Estimate e-Golf Battery State Using Diagnostic Data and a Digital Twin
Source abstract
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) 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 IoT-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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Electric and Hybrid Vehicle Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Automotive Technology, Technical University of Munich, 85748 Garching, Germany
References (40)
Show source-provided references
1. Europäische Union (2020, December 05). Verordnung (EU) 2019/631 des Europäischen Parlaments und des Rates vom 17. April 2019 zur Festsetzung von CO2-Emissionsnormen für neue Personenkraftwagen und für neue Leichte Nutzfahrzeuge und zur Aufhebung der Verordnungen (EG) Nr. 443/2009 und (EU) Nr. 510/2011: (EU) 2019/631. Available online: https://eur-lex.europa.eu/legal-content/DE/TXT/PDF/?uri=CELEX:32019R0631&from=ES.
2. Küpper, D., Kuhlmann, K., Wolf, S., Pieper, C., Xu, G., and Ahmad, J. (2020, December 05). The Future of Battery Production in Electric Vehicles. Available online: https://www.bcg.com/publications/2018/future-battery-production-electric-vehicles.
3. Korthauer, R. (2013). Handbuch Lithium-Ionen-Batterien, Springer.10.1007/978-3-642-30653-2 ↗
4. Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter10.1007/s00521-018-3901-7 ↗
5. Open circuit voltage characterization of lithium-ion batteries10.1016/j.jpowsour.2014.06.152 ↗
6. Comparative study on the battery state-of-charge estimation method10.17485/ijst/2015/v8i21/84109 ↗
7. LiPB battery soc estimation using extended Kalman filter improved with variation of single dominant parameter10.6113/JPE.2012.12.1.40 ↗
8. Methods for state-of-charge determination and their applications10.1016/S0378-7753(01)00560-2 ↗
9. Sparse Bayesian Learning and the Relevance Vector Machine
10. Brill, M. (2012). Entwicklung und Implementierung einer neuen Onboard-Diagnosemethode für Lithium-Ionen-Fahrzeugbatterien: Dissertation, VDI Verlag GmbH. Verkehrstechnik/Fahrzeugtechnik.
11. Roscher, M. (2011). Zustandserkennung von LiFePO4-Batterien für Hybrid- und Elektrofahrzeuge: Dissertation, Shaker Verlag. Aachener Beiträge des ISEA.
12. Shah, F.A., Shahzad Sheikh, S., Mir, U.I., and Owais Athar, S. (2019, January 26–27). Battery Health Monitoring for Commercialized Electric Vehicle Batteries: Lithium-Ion. Proceedings of the 5th International Conference on Power Generation Systems and Renewable Energy Technologies, Istanbul, Turkey.10.1109/PGSRET.2019.8882735 ↗
13. Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles10.1016/j.jpowsour.2015.01.129 ↗
14. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles10.1109/TVT.2018.2864688 ↗
15. Klass, V. (2015). Battery Health Estimation in Electric Vehicles. [Ph.D. Thesis, KTH]. Available online: https://www.diva-portal.org/smash/get/diva2:853550/FULLTEXT01.pdf.
16. Impedanzbasierte Zustandsdiagnose fur Energiespeicher in Automobilen
17. Balagopal, B., and Chow, M.Y. (2015, January 22–24). The State of the Art Approaches to Estimate the State of Health (SOH) and State of Function (SOF) of Lithium Ion Batteries. Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Cambridge, UK.10.1109/INDIN.2015.7281923 ↗
18. Zimmermann, W., and Schmidgall, R. (2014). Bussysteme in der Fahrzeugtechnik: Protokolle, Standards und Softwarearchitektur, Springer Vieweg. [5., aktual. und erw. aufl. ed.]. ATZ/MTZ-Fachbuch.10.1007/978-3-658-02419-2 ↗
19. Supke, J., and Zimmermann, W. (2020, December 05). Diagnosesysteme im Automobil: Transport- & Diagnoseprotokolle. Available online: http://www.emotive.de/documents/WebcastsProtected/Transport-Diagnoseprotokolle.pdf.
20. Kunze, S. (2020, December 05). On-Board Fahrzeugdiagnose. Available online: https://tu-dresden.de/ing/informatik/ti/vlsi/ressourcen/dateien/dateien_studium/dateien_lehstuhlseminar/vortraege_lehrstuhlseminar/hs_ss05_pdf/steffen_kunze.pdf?lang=de.
21. Grieves, M. (2020, December 05). Digital Twin: Manufacturing Excellence through Virtual Factory Replication This Paper Introduces the Concept of a A Whitepaper by Dr. Michael Grieves. Available online: https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication.
22. Jones, D., Snider, C., Nassehi, A., Yon, J., and Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP J. Manuf. Sci. Technol.10.1016/j.cirpj.2020.02.002 ↗
23. Baumann, M., Rohr, S., and Lienkamp, M. (2018, January 10–12). Cloud-connected battery management for decision making on second-life of electric vehicle batteries. Proceedings of the 2018 Thirteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte Carlo, Monaco.10.1109/EVER.2018.8362355 ↗
24. (2020, December 05). Self-Study Programme 530: The e-Golf. Available online: https://vwgpaintandbody.co.uk/wp-content/uploads/Volkswagen-e-golf.pdf.
25. Rahimzei, E., Sann, K., and Vogel, M. (2020, December 05). Kompendium: Li-Ionen-Batterien: Grundlagen, Bewertungskriterien, Gesetze und Normen. Available online: https://www.dke.de/resource/blob/933404/3d80f2d93602ef58c6e28ade9be093cf/kompendium-li-ionen-batterien-data.pdf.
26. Idaho National Laboratory (2020, December 05). 2015 Volkswagen e-Golf Advanced Vehicle Testing—Baseline Vehicle Testing Results, Available online: https://avt.inl.gov/sites/default/files/pdf/fsev/fact2140egolf2015.pdf.
27. (2020, April 25). Hochvoltbatteriesystem im e-Golf. Available online: https://www.volkswagen-newsroom.com/de/batterie-3634.
28. Novel method for the on-line estimation of low-frequency impedance of lithium-ion batteries10.1016/j.est.2020.101818 ↗
29. Auto-Intern GmbH (2020, February 04). Was Ist VCDS?. Available online: https://www.vcds.de/ueber-vcds/ueber-vcds.
30. Shen, P., Ouyang, M., Lu, L., and Li, J. (2016, January 17–20). State of Charge, State of Health and State of Funktion Co-estimation of Lithium-ion Batteries for Electric Vehicles. Proceedings of the 2016 IEEE Vehicle Power and Propulsion Conference (VPPC), Hangzhou, China.10.1109/VPPC.2016.7791782 ↗
31. (2020, November 26). Volkswagen E-Golf—VIN 2012, Available online: https://avt.inl.gov/sites/default/files/pdf/fsev/batteryEGolf2012.pdf.
32. Detachable electrical connection of battery cells by press contacts10.1016/j.est.2016.09.011 ↗
33. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python10.1038/s41592-019-0686-2 ↗
34. Adermann, J., Kreibich, J., and Lienkamp, M. (2017). Experimental Study of Energy Consumption Variation in Recurring Driving Trips. J. Electr. Eng., 5.10.17265/2328-2223/2017.05.004 ↗
35. Park, S., Savvides, A., and Srivastava, M. (2001, January 6–7). Battery capacity measurement and analysis using lithium coin cell battery. Proceedings of the 2001 International Symposium on Low Power Electronics and Design—ISLPED ’01, Huntington Beach, CA, USA.10.1145/383082.383187 ↗
36. Zhang, L., Peng, H., Ning, Z., Mu, Z., and Sun, C. (2017). Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles. Appl. Sci., 7.10.3390/app7101002 ↗
37. Zenati, A., Desprez, P., Razik, H., and Rael, S. (2012, January 4–8). A methodology to assess the state of health of lithium-ion batteries based on the battery’s parameters and a fuzzy logic system. Proceedings of the 2012 IEEE International Electric Vehicle Conference—IEVC, Greenville, SC, USA.10.1109/IEVC.2012.6183268 ↗
38. (2020, November 26). Volkswagen E-Golf—VIN 6525, Available online: https://avt.inl.gov/sites/default/files/pdf/fsev/batteryEGolf6525.pdf.
39. (2020, November 26). Volkswagen E-Golf—VIN 5818, Available online: https://avt.inl.gov/sites/default/files/pdf/fsev/batteryEGolf5818.pdf.
40. (2020, November 26). Volkswagen E-Golf—VIN 2140, Available online: https://avt.inl.gov/sites/default/files/pdf/fsev/batteryEGolf2140.pdf.