Approaches to simulate battery behavior and aging in different contexts

Batteries are used in various applications with significantly different requirements and many diverse environmental conditions. Predicting the behavior of batteries with different cell chemistries and formats when impacted by these parameters is challenging. Hence, different individual approaches for battery simulations exist. Combining some of these individual approaches can have a strong impact on the overall model performance. In this webinar, we will shed some light on the benefits semi-empirical and machine learning models as well as their combination – we call this a hybrid model.

Questions that will be discussed are:

• Why do we need battery models?
• What are the advantages and disadvantages of different approaches?
• How can field data be incorporated into models?
• What are the benefits of the TWAICE hybrid model over traditional approaches?

REGISTER NOW!