Periodic Reporting for period 1 - iBattMan (Smart, Connected and Secure Battery Management System Enhanced by Next Generation Edge and Cloud Computing, Sensors and Interoperable Architecture)
Berichtszeitraum: 2024-01-01 bis 2025-06-30
iBattMan aims to design an innovative, modular, and scalable BMS for a wide range of vehicles, from small passenger cars to e-buses and electric trucks, with improved performance, connectivity, security, and reliability to enhance battery performance and reduce total cost of ownership in EV applications and smart battery use for grid support and in 2nd life applications, based on a holistic design of an interoperable architecture, and supported by a suite of advanced sensors and edge- and cloud-computational resources.
The project is:
i) developing new technologies in the field of advanced sensors, communication, edge- and cloud computing, and bi-directional charging, which combined allow to create a new concept of standardized, modular and scalable BMS for electromobility and smart cities applications, enabling improved performance, autonomy, safety, levelized cost of storage (LCoS) of EVs, and grid services to support a higher penetration of renewable energy on the grid and the development of Smart Cities and Net Zero Building concepts;
ii) creating new technologies and processes, to facilitate EV battery packs reuse in second life applications and further recycling at its end-of-life (EoL), contributing to a circular and integrated supply chain in the EU for the fabrication of battery packs, as well as effective and sustainable models for urban electromobility;
iii) demonstrating the effectiveness of the solution in relevant use cases for electromobility and 2nd life storage, and providing a blueprint for stakeholders to customize the technologies developed in iBattMan for each case.
To achieve these ambitious targets, iBattMan combines innovative technologies in electrochemistry, advanced modeling, sensors, electronics, communications, edge and cloud computing, the Internet of Things (IoT), Big Data, Artificial Intelligence, and Machine Learning (AI/ML).
The project is:
i) developing new technologies in the field of advanced sensors, communication, edge- and cloud computing, and bi-directional charging, which combined allow to create a new concept of standardized, modular and scalable BMS for electromobility and smart cities applications, enabling improved performance, autonomy, safety, levelized cost of storage (LCoS) of EVs, and grid services to support a higher penetration of renewable energy on the grid and the development of Smart Cities and Net Zero Building concepts;
ii) creating new technologies and processes, to facilitate EV battery packs reuse in second life applications and further recycling at its end-of-life (EoL), contributing to a circular and integrated supply chain in the EU for the fabrication of battery packs, as well as effective and sustainable models for urban electromobility;
iii) demonstrating the effectiveness of the solution in relevant use cases for electromobility and 2nd life storage, and providing a blueprint for stakeholders to customize the technologies developed in iBattMan for each case.
To achieve these ambitious targets, iBattMan combines innovative technologies in electrochemistry, advanced modeling, sensors, electronics, communications, edge and cloud computing, the Internet of Things (IoT), Big Data, Artificial Intelligence, and Machine Learning (AI/ML).
During the first reporting period, we identified key requirements, standards, and design-driving use cases across a range of e-mobility and stationary applications, and adapted a specific BMS to meet a partner vehicle's requirements and architecture. A preliminary BMS architecture was designed for a particular vehicle, but the interfaces have been designed general enough to be validated on two different battery packs.
We have
The firmware design to include advanced functionalities has been performed in WP3, starting with the general specifications for the BMS system integration, and continuing with defining the BMS measurement at cell/module-level and pack-level sensors – to focus not only on battery status monitoring for the SoX algorithms but also to supervise the battery status under hazardous scenarios, i.e. thermal runaway.
The BMS interfaces to support cloud connectivity, bi-directional interaction with charging infrastructure, integration with other ECUs, and compatibility with devices that support second-life applications have been defined and a multi-sensing unit has been developed that can incorporate all new sensors dedicated to providing fast and reliable action.
A database has been created to record new sensor information. Preliminary adaptable models independent of cell chemistry have been investigated based on a novel workflow based on algorithms able to translate specific physics-based models into machine learning models.
Value-added services, such as battery tests (voltage, current, internal resistance), normal tests, complex tests such as electrochemical impedance tests, charging and discharging modes and V2X services such as frequency regulation in grid forming scenarios to maximize revenue in energy arbitrage and peak shaving scenarios have been defined and integrated to facilitate coordination between electric vehicles and the energy infrastructure, promoting more sustainable operation of the electrical system.
We have
The firmware design to include advanced functionalities has been performed in WP3, starting with the general specifications for the BMS system integration, and continuing with defining the BMS measurement at cell/module-level and pack-level sensors – to focus not only on battery status monitoring for the SoX algorithms but also to supervise the battery status under hazardous scenarios, i.e. thermal runaway.
The BMS interfaces to support cloud connectivity, bi-directional interaction with charging infrastructure, integration with other ECUs, and compatibility with devices that support second-life applications have been defined and a multi-sensing unit has been developed that can incorporate all new sensors dedicated to providing fast and reliable action.
A database has been created to record new sensor information. Preliminary adaptable models independent of cell chemistry have been investigated based on a novel workflow based on algorithms able to translate specific physics-based models into machine learning models.
Value-added services, such as battery tests (voltage, current, internal resistance), normal tests, complex tests such as electrochemical impedance tests, charging and discharging modes and V2X services such as frequency regulation in grid forming scenarios to maximize revenue in energy arbitrage and peak shaving scenarios have been defined and integrated to facilitate coordination between electric vehicles and the energy infrastructure, promoting more sustainable operation of the electrical system.
A cloud-based BMS using an IoT platform (IOTP) has been designed based on the system requirements defined in WP2, and the physical sensors for monitoring key battery parameters selected in WP2, and an initial version of the user interface has been developed. The implementation of communication interfaces with the vehicle through a cloud gateway, based on the architecture defined in WP3, and the integration of ML/AI models were also addressed.
A secure and decentralised access management for battery data within electric vehicles has been implemented to ensure a safe and reliable connection between the BMS and the ECU.
A first set of models has been developed for implementation on the BMS for onboard SoX estimation, based on a reduced-order physics-based ECM model with neural network components that learn to correct model residuals or capture parameter drift over time.
A security analysis has been performed, and a system-wide security architecture has been developed based on research into suitable update frameworks for integration into the project.
A secure and decentralised access management for battery data within electric vehicles has been implemented to ensure a safe and reliable connection between the BMS and the ECU.
A first set of models has been developed for implementation on the BMS for onboard SoX estimation, based on a reduced-order physics-based ECM model with neural network components that learn to correct model residuals or capture parameter drift over time.
A security analysis has been performed, and a system-wide security architecture has been developed based on research into suitable update frameworks for integration into the project.