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Abstrakt
The resistance offers insight into the efficiency and power capability of Lithiumion (Liion) batteries. That is, it can describe the performance of the batteries. However, as with other performance parameters of Liion batteries, the resistance is dependent on the operating conditions and the age of the battery. Traditionally, to capture these dependencies, Liion cells are aged at different conditions by applying synthetic mission profiles, which are periodically stopped to measure the resistance at standard conditions. Even though accurate information about the
resistance behaviour are obtained, the measurements are timeconsuming. Therefore, we extract the resistance directly from a dynamic reallife profile. The extracted resistance is modelled as function of the stateofcharge (SOC). The parameters of the model are allowed to vary over time to account for increase in the resistance as the battery ages. In order to capture the variation in time of the parameters of the loglinear model are assumed to follow a vector autoregressive (VAR) model. The estimated VAR is used to predict the long term behaviour of the expected internal resistance. The prediction of the long term behaviour will enable the calculation of the remaining useful life of the battery, allowing for the inclusion of future battery usage through the SOC.
resistance behaviour are obtained, the measurements are timeconsuming. Therefore, we extract the resistance directly from a dynamic reallife profile. The extracted resistance is modelled as function of the stateofcharge (SOC). The parameters of the model are allowed to vary over time to account for increase in the resistance as the battery ages. In order to capture the variation in time of the parameters of the loglinear model are assumed to follow a vector autoregressive (VAR) model. The estimated VAR is used to predict the long term behaviour of the expected internal resistance. The prediction of the long term behaviour will enable the calculation of the remaining useful life of the battery, allowing for the inclusion of future battery usage through the SOC.
Originalsprog  Engelsk 

Titel  IPEMC 2020ECCE Asia 
Status  Accepteret/In press  2020 
Fingeraftryk Dyk ned i forskningsemnerne om 'A TimeVarying Loglinear Model for Predicting the Resistance of Lithiumion Batteries'. Sammen danner de et unikt fingeraftryk.
Projekter
 1 Afsluttet

Cloud BMS: Cloud BMS  The new generation of intelligent battery management systems
Stroe, D., Kær, S. K. & Vilsen, S. B.
01/01/2018 → 30/09/2020
Projekter: Projekt › Forskning