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!