Gast ::
| Hauptseite > Publikationsdatenbank > Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| Hauptseite > Publikationsdatenbank > Online capacity estimation of lithium-ion batteries with deep long short-term memory networks |
| Journal Article | FZJ-2020-04838 |
; ; ; ; ;
2021
Elsevier
New York, NY [u.a.]
This record in other databases: 
Please use a persistent id in citations: http://hdl.handle.net/2128/26371 doi:10.1016/j.jpowsour.2020.228863
Abstract: There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specificallyfor the estimation of their state of health, for example, via their remaining capacity. The online estimationof the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputsavailable from a cell under operation. The scope of this work is the development of a data-driven capacityestimation model for cells under real-world working conditions with recurrent neural networks having longshort-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve isused as input, reflecting input availability in the real world. The network achieves a best-case mean absolutepercentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handlevariations in the length of the input time series and can generate a viable estimation even with an incompletecollection of input due to sensor errors. The model validation with several scenarios is done in a local embeddeddevice, highlighting the use case of such models in future battery management systems.
; 
; 
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |
PDF
Fulltext by OpenAccess repository