HealthBatt – Intelligent sensor technology for the condition monitoring of battery storage systems to predict the lifetime and increase the operational reliability

In the project HealthBatt, a multi-sensor approach is being developed to comprehensively record and analyze stress profiles in battery storage systems. By combining damage analysis and AI-based data evaluation, the service life of battery systems can be extended and the safety of use in case of overload can be increased.

Current battery management systems in electrified vehicles mostly rely on thermal and electrical measurements to assess the state of the battery storage. In order to consider additional loads, such as shocks, mechanical vibrations, and humidity and to provide a more accurate estimation of the remaining useful lifetime of battery systems, the research project HealthBatt aims at a comprehensive sensor concept.

Project objective

With this new multi-sensor approach, the damage analysis, and an intelligent data evaluation, the remaining useful lifetime of battery storages can be determined more precisely, unexpected failures can be reduced, and a second life application as a stationary system can be enabled.

Joining and integration concept

The integration in new developments and existing products will be enabled through minimal space requirements and an automated joining of the sensors. As a contactless and mostly maintenance-free tool, the iwb will investigate the use of laser beam welding for the mechanical and electrical integration of battery cells and sensor components into the storage housing. A full integration concept for the sensors of the project partners will be developed, incorporating innovative process monitoring and collaborative robotics.

Damage analysis and data processing

The load histories recorded by the sensors in field tests are subsequently correlated with the identified damages of the battery storage by the iwb. Due to the volume and complexity of the sensor data, AI-based models are employed for the determination of the remaining useful lifetime, which have already been qualified in condition monitoring of gearbox components, for instance. The integration of these models is realized through a Digital Twin in a secure cloud system, ensuring reliable access to the evaluation logic. This battery storage model enables the assessment of the storage system's condition based on the load history and provides an determination of the remaining useful lifetime. The latter facilitates the decision regarding a potential second-life application, such as in stationary energy storage systems for residential photovoltaic installations.

Functional demonstrator

At the end of the project, the consortium will create a demonstrator that will serve as a validation of the presented multi-sensor approach as well as quantify the technical value of the concept.

 

Acknowledgments

HealthBatt is a collaborative project, which is supported by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the funding program “Research in the Priority Area of Battery Cell Production” (funding code 16BZF34D) and supervised by the VDI Technology Center (VDI TZ). We would like to thank the BMWK and the VDI TZ for their support and for the effective and trusting cooperation as well as the project partners: the VARTA Storage GmbH, the Infineon Technologies AG, and the Fraunhofer Institutes for Silicate Research (ISC) and Electronic Microsystems and Solid State Technologies (EMFT).

 

Project details