Do you Build AI Models?
Train them with batemo!
Challenge
- Does my AI algorithm track the state of charge correctly, even under extreme conditions?
- How accurately does my AI estimate the state of health?
- How do I ensure that my AI is not biased from imbalÂanced battery training datasets causing over- or underfitting?
- How can my AI generate interÂpretable results that provide insights into the physical battery state?
- How do I know if I fed suffiÂcient data so that model predicÂtions are robust?
Solution - Battery AI Training
Fast
Physical
Accurate
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Get a Batemo Cell Model from the Batemo Cell Model Library or we create a Custom Cell Model specifÂiÂcally for you.
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Integrate the cell model into your preferred simulaÂtion environÂment for develÂoping your AI innovations.
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Use software-in-the-loop development methods to train your AI algorithm based on the Batemo Cell Model as high-preciÂsion physical core model. Run fully automated training routines by letting the AI model control the boundary conditions and parameÂters of the cell model simulaÂtions. Compare the predicÂtions of the data-driven model against synthetic validaÂtion sets from the high-fidelity physical model to assess accuracy and generalizability.
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As a final step, you move to field operaÂtion. Because the Batemo Cell Model is valid, you can expect straight-forward AI operaÂtion in the field.