Predicting Cycle Life of NMC Cells by Discharge Capacity Voltage Curves
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Wigforss, Christoffer
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The biggest issue with rechargeable batteries is arguably their limited lifetime. They
suffer from capacity degradation and power fade, and their performance decreases
as they age. Estimating the remaining useful life is therefore an important task.
However, the complex internal aging mechanisms are difficult to model. Recently,
machine learning has become a promising approach for predicting remaining useful
life. This thesis evaluates whether a new elastic net machine learning model trained
on data from LFP cells can be used to predict cycle life of NMC cells. The model
uses capacity and voltage data during discharge phases to derive a feature highly
correlated to cycle life. Four commercial NMC cells were cycled in Chalmers Electric
Power Battery Lab to collect cycling data. The model was able to make useful cycle
life predictions for these cells, which suggests that the approach is applicable to
other lithium-ion cells.
Beskrivning
Ämne/nyckelord
NMC , cycle life , RUL , prediction , machine learning , elastic net