Approaches to simulate battery behaviour and aging in different contexts

<p><strong>About this Webinar:</strong></p>

<p>Batteries are used in various applications with significantly different requirements and many diverse environmental conditions. Predicting&nbsp;the behavior of&nbsp;batteries&nbsp;with&nbsp;different cell chemistries and formats when impacted by these parameters is challenging. Hence, different&nbsp;individual&nbsp;approaches&nbsp;for&nbsp;battery simulations&nbsp;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&nbsp;on the benfits&nbsp;semi-empirical&nbsp;and machine learning models as well as their combination – we call this a hybrid model.</p>

<p><strong>Questions that will be discussed are:</strong></p>

<ul><li>Why do we need battery models?&nbsp;</li><li>What are the advantages and disadvantages of different approaches?&nbsp;</li><li>How can field data be incorporated into models?</li><li>What are the benefits of&nbsp;the TWAICE&nbsp;hybrid model over traditional approaches?</li></ul>

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