How is AI Battery Simulation integrated into existing test environments?

Integration takes place in stages via existing test benches and data interfaces, allowing existing systems to continue to be used. Initially, selected cell chemistries are integrated, before deployment is gradually expanded using stable data flows and established workflows.

What data is required for reliable end-of-life predictions?

Cycle tests from laboratories and validation test benches form the foundation and are supplemented by the test runs contained in the pre-trained model. In combination with physical models, this creates a consistent database that improves prediction quality and enables a nuanced evaluation of different cell chemistries.

For which industries and teams is AI Battery Simulation particularly well-suited?

The solution is designed for companies in the automotive and manufacturing industries that develop complex products with a wide range of variants. Development and validation teams that regularly make decisions based on extensive test and simulation data and want to accelerate their processes will benefit in particular.

AI Battery Simulation

Shorten validation cycles for new battery cells from many months to just a few weeks and make reliable decisions early on in development and procurement.

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Circuit board with a glowing blue battery icon representing digital energy simulation.

Challenge: Slow Battery Cell Development

In battery cell development, companies face a variety of challenges—from long validation cycles and high manual effort to slow time-to-market:

Long validation cycles

A single validation run for a new cell chemistry ties up test benches, skilled personnel, and budget resources for many months. Today, people often speak of 12 to 18 months.

Late transparency

The complete test matrix is not available until key design and sourcing decisions should already have been made—that is, only late in the development cycle.

Significant manual effort

P2D and ECM models are parameterized manually, which is unnecessarily time-consuming due to a lack of automation.

Delayed market launch

Slow decision-making—for example, regarding design, approval, and procurement—leads to high testing costs and delays in bringing new products to market.

Measurable Value: Make Decisions Faster, Reduce Costs

Use AI-powered battery simulation to understand the aging state of cells earlier and reduce development risks. Make design and sourcing decisions based on digital predictions rather than solely on late-stage test results. Reduce manual modeling effort and increase the efficiency of your development and validation teams.

Earlier Decisions: Make reliable end-of-life assessments based on early simulation results.
Less manual effort: Reduce modeling and validation work through AI-supported analysis.
Faster time-to-market: Accelerate design, approval, and procurement decisions.
Lower development costs: Reduce testing costs through more targeted test planning and digital predictions.

Solution: AI-powered end-of-life prediction after 50 cycles

AI Battery Simulation enables the prediction of cell end-of-life characteristics after just about 50 cycles. It is based on a pre-trained AI model that draws on more than 4,000 cycle tests and physical models to replicate real-world aging processes. Test data, physics-based models, and AI models are integrated into a single environment, creating a consistent data foundation for development and validation.

On this basis, aging trajectories can be evaluated early on, and different cell chemistries can be systematically compared. The combination of data-driven and physics-based approaches increases the reliability of predictions and supports a well-founded evaluation of variants. At the same time, reliance on lengthy test series is reduced, as simulation results provide reliable insights into the cells’ future behavior even in early development phases.

Competitive Advantage: Faster Development and Informed Decisions

The combination of AI models and an integrated database enables more efficient battery development and supports well-informed decisions throughout the development process.

Faster innovation cycles

Shorten development times with simulation results available early on.

Improved data foundation

Link test data, physical models, and AI predictions.

More Efficient Collaboration

Use a common foundation for your development and validation.

Technology partnership

Benefit from the combination of Sphere Energy APEX and Schwarz Digits’ infrastructure.

The Foundation: STACKIT and APEX

Rely on the combination of Sphere Energy APEX and Schwarz Digits’ infrastructure to integrate AI battery simulation into your IT and OT landscape. Take advantage of an environment designed for data- and compute-intensive applications.