Do you Build AI Models?

Train them with batemo!


The development of data-driven artifi­cial intel­li­gence (AI)-models or algorithms such as neural networks (ANN) or support vector machines (SVM), which use machine learning techniques to learn the linkage between battery in- and output variables, is an exciting but challenging task. It means to create a model that captures the non-correl­a­tive, non-linear and dynamic behavior of a real battery cell by supplying large amounts of data for AI training. Ensuring that the training data are of high quality and cover all operating conditions is essen­tial for building robust AI models. Something that is missing when technology evolves fast – like in the battery business with novel chemistries that all have their own challenges. 
The major problem is that fast, physical and accurate battery models are missing for AI development. As alter­na­tive, testing-based workflows are applied. However, conducting suffi­cient exper­i­ments at the time when the datasets are needed for training is close to impos­sible, as it is expen­sive and time-consuming. This causes major issues when the algorithm trans­fers data deficien­cies to inaccu­rate predic­tions, non-inter­pretability and unsafe opera­tion.
This is true for many aspects of AI development. Let’s make some examples: 
Finding reliable answers to these questions fast is diffi­cult… very difficult. 


You need the ultimate tool for devel­oping your data-driven AI algorithm by giving it access to the best possible data base for training and valida­tion. This is exactly what the Batemo Cell Models can do for you. Batemo’s unique battery modeling technology allows you to develop advanced AI algorithms based on globally validated battery cell models. The under­lying idea is to develop your AI not with measure­ment data, but with the most accurate battery cell models there are. With the Batemo Cell Model as high-fidelity physical core model, you ensure that every­thing during AI development holds true when you move to field opera­tion. By having access to the Batemo Cell Model Library, you ensure that the training of your AI algorithms is consis­tent and robust amongst all your cell types, and that you have the ideal training source for all your cells from day one. By incor­po­rating the Batemo Cell Models into your development, you can unlock the full poten­tial of data-driven approaches to build better AI algorithms with less resources a lot faster.


The Batemo Cell Models run within seconds within a full automa­tion backend. You can generate thousands of training profiles virtu­ally over night, and there­with receive immediate feedback on AI function­ality and quality. 


The Batemo Cell Models are strictly physical and provide access to inner cell quanti­ties. Only if you base your AI development on a physical core model that correctly splits up the under­lying processes, you can enable the AI to predict the perfor­mance of fresh and aged battery cells under all operating conditions. 


The Batemo Cell Models are the most accurate battery cell that exist on the market - guaran­teed! We always demon­strate the validity through exten­sive measure­ments that prove highest accuracy. Only in this way you can ensure that the AI receives validated cell behavior as data base. 
The method­ology we apply is novel and robust and repre­sents a paradigm shift in AI development. It is based on syner­gis­ti­cally combining physics-based Batemo simula­tions, data-driven machine learning algorithms, and testing.
  • 1st

    Get a Batemo Cell Model from the Batemo Cell Model Library or we create a Custom Cell Model specif­i­cally for you. 

  • 2nd

    Integrate the cell model into your preferred simula­tion environ­ment for devel­oping your AI innovations.

  • 3rd

    Use software-in-the-loop development methods to train your AI algorithm based on the Batemo Cell Model as high-preci­sion physical core model. Run fully automated training routines by letting the AI model control the boundary conditions and parame­ters of the cell model simula­tions. Compare the predic­tions of the data-driven model against synthetic valida­tion sets from the high-fidelity physical model to assess accuracy and generalizability.

  • 4th

    As a final step, you move to field opera­tion. Because the Batemo Cell Model is valid, you can expect straight-forward AI opera­tion in the field. 

A training setup for getting the most accurate, yet flexible workflow possible to predict battery aging by connecting an AI Algorithm with the Batemo Cell Model is shown below: 


By using the Batemo Cell Models to make your AI development simula­tion-based and faster, you reduce costs while obtaining better AI algorithms and results. This is how we generate value and contribute to your success. 


Using the Batemo Cell Models you reduce the failure proba­bility of your data-driven algorithm in your AI software by one order of magni­tude. Every day you run thousands of automated test scenarios yielding a highest quality AI. In this way, you harness the full poten­tial of data-driven approaches to optimize battery performance. 


With the Batemo Cell Models your AI development takes a fraction of the time. By having a model as ideal training data source at hand, you avoid spending years into testing and data processing. By getting immediate feedback on the function­ality of your adaptions and improve­ments you avoid re-design loops.

Lower Cost

Conducting exper­i­ments under various conditions to capture the full range of battery behavior is expen­sive. The Batemo Cell Models lower the cost of your AI development by drasti­cally reducing expenses for cell procure­ment, testing and data processing. 


Let’s take the first step!