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Understanding the internal workings of a battery is crucial to maximize its lifespan. Experts use “aging models” to replicate how a battery degrades throughout its lifetime. These models use mathematical equations to calculate lifetime characteristics depending on how the battery is used. But upon closer inspection, several different modeling approaches exist. This article compares four common aging models to predict battery lifetime.
Battery lifetime is a concern that many people share. Whether it’s a smartphone, home storage battery, or electric vehicle, there is a nagging worry that the battery will not last as long as expected. The key to getting the most life out of a battery is knowing what is happening within the battery. As soon as we understand the influencing factors and quantify their impact on the degradation of the battery, we can find the most suitable countermeasure to aging. This is where the different aging models to predict battery lifetime come into play.
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First things first: All these modeling approaches have their pros and cons. None of these approaches is generally superior to all the others. So, in order to find the best fit it is important to evaluate the target use case. We, at ACCURE, make sure we use the right modeling approach under the right conditions.
The benefit of the mechanistic model is the compromise that it represents. On the one hand, it provides a deeper understanding of what is happening inside the battery while it is aging. While on the other hand, it does not require complex parametrization. The mechanistic model is a compromise. It provides accuracy and insights without the complexity of a physical-chemical aging model.
A mechanistic model breaks down aging from cell level to electrode level. It specifies in which ranges the negative and positive electrodes are utilized when the cell is operated. The mechanistic model is also able to imitate any arbitrary combination of loss of lithium inventory (LLI), loss of active material on the negative electrode (LAMNE), and loss of active material on the positive electrode (LAMPE). Moreover, it can detect when an electrode is too close to an unsafe operating range. A popular example is the detection of thermodynamic lithium plating on the negative electrode.
Mechanistic modeling is anything but a trivial task. Recently, we collaborated with Matthieu Dubarry (Ph.D.) and the Hawai'i Natural Energy Institute (HNEI) to push the boundaries of mechanistic modeling. The results of this collaboration can be found in our recent open-access publication.
Interestingly, we found that most state-of-the-art mechanistic models are inaccurately modeling the loss of active material on the positive electrode.
More importantly, though, we propose the necessary changes to fix this issue.
Through our work with leading battery researchers, ACCURE is helping the world better understand lithium-ion batteries. By building better mechanistic models, we will all get a little more life out of our batteries.
Matthias helps customers gain actionable insights into their battery systems. As Senior Battery Expert and leader of the battery expert team at ACCURE he is responsible for the development of leading-edge battery diagnostics and analytics. As an accomplished systems engineer, Matthias has extensive experience in automotive battery system development. He holds a Master of Science in Electrical Engineering, Information Technology and Technical Computer Science. In his free time, he enjoys sports including soccer, skiing, snowboarding, and jogging.