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Monolith at The International Battery Seminar & Exhibit 2025​

 

Reduce validation testing time and effort. Get to market faster.

Loews Royal Pacific Resort at Universal Orlando
March 17-20 2025
Stand 1018
monolith_AI_tradeshow_IBSE2025

Can We Trust AI for Battery Design and Testing?

Thursday March 20th, 12:00 PM
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AI is transforming industries, offering powerful new tools but also raising debates around regulation and trust. In engineering, AI can streamline processes, enhance innovation, and reduce costs—but is it always appropriate?
In this talk, Marius Andreas Koestler, VP AI for Batteries, explores how AI and machine learning can drive faster time-to-market, improved design, and cost savings, with a focus on battery technology. Through practical examples, he’ll demonstrate AI’s impact on optimising battery performance, energy storage, and predictive maintenance, and discuss future opportunities and challenges for AI in engineering.

Meet our team  

Have technical demos, chats & consultations at our booth
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Arnaud Doko

Solutions Engineer

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Marius Andreas Koestler

VP AI for Batteries

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Meg
Megan Jenkins

Head of Marketing

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About The International Battery Seminar & Exhibit: 

Founded in 1983, the International Battery Seminar & Exhibit has established itself as the premier event showcasing the state of the art of worldwide energy storage technology developments for consumer, automotive, military, grid, and industrial applications. As the longest-running annual battery industry event in the world, this meeting has always been the preferred venue to announce significant developments, new products, and showcase the most advanced battery technology. 

 

About Monolith

 

We enable engineers all over the world to:

 

  • Understand physically intractable problems
  • Fully explore multiple virtual test scenarios
  • Reduce costs and time investment throughout the whole R&D cycle
  • Increase confidence in predictions & recommendations on which tests to run next‍

Ensure battery design quality & safety using AI

 

With the power of AI, you can model battery performance across the design space with a fraction of a traditional test plan. Using the Next Test Recommender, you can apply multiple machine learning algorithms at once to chart your testing path using the fewest steps possible incrementally.

 

Predict the critical tests to run


Test too much and you waste time confirming what you already know. Test too little and you risk missing performance issues. Schedule, quality, and your career depend on finding the balance.

 

  • Run the most important tests and skip the rest
  • Optimize resources spent on costly test rigs and facilities
  • Validate your designs faster with fewer prototype iterations 

Trusted by

BAE Systems
Jota
Aptar
Mercedes
Honda
Siemens
gartner

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