Combining Virtual Reality with Mixed Reality for Efficient Training in Battery Manufacturing

Denisart, Lucie; Zapata‐Dominguez, Diana; David, Xavier; Leclere, Aubin; Lelong, Romain; Liu, Chaoyue; Xu, Jiahui; Loup‐Escande, Emilie; Franco, Alejandro A.

doi:10.1002/batt.202300268

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…The high-quality electrode and CL nanostructures generated by GLIDER offer significant value for educational purposes in fuel cell manufacturing. GLIDER facilitates the exploration of intricate interactions among multiple key material components of CLs through virtual reality, , providing users with a diverse range of nanostructures. While the powerful generation ability of GLIDER has been established, it is important to note that the diversity in generation depends on the input diversity of CL nanostructures.…”

Section: Resultsmentioning

confidence: 99%

Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures

Niu,

Zhao,

Deng

et al. 2024

ACS Nano

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Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and costeffectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vectorvariational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, e.g., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices.

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Section: Resultsmentioning

confidence: 99%

Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures

Niu,

Zhao,

Deng

et al. 2024

ACS Nano

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Breaking Down the Barriers between the Digital and the Real: Mixed Reality Applied to Battery Manufacturing R&D and Training

Denisart,

Troncoso,

Loup‐Escande

et al. 2024

Batteries & Supercaps

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In a scenario in which the manufacturing of high‐performance, safe batteries on an unprecedented large scale is crucial for the energy transition and fight against climate change, research laboratories and cell production industries are facing challenges due to the lack of efficient data management and training tools. In this context, the use of intelligent devices plays an important role on the path towards the optimization of the manufacturing process and the enhancement of the battery performance while reducing production costs. In this Concept, we present an innovative Mixed Reality tool for efficient data collection and training in real‐time in battery research laboratories and battery manufacturing pilot lines, which runs on Microsoft HoloLens 2 glasses. Thanks to this tool, users can collect data while keeping their hands free and receive advice in real time to design and build batteries with tailored properties. This optimizes data management in battery manufacturing environments. Now, thanks to our Mixed Reality application, users can collect data in the place of work, save this data automatically on a server and exploit it to receive advice and feedback to support their decision‐making and learning of the manufacturing process.

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Combining Virtual Reality with Mixed Reality for Efficient Training in Battery Manufacturing

Cited by 2 publications

References 37 publications

Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures

Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures

Breaking Down the Barriers between the Digital and the Real: Mixed Reality Applied to Battery Manufacturing R&D and Training

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