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Advanced Intelligent Diagnosis of Lithium-Ion Batteries

The project aims to develop a cost-effective, non-proprietary and user-friendly diagnostic tool for lithium-ion batteries (LIB). The system is developed as a demonstrator that can access the traction batteries of various electric vehicles via standard interfaces and at the same time uses complex battery diagnostic algorithms via an online connection to the cloud.

With the rising popularity and market penetration of electric vehicles (EVs), workshops will increasingly be dealing with their maintenance, diagnosis and repair. Due to the mounting technical complexity of EVs, auto technicians will find it more and more difficult to perform reliable troubleshooting, in particular when it comes to LIBs. For these reasons, workshops need diagnostic equipment that is easy to operate and enables a reliable diagnosis of the LIB based on short static tests.

Objectives and methods

The aim of the project is therefore the development of a cost-effective diagnostic system for EVs that enables workshops and test facilities to carry out a simple, quick, vehicle-independent diagnosis of the traction battery. To this end, a diagnostic tester for workshops that has online capability and is able to perform non-proprietary measurements on the battery at standstill is being developed. In addition, a cloud IT infrastructure based on the EDI hive framework is created as part of the project. This allows the secure and reliable online transfer of the measured values to the cloud. The cloud technology means the service is scalable for many workshops.

The measured values are then evaluated in the cloud using complex diagnostic algorithms. After the evaluation, the results are transferred from the cloud IT infrastructure to the diagnostic system and displayed to the workshop or test facility personnel, along with easy-to-follow instructions for intuitive operation. Due to the online capability of the diagnostic system, there is no need to implement complex, vehicle-specific diagnostic algorithms on the device itself. The device can therefore be produced at lower cost and is always compatible with the latest battery models.

Project Profile

Project coordinator

  • CTC cartech company GmbH (particiation terminated)

Project duration

  • 09/2018 – 09/2022

Project partners

  • EDI GmbH
  • Fraunhofer Institute for Energy Economics and Energy System Technology – IEE
  • Research Institute for Automotive Engineering and Vehicle Engines Stuttgart – FKFS

Sponsored by the Federal Ministry of Education and Research as part of the “Electronics and Autonomous Driving” programme. The project owner is VDI/VDE Innovation+Technik GmbH.