Do you Build AI Models?
Train them with batemo!
Challenge
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:
- 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 imbalanced battery training datasets causing over- or underfitting?
- How can my AI generate interpretable results that provide insights into the physical battery state?
- How do I know if I fed sufficient data so that model predictions are robust?
Finding reliable answers to these questions fast is difficult… very difficult.
Solution - Battery AI Training
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.
Fast
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.
Physical
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.
Accurate
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.
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:
Advantages
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.
Better
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.
Faster
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.
