Energetic seeks to develop a next generation BMS using a hybrid of multiple AI and physics-based models to optimize battery utilization in the first (transportation) and second life (stationary). The project will use enhanced sensing technologies, cloud and edge control integration, and investigate explainable AI models as methods to achieve this.
The developed models will be integrated into a hybrid (AI and physics-based) digital twin-based BMS model, which will be later deployed on BMS hardware and tested with a real battery stack. The developed BMS should be able to cover at least 60% of use cases locally, and 90% of desired use cases via connected computation (Edge/Cloud).
Typhoon’s role as a project partner
Typhoon HIL is leading real-time testing activities of the developed Smart Digital Twin BMS software prior to and after its subsequent integration in the BMS hardware. These tests will be connected between a BMS testbed and BMS hardware (the BMS “edge”) hosted at Typhoon HIL’s R&D headquarters and the cloud-based BMS software where more complex AI-driven calculations can be performed. By validating performance in the C-HIL testbed, the battery should be ready for stack level demonstration testing at the end of the project.