Beyond the Test Bench - The Case for Trusting AI Over the Test Bench

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Abstract

As the global transition to electrification accelerates, battery development has become the primary bottleneck of industrial innovation. Traditional validation, rooted in physical testing and Design of Experiments (DoE), is failing to keep pace with the exponential complexity of modern battery chemistry and the pressure from the competitors. This paper introduces a paradigm shift: the Triple-Check Framework. By leveraging Physics-Informed Foundation AI Models, we propose a certification protocol that transforms AI from a "black-box" assistant into a primary validation tool, capable of providing on par statistical safety than traditional laboratory testing. Consequently, this methodology offers a strategic path to market by accelerating development timelines and improving cost-efficiency through the systematic optimization of laboratory resources

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