Imagine if your phone or car battery had a brain that truly understood its internal chemistry in real-time. Instead of using generic safety margins that waste battery capacity, this system uses a mix of physics and AI to squeeze every bit of energy out of the cells. It's like having a professional mechanic monitoring the battery's health every second to make it last longer and charge faster.
Current battery management systems use conservative safety boundaries that waste usable energy and limit charging speeds. This leads to shorter effective battery life and slower time-to-market for new battery chemistries.
A hybrid BMS hardware and software solution combining on-board physics models with cloud-based AI for precise battery state estimation.
If you are an EV OEM dealing with conservative battery limits that reduce vehicle range — this project developed a physics-based BMS that increases the usable SoC window. This allows drivers to use more of the battery's actual capacity without risking safety.
If you are a storage provider dealing with the high cost of replacing degraded battery packs — this project developed a system for second-life management. This enables the safe and efficient reuse of batteries after their first life in vehicles.
If you are a cell manufacturer dealing with long development cycles for new chemistries — this project developed a scalable parameterization method. This shortens the time-to-market for new battery packs by requiring minimal prior knowledge of the cells.
Based on available project data, specific pricing is not mentioned, but the project aims to reduce overall costs through more efficient use of materials and optimized battery utilization.
Yes, the project includes 10 industrial partners and aims to raise the technology to TRL 5-7 and eventually TRL 8-9 for integration into future battery packs.
The project partners intend to establish commercial relations with at least 5 suppliers and 3 OEMs, specifically for the licensing of IP and technology.
The solution consists of a hybrid hard- and software approach where physics-based models run both on-board the BMS and in the cloud.
The project runs from June 2023 to May 2027, with industrial partners planning to advance the TRL levels in the two years following the project.
The consortium is heavily industry-driven, with 10 out of 15 partners being industrial entities (67% ratio). This strong commercial presence, spanning 7 countries, suggests a high focus on market viability and direct integration into production lines rather than purely academic research.
Contact AIT Austrian Institute of Technology GmbH for licensing inquiries.
Contact us to connect with the NEXTBMS consortium for early adoption of advanced BMS software.
