Methodology

BATMAX sets out to pave the way for advanced
next generation data-based and adaptable battery
management systems capable of fulfilling the
needs and requirements of various mobile and
stationary applications and use cases.

BATMAX develops a framework to efficiently
parameterise physics-based models. Advanced
numerical methods accelerate the extraction of
relevant parameters from experimental and
numerical simulation data.

BATMAX develops hardware and sensorisation
on cell and system level for collection and
communication of battery measurement data
and integrates an open source BMS platform
to a laboratory scale prototype system.

The BATMAX BMS framework (hardware and
software) will enable to exploit advanced battery
models with integrated digital twin framework
that is capable to cope with high amount of
measured data, which will enable to monitor the
battery aging in depth and to facilitate the key
functions of systems.

To achieve models that can handle data in real-time, the physics-based
models will be complemented by data driven approaches towards hybrid
models, which are then implemented in the digital twins. In addition,
AI-driven prognostic models covering battery state estimations will be
developed. Both kinds of models, hybrid and AI driven, will be tailored
for computing at edge or cloud level and will utilise data from sensors
and from physical models.

METHODOLOGY FRAMEWORK

Multi-Scale Modelling Framework

shape

Physics-driven
Models

Digital Twins and Framework Validation

Digital Twin
arrow

Model Fusion

shape

Hybrid Models

arrow

Simulation Data

arrow
arrow

Pre-processed Data

arrow

Data

arrow

Raw
Data

Data Processing
& Data Platform

arrow

Pre- processed Data

Data-Driven Model Development

shape

Data-Driven
Models