Periodic Reporting for period 1 - BATMAX (Battery management by multi-domain digital twins)
Reporting period: 2023-05-01 to 2024-10-31
The transportation sector is responsible for roughly one-quarter of the total greenhouse emissions in the EU with road vehicles contributing to over 60% of the emissions. Besides transport, an equally large transition from fossil towards green energy is on-going as essential parts of the European Green Deal. Pollution from shipping causes more than 50 000 premature deaths in Europe annually. Electric vehicles change the value and supply chains of vehicle industries dramatically. Therefore, the EU has initiated a wide range of actions across the value chain to create an European industrial base for batteries as well as the relevant research and innovation ecosystems. Batteries are a key element in the work programme for cross-sectoral solutions for climate transition, enabling the rollout of zero-emission mobility and renewable energy.
The project BATMAX paves the way for advanced next generation data-based and adaptable battery management systems capable of fulfilling the needs of various mobile and stationary applications and use cases. Given the role of battery systems as a key enabling technology within the green shift in transport, mobility and energy, it is evident that multiple combinations of requirements, use cases, duties and businesses are placed upon battery systems.
The main objective of the project is to contribute to improving battery system performance, safety, reliability, service life, lifetime cost and therefore to maximise the value created by operation of the battery systems in various kinds of end use applications. BATMAX creates a framework for next generation of battery management based on large amounts of data, both experimental, operational and synthetic, adaptable physics-based models, suitable reduced-order models for both physical BMS algorithms and real-time multi-scale digital twins.
The project BATMAX paves the way for advanced next generation data-based and adaptable battery management systems capable of fulfilling the needs of various mobile and stationary applications and use cases. Given the role of battery systems as a key enabling technology within the green shift in transport, mobility and energy, it is evident that multiple combinations of requirements, use cases, duties and businesses are placed upon battery systems.
The main objective of the project is to contribute to improving battery system performance, safety, reliability, service life, lifetime cost and therefore to maximise the value created by operation of the battery systems in various kinds of end use applications. BATMAX creates a framework for next generation of battery management based on large amounts of data, both experimental, operational and synthetic, adaptable physics-based models, suitable reduced-order models for both physical BMS algorithms and real-time multi-scale digital twins.
BATMAX develops a framework to efficiently parameterise physics-based models is essential to reduce the cost of model development and encourage their use in BMSs. 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. A central output is an extensive multi- purpose and scalable digital twin framework is developed and validated for advanced battery management. Key impacts from BATMAX contribute to 10% battery lifetime increase on average scenario, 20% performance increase in specific scenarios and contribution to lifecycle cost reduction by at least 10%.
The BATMAX project has made significant progress during the first reporting period, with several key deliverables and milestones achieved as planned. The development of the framework for efficient parameterization of physics-based models has shown promising results, with high accuracy in model simulations. Hardware and sensorisation for battery measurement data collection are progressing well, with initial versions of sensing boards and communication drivers already developed. Hybrid and AI-driven models for optimizing battery management are under development and testing, showing good initial results. The project has also made strides in developing a multi-scale digital twin framework for dynamic battery management, with offline testing of models currently underway. Overall, the project is on track to meet its objectives and deliver impactful results in the next reporting period.
The BATMAX project has made significant progress during the first reporting period, with several key deliverables and milestones achieved as planned. The development of the framework for efficient parameterization of physics-based models has shown promising results, with high accuracy in model simulations. Hardware and sensorisation for battery measurement data collection are progressing well, with initial versions of sensing boards and communication drivers already developed. Hybrid and AI-driven models for optimizing battery management are under development and testing, showing good initial results. The project has also made strides in developing a multi-scale digital twin framework for dynamic battery management, with offline testing of models currently underway. Overall, the project is on track to meet its objectives and deliver impactful results in the next reporting period.
Framework for efficient parametrisation of physics-based models: BATMAX is to further develop the open source physics-based code BattMo and establish and parameterise the multi-scale modelling framework to accurately describe the performance of battery cells, both at the beginning-of-life and at various states of health and investigating efficient processes and minimum viable datasets for accurate parametrisation. An on-going activity is also to bridge the approach from battery cells towards module and systems including multi-physical coupled phenomena.
Hardware and sensorisation on cell and system level for collection and communication of battery measurement data: BATMAX develops the next generation BMS hard- and software and provides the software stack for the evaluation of novel battery system level sensors. This additional sensor data will be collected and evaluated on the BMS by algorithms, that can be implemented on the BMS in an IP secure way. Furthermore, the BMS is implemented as an IOT native device, enabling the data transfer to the cloud, providing the additional computational power to utilise the battery energy most efficient. Battery measurement data is the key to accurate battery models, from physics-based ones to artificial intelligence driven ones. It is therefore crucial that this measurement data is gathered under reliable and repeatable conditions and the whole dataset is verifiable. Activity creates workflow and documentation guidelines that enable the usage and sharing of battery measurement data for modelling with confidence in the accurateness of the data.
Hybrid and AI-driven models to optimise lifetime and management of the battery (BMS): BATMAX is to set up, in collaboration with project partners, a joint data platform for the project to enable full scale data-driven research. This includes tools for data pre-processing and data fusion. To complement the physics-based models by data-driven approaches towards hybrid models for further inclusion in the scalable and modular digital twins. The ambistion of the activity is to develop AI-driven prognostics for the BMS, covering SoX estimation for local operation (edge-level), and even to take a step into research of a self-learning BMS that utilises data from cloud-level for optimal battery pack operation, therefore pointing to bidirectional edge-cloud framework.
Adaptable battery management with multi-scale battery digital twin framework for dynamic operation: BATMAX pushes the development of a digital twin framework for design, optimisation and validation of Advanced Battery Management System for batteries for various applications. This enables addressing strategies for optimal operation and longest lifetime of the energy storage system should be flexible applicable to several use cases and operational profiles. For development, a multi-scale hardware-in-the-loop methodology for cell-to-cell battery management is developed including safety and abuse scenarios. BATMAX will later validate the framework against operational data from real reference operation cases.
Hardware and sensorisation on cell and system level for collection and communication of battery measurement data: BATMAX develops the next generation BMS hard- and software and provides the software stack for the evaluation of novel battery system level sensors. This additional sensor data will be collected and evaluated on the BMS by algorithms, that can be implemented on the BMS in an IP secure way. Furthermore, the BMS is implemented as an IOT native device, enabling the data transfer to the cloud, providing the additional computational power to utilise the battery energy most efficient. Battery measurement data is the key to accurate battery models, from physics-based ones to artificial intelligence driven ones. It is therefore crucial that this measurement data is gathered under reliable and repeatable conditions and the whole dataset is verifiable. Activity creates workflow and documentation guidelines that enable the usage and sharing of battery measurement data for modelling with confidence in the accurateness of the data.
Hybrid and AI-driven models to optimise lifetime and management of the battery (BMS): BATMAX is to set up, in collaboration with project partners, a joint data platform for the project to enable full scale data-driven research. This includes tools for data pre-processing and data fusion. To complement the physics-based models by data-driven approaches towards hybrid models for further inclusion in the scalable and modular digital twins. The ambistion of the activity is to develop AI-driven prognostics for the BMS, covering SoX estimation for local operation (edge-level), and even to take a step into research of a self-learning BMS that utilises data from cloud-level for optimal battery pack operation, therefore pointing to bidirectional edge-cloud framework.
Adaptable battery management with multi-scale battery digital twin framework for dynamic operation: BATMAX pushes the development of a digital twin framework for design, optimisation and validation of Advanced Battery Management System for batteries for various applications. This enables addressing strategies for optimal operation and longest lifetime of the energy storage system should be flexible applicable to several use cases and operational profiles. For development, a multi-scale hardware-in-the-loop methodology for cell-to-cell battery management is developed including safety and abuse scenarios. BATMAX will later validate the framework against operational data from real reference operation cases.