The Science of Sustainable Mobility
Accelerate battery innovation, optimize materials performance, and digitalize the laboratory of the future with 3D UNIVERSES and Scientific AI.
The 3D UNIVERSE of Transportation & Mobility
Vehicle innovation is no longer a linear engineering task; it is a multi-scale scientific challenge. With BIOVIA, transportation & mobility companies create specialized 3D UNIVERSES—high-fidelity environments where the Virtual Twins of your battery chemistry, laboratory procedures, and mechanical systems converge.
By unifying these worlds together and applying Scientific AI, OEMs can navigate the transition from Lithium-Ion to Solid-State and beyond with unprecedented speed and safety.
Accelerating Next-Gen Battery Chemistries
The push for higher energy density and fire safety is driving the rapid shift toward Solid-State and Sodium-Ion technologies, while next-gen lithium-ion research is also accelerating.
Traditional trial-and-error is too slow. BIOVIA uses Scientific AI and the Virtual Twin of Chemistry to design out risks like thermal runaway at the molecular level.
- In Silico Discovery: Screen billions of electrolyte and electrode combinations virtually to find the "golden formulation."
- Stability Modeling: Use BIOVIA Materials Studio to predict the formation of the Solid Electrolyte Interface (SEI) and mitigate dendrite growth before a physical cell is ever built.
- Generative Chemistry: Leverage Scientific AI to propose novel molecular structures that optimize the trade-off between range, weight, and charging speed.
Digitalizing the "Gigafactory" Lab
As battery production scales to terawatt-hours, laboratory bottlenecks have become the primary threat to time-to-market.
BIOVIA ONE Lab transforms your physical R&D and QA/QC operations with a Virtual Twin of the Lab. By digitalizing every bench task, we enable the "Lab of the Future" for transportation & mobility.
- 80% Faster Screening: Automate formulation workflows and data capture from battery cyclers.
- Unified Data Model: Eliminate silos between the "Virtual World" of design and the "Real World" of testing.
- IoT Connectivity: Directly integrate lab instruments to provide real-time visibility into experimental results, ensuring that every batch meets automotive-grade standards.
The Digital Battery Passport & Compliance
Global mandates (like the EU Battery Regulation) now require a "Digital Battery Passport" for every pack, tracking its material origin and carbon footprint.
Within your 3D UNIVERSE, BIOVIA ensures that compliance is not an afterthought, but a byproduct of your research.
- Traceability by Design: Automatically capture the chemical lineage and genealogy of your materials from the moment of discovery.
- Real-Time Material Tracking: Maintain a barcode-driven, "as-made" chemical lineage from raw material receipt to cell assembly to meet strict audit requirements.
- Automated Genealogy & SDS Management: Instantly link physical substances to safety data and parent-child genealogy, ensuring end-to-end data integrity for regulatory passports.
Circularity & Lifecycle Optimization
Critical mineral scarcity is forcing the industry toward "Circular Engineering"—designing batteries today for the recycling and second-life markets of tomorrow.
BIOVIA enables a closed-loop strategy by connecting the Virtual Twin of the battery to its real-world performance data.
- Second-Life Prediction: Use Scientific AI to analyze degradation patterns and determine which cells are suitable for stationary energy storage after their vehicle life.
- Recycling Simulation: Model the effectiveness of hydrometallurgical or pyrometallurgical recycling processes to maximize the recovery of Lithium, Cobalt, and Nickel.
- Sustainability Insights: Balance economic performance against environmental impact using multi-scale simulation.
Scientific AI: Your Intelligent Co-Pilot
BIOVIA leverages Scientific AI to create a continuous feedback loop. Generative AI suggests new material structures, which are then validated in the physical lab managed by ONE Lab. This data flows back into the virtual model, creating an "active learning" cycle that shortens the path from discovery to the production line.
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