SSALT-Batt

 

SSALT-Batt: Step-stress accelerated life testing experiments for batteries

In this project we aim at developing optimal step-stress accelerated lifetime testing (SSALT) experiments to predict lifetimes of batteries, while controlling the duration of the experiments and the quality of inferential results for the batteries lifetime under normal operation conditions (NOC). An accurate link model that extrapolates the estimations and predictions from accelerated to NOC will be specified through extensive simulation studies and experiments with different types of batteries. The fundamental knowledge for getting orientation to set-up the simulations framework and the follow-up experiments is aimed to be gained in our approach. Moreover, based on real test data, the quantification of the advantages of SSALT with respect to test duration compared to existing constant stress ALT procedures and the accuracy of the associated statistical methods will be studied.

Contact

Moritz Teuber

Name

Moritz Teuber

Head of Section Modeling, Analytics and Lifetime Prediction

Telephone

work Phone
+49 241 80 99616

E-Mail

 

The importance of lithium-ion batteries in areas like electrified transportation and storage of sustainable energy is undoubtful. Their lifetime and lifetime prediction in real application is crucial for reliable operation and successful market introduction. As ageing tests using normal operation conditions (NOC) are very time and cost intensive, accelerated life testing (ALT) experiments are a powerful alternative, widely used to determine the lifetime distributions of highly reliable products. Step-stress ALT experiments (SSALT) form an essential part of ALT procedures. Under a SSALT experiment, the stress level is increased at intermediate time points of the experiment. An associated lifetime-stress relationship is needed to predict the product’s reliability under NOC.

 

Duration

1st January, 2018 to 31st December, 2018

 
 

Funding

Federal Ministry of Education and Research
 
 

Partners

RWTH