Estimate e-Go Battery State Using Diagnostic Data and a Digital Twin

Li-ion battery packs are the heart of modern electric vehicles. Due to their perishable nature, it is crucial to supervise them closely. In addition to on-board supervision over safety and range, insights into the battery's degradation are also becoming increasingly important, not only for the vehicle manufacturers but also for vehicle users. The concept of digital twins has already emerged on the field of automotive technology, and can also help to digitalize the vehicle's battery. In this work, we set up a data pipeline and digital battery twin to track the battery state, including State of charge (SOC) and State of Health (SOH). To achieve this goal, we reverse-engineer the diagnostics interface of a 2014 e-Golf to query for UDS messages containing both battery pack and cell-individual data. An OBD logger records the data with edge-processing capability. Pushing this data into the cloud twin system using loT-technology, we can fit battery models to the data and infer for example, cell individual internal resistance from them. We find that the resistances of the cells differ by a magnitude of two. Furthermore, we propose an architecture for the battery twin in which the twin fleet shares resources like models by encapsulating them in Docker containers run on a cloud stack. By using web technology, we present the analyzed results on a web interface.

Automated summary

Li-ion battery packs are essential for modern electric vehicles, and closely monitoring their degradation is crucial. Digitalizing the battery to track its state, including State of charge and State of Health, can provide valuable insights for both manufacturers and users. By using IoT technology to push data into the cloud twin system, battery models can be fitted to data for analysis and inference.