, which must be addressed in order to improve the acceptance of this technology. The gradual degradation of Li-ion cells over time lies at the heart of this problem. Time, usage and environmental conditions lead to performance deterioration and cell failures, which, in rare cases, can be catastrophic due to res or explosions. The physical and chemical mechanisms responsible for degradation are numerous, complex and interdependent. Our understanding of degradation and failure of Li-ion cells is still very limited and more limited yet are reliable and practical methods for the detection and prediction of these phenomena.</p> <p>This thesis presents a comprehensive approach for the diagnosis and prognosis of degradation in Li-ion cells. The key to this approach is the extraction of information on electrode-speci c degradation through open circuit voltage (OCV) measurements. This is achieved in three stages. Firstly, a parametric OCV model is created, which computes the OCV of each electrode. Secondly, a diagnostic algorithm is devised, through which the OCV model is tted to OCV measurements recorded on Li-ion cells at various stages throughout their cycle life. The algorithm identi es the nature and quanti es the extent of degradation experienced by the cells. Lastly, the outputs of the algorithm are used to identify the likely failure modes of the cells and predict their end-of-life.</p> <p>The presented methods improve safe operation and predictions of remaining useful cycle life for commercial Li-ion cells. Greater certainty about the reliability, safety, required maintenance and depreciation of Li-ion battery systems can signi cantly enhance the competitiveness of battery electric storage in both automotive and stationary applications. The ndings presented in this work are therefore not only of technological but also of commercial interest.</p> |
| first_indexed | 2024-03-07T00:24:10Z |
| format | Thesis |
| id | oxford-uuid:7d8ccb9c-1469-4209-9995-5871fc908b54 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:24:10Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oxford-uuid:7d8ccb9c-1469-4209-9995-5871fc908b542022-03-26T21:04:26ZDiagnosis and prognosis of degradation in lithium-ion batteriesThesishttp://purl.org/coar/resource_type/c_db06uuid:7d8ccb9c-1469-4209-9995-5871fc908b54Lithium ion batteriesORA Deposit2017Birkl, CHowey, D<p>Lithium-ion (Li-ion) batteries are the most popular energy storage technology in consumer electronics and electric vehicles and are increasingly applied in stationary storage systems. Yet, concerns about safety and reliability remain major obstacles, which must be addressed in order to improve the acceptance of this technology. The gradual degradation of Li-ion cells over time lies at the heart of this problem. Time, usage and environmental conditions lead to performance deterioration and cell failures, which, in rare cases, can be catastrophic due to res or explosions. The physical and chemical mechanisms responsible for degradation are numerous, complex and interdependent. Our understanding of degradation and failure of Li-ion cells is still very limited and more limited yet are reliable and practical methods for the detection and prediction of these phenomena.</p> <p>This thesis presents a comprehensive approach for the diagnosis and prognosis of degradation in Li-ion cells. The key to this approach is the extraction of information on electrode-speci c degradation through open circuit voltage (OCV) measurements. This is achieved in three stages. Firstly, a parametric OCV model is created, which computes the OCV of each electrode. Secondly, a diagnostic algorithm is devised, through which the OCV model is tted to OCV measurements recorded on Li-ion cells at various stages throughout their cycle life. The algorithm identi es the nature and quanti es the extent of degradation experienced by the cells. Lastly, the outputs of the algorithm are used to identify the likely failure modes of the cells and predict their end-of-life.</p> <p>The presented methods improve safe operation and predictions of remaining useful cycle life for commercial Li-ion cells. Greater certainty about the reliability, safety, required maintenance and depreciation of Li-ion battery systems can signi cantly enhance the competitiveness of battery electric storage in both automotive and stationary applications. The ndings presented in this work are therefore not only of technological but also of commercial interest.</p> |
| spellingShingle | Lithium ion batteries Birkl, C Diagnosis and prognosis of degradation in lithium-ion batteries |
| title | Diagnosis and prognosis of degradation in lithium-ion batteries |
| title_full | Diagnosis and prognosis of degradation in lithium-ion batteries |
| title_fullStr | Diagnosis and prognosis of degradation in lithium-ion batteries |
| title_full_unstemmed | Diagnosis and prognosis of degradation in lithium-ion batteries |
| title_short | Diagnosis and prognosis of degradation in lithium-ion batteries |
| title_sort | diagnosis and prognosis of degradation in lithium ion batteries |
| topic | Lithium ion batteries |
| work_keys_str_mv | AT birklc diagnosisandprognosisofdegradationinlithiumionbatteries |