Imagine having a perfect digital clone of a battery that predicts exactly when it will wear out or fail. Instead of guessing, this system uses real-time data and smart math to keep the battery healthy. It is like having a personal trainer for your battery that optimizes every charge and discharge cycle.
Battery systems in transport and energy often suffer from unpredictable aging and high replacement costs. Current management systems lack the precision to maximize the actual life and performance of the cells.
A scalable digital twin system and a laboratory prototype including cell-level sensors and an open-source battery management software platform.
If you are an EV OEM dealing with unpredictable battery degradation — this project developed a digital twin system that can increase battery lifetime by 10% on average. This allows for better warranty management and higher vehicle resale value.
If you are an electric ship operator dealing with high replacement costs for massive battery packs — this project developed a monitoring system that contributes to a lifecycle cost reduction of at least 10%. This ensures more predictable maintenance schedules for sea vessels.
If you are a grid storage provider dealing with efficiency losses during peak loads — this project developed a management system that can provide a 20% performance increase in specific scenarios. This maximizes the amount of green energy stored and delivered to the grid.
The project aims to reduce lifecycle costs by at least 10% through better monitoring and optimized battery operation.
The project is currently integrating an open source platform into a laboratory scale prototype system, suggesting it is in the validation phase before full industrial scaling.
Based on available project data, the project integrates an open source BMS platform, but specific commercial licensing terms for the digital twin are not detailed.
It develops specific hardware and sensorisation at both the cell and system level to collect and communicate measurement data.
The project period runs from May 2023 to October 2026, indicating that final validated results will be available by late 2026.
The consortium is highly balanced for commercialization, consisting of 14 partners across 9 countries. With a 50% industry ratio (7 industrial partners, including 5 SMEs), the project ensures that the research from the 7 research organizations is directly applied to market needs. The presence of SMEs suggests a focus on agile implementation and specialized software deployment.
Contact VTT Research Ltd in Finland for technical inquiries.
Contact us to connect with the BATMAX consortium for early adoption of the digital twin tools.
