Imagine if your phone or car battery had a built-in doctor that could see exactly what's happening inside every cell in real-time. Instead of just guessing how much power is left, this system uses a special electronic 'stethoscope' to detect wear and tear before things break. This means batteries last longer and are much less likely to overheat or fail unexpectedly.
Current Battery Management Systems (BMS) lack the precision to accurately predict battery health and failure, leading to premature replacement and safety risks like thermal runaway.
Two 48V modules featuring zBMS architecture and EIS chips, along with a cloud platform for battery data storage.
If you are an EV manufacturer dealing with battery degradation and range anxiety — this project developed a BMS with EIS chips that can extend battery lifetime by 20%. This allows for more reliable performance and increases the resale value of the vehicle.
If you are a storage provider dealing with the risk of thermal runaway in large battery arrays — this project developed a mechanical swelling model and core temperature estimation. This prevents catastrophic failures and ensures long-term operational safety.
If you are a company dealing with the uncertainty of used battery health for second-life applications — this project developed accurate SoH and RUL prediction models. This ensures financial and social acceptance of extended battery use through reliable performance data.
Based on available project data, there is no specific pricing or cost information provided for the hardware or software solutions.
The project aims to validate solutions for stationary and automotive use cases at TRL 4, meaning it is currently at the laboratory/prototype validation stage rather than full industrial scale.
Based on available project data, specific licensing terms are not mentioned, though the project intends to share data via a cloud platform following FAIR principles.
The system integrates via a zBMS architecture including an EIS chip and advanced microprocessors to improve signal acquisition and computational resources.
The project period runs from 2023-05-01 to 2026-10-31, indicating that final results and validations will be available toward the end of 2026.
The consortium is highly industry-weighted with a 50% industry ratio (4 out of 8 partners), suggesting a strong focus on commercial viability. It spans 5 countries (AT, BE, CH, DE, IT), combining the academic research of 2 universities and 1 research center with the practical implementation capabilities of industrial tier-1 members.
Contact Vrije Universiteit Brussel for technical inquiries regarding the zBMS architecture.
Contact SciTransfer to connect with the NEMO consortium for early-stage licensing of EIS-based BMS tools.
