Accurate predictions of lithium-ion battery life
[...]methods for assessing battery health are becoming increasingly important. [...]the models did not involve slow test cycles, or require any assumptions to be made about the chemistry and degradation mechanisms occurring in the batteries, which had been the case in previously reported studies tha...
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| Veröffentlicht in: | Nature (London) 2019-04, Vol.568 (7752), p.325-326 |
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| container_title | Nature (London) |
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| creator | Berecibar, Maitane |
| description | [...]methods for assessing battery health are becoming increasingly important. [...]the models did not involve slow test cycles, or require any assumptions to be made about the chemistry and degradation mechanisms occurring in the batteries, which had been the case in previously reported studies that used machine learning6. [...]their approach for predicting cycle life complements all previously used approaches. |
| doi_str_mv | 10.1038/d41586-019-01138-1 |
| format | Article |
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| identifier | ISSN: 0028-0836 |
| ispartof | Nature (London), 2019-04, Vol.568 (7752), p.325-326 |
| issn | 0028-0836 1476-4687 |
| language | eng |
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| source | Nature; Springer Nature - Complete Springer Journals |
| subjects | Alternative energy sources Artificial intelligence Batteries Electric vehicles Electricity Energy consumption Life prediction Lifetime Lithium Lithium-ion batteries Organic chemistry Rechargeable batteries Renewable resources |
| title | Accurate predictions of lithium-ion battery life |
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