Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2020
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| _version_ | 1797075624448229376 |
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| author | Li, W Sengupta, N Dechent, P Howey, D Annaswamy, A Sauer, DU |
| author_facet | Li, W Sengupta, N Dechent, P Howey, D Annaswamy, A Sauer, DU |
| author_sort | Li, W |
| collection | OXFORD |
| description | There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems. |
| first_indexed | 2024-03-06T23:52:57Z |
| format | Journal article |
| id | oxford-uuid:733ef28f-bd65-4966-b939-de4759611d74 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:52:57Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:733ef28f-bd65-4966-b939-de4759611d742022-03-26T19:55:07ZOnline capacity estimation of lithium-ion batteries with deep long short-term memory networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:733ef28f-bd65-4966-b939-de4759611d74EnglishSymplectic ElementsElsevier2020Li, WSengupta, NDechent, PHowey, DAnnaswamy, ASauer, DUThere is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems. |
| spellingShingle | Li, W Sengupta, N Dechent, P Howey, D Annaswamy, A Sauer, DU Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title | Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title_full | Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title_fullStr | Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title_full_unstemmed | Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title_short | Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| title_sort | online capacity estimation of lithium ion batteries with deep long short term memory networks |
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