Article
Version 1
Preserved in Portico This version is not peer-reviewed
Estimate e-Golf Battery State Using Diagnostic Data and a Digital Twin
Version 1
: Received: 11 January 2021 / Approved: 12 January 2021 / Online: 12 January 2021 (14:26:14 CET)
A peer-reviewed article of this Preprint also exists.
Merkle, L.; Pöthig, M.; Schmid, F. Estimate e-Golf Battery State Using Diagnostic Data and a Digital Twin. Batteries 2021, 7, 15. Merkle, L.; Pöthig, M.; Schmid, F. Estimate e-Golf Battery State Using Diagnostic Data and a Digital Twin. Batteries 2021, 7, 15.
Abstract
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), state of health (Capacity) (SOHc ) and state of health (Resistance) (SOHr ). To
achieve this goal, we reverse-engineer the diagnostics interface of a 2014 e-Golf to query for Unified
Diagnostic Services (UDS) messages containing both battery pack and cell-individual data. An
on-board diagnosis (OBD) logger records the data with edge-processing capability. Pushing this data
into the cloud twin system using IoT-technology, we can fit battery models to the data and infer
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.
Keywords
UDS-Diagnosis; Battery Twin; Data Logger; SOH
Subject
Engineering, Automotive Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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