Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes
State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/7/364 |
| _version_ | 1797590293796618240 |
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| author | Steven Neupert Julia Kowal |
| author_facet | Steven Neupert Julia Kowal |
| author_sort | Steven Neupert |
| collection | DOAJ |
| description | State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations. |
| first_indexed | 2024-03-11T01:18:31Z |
| format | Article |
| id | doaj.art-52f442a7411848c2a76d5c46cf3ee23b |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T01:18:31Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-52f442a7411848c2a76d5c46cf3ee23b2023-11-18T18:18:56ZengMDPI AGBatteries2313-01052023-07-019736410.3390/batteries9070364Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking PurposesSteven Neupert0Julia Kowal1Department Electrical Energy Storage Technology, Technische Universität Berlin, Einsteinufer 11, 10587 Berlin, GermanyDepartment Electrical Energy Storage Technology, Technische Universität Berlin, Einsteinufer 11, 10587 Berlin, GermanyState estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.https://www.mdpi.com/2313-0105/9/7/364SOCSOHdatasetageingmodelestimation |
| spellingShingle | Steven Neupert Julia Kowal Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes Batteries SOC SOH dataset ageing model estimation |
| title | Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes |
| title_full | Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes |
| title_fullStr | Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes |
| title_full_unstemmed | Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes |
| title_short | Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes |
| title_sort | model based state of charge and state of health estimation algorithms utilizing a new free lithium ion battery cell dataset for benchmarking purposes |
| topic | SOC SOH dataset ageing model estimation |
| url | https://www.mdpi.com/2313-0105/9/7/364 |
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