ABSTRACT
Following the trends of electrification, the energy storage of vehicles is gaining importance as the most expensive part of an electric car. Since lithium-ion batteries are perishable goods and underlie e. g. aging effects, environmental and operating conditions during manufacturing and car usage need close supervision. With regard to the paradigm of digital twins, data from various life cycle phases needs to be collected and processed to improve the general quality of the system. To achieve this complex task, a suitable framework is needed in order to operate the fleet of digital twins during manufacturing processes, the automotive usage and a potential second life. Based on a literature review, we formulate requirements for a digital twin framework in the field of battery systems. We propose a framework to develop and operate a fleet of digital twins during all life cycle phases. Results feature a case study in which we implement the stated framework in a cloud-computing environment using early stages of battery system production as test a bed. With the help of a self-discharge model of li-ion cells, the system can estimate the SOC of battery modules and provide this information to the arrival testing procedures.
- Alam, K. M., Sopena, A., and Saddik, A. E. Design and Development of a Cloud Based Cyber-Physical Architecture for the Internet-of-Things. Proceedings - 2015 IEEE International Symposium on Multimedia, ISM 2015 (2016), 459--464.Google Scholar
- Bao, J., Guo, D., Li, J., and Zhang, J. The modelling and operations for the digital twin in the context of manufacturing. Enterprise Information Systems 0, 0 (2018), 1--23.Google Scholar
- Ciavotta, M., Alge, M., Menato, S., Rovere, D., and Pedrazzoli, P. A Microservice-based Middleware for the Digital Factory. Procedia Manufacturing 11, June (2017), 931--938.Google ScholarCross Ref
- Delsing, J. Arrowhead, 2019.Google Scholar
- Deutschen, T., Gasser, S., Schaller, M., and Siehr, J. Modeling the self-discharge by voltage decay of a NMC/ graphite lithium-ion cell. Journal of Energy Storage 19, June (2018), 113--119.Google ScholarCross Ref
- Ding, K., Chan, F. T., Zhang, X., Zhou, G., and Zhang, F. Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors. International Journal of Production Research 0, 0 (2019), 1--20.Google Scholar
- Etzkorn, L. H. Introduction to Middleware, 1 ed. Chapman and Hall/CRC, New York, 2017.Google Scholar
- Liu, X. F., Al Sunny, S. M. N., Nguyen, N.-T., Tao, W., Leu, M. C., Shahriar, M. R., and Hu, L. Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect. Procedia Manufacturing 26 (2018), 1193--1203.Google ScholarCross Ref
- Lukasiewycz, M., Steinhorst, S., Sagstetter, F., Chang, W., Waszecki, P., Kauer, M., and Chakraborty, S. Cyber-Physical Systems Design for Electric Vehicles. In 2012 15th Euromicro Conference on Digital System Design (sep 2012), IEEE, pp. 477--484.Google Scholar
- Merkle, L., Segura, A. S., Torben Grummel, J., and Lienkamp, M. Architecture of a Digital Twin for Enabling Digital Services for Battery Systems. In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (may 2019), IEEE, pp. 155--160.Google ScholarCross Ref
- Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., and Ueda, K. Cyber-physical systems in manufacturing. CIRP Annals 65, 2 (2016), 621--641.Google ScholarCross Ref
- OASIS Standard. Mqtt-v3.1.1- Specification, 2014.Google Scholar
- OMG. Data Distribution Service Specification Version 1.4, 2015.Google Scholar
- Schluse, M., and Rossmann, J. From Simulation to Experimentable Digital Twins. Systems Engineering (ISSE), 2016 IEEE International Symposium on (2016), 1--6.Google ScholarCross Ref
- Schroeder, G. N., Steinmetz, C., Pereira, C. E., and Espindola, D. B. Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange. IFAC-PapersOnLine 49, 30 (2016), 12--17.Google ScholarCross Ref
- Yun, S., Park, J. H., and Kim, W. T. Data-centric middleware based digital twin platform for dependable cyber-physical systems. International Conference on Ubiquitous and Future Networks, ICUFN (2017), 922--926.Google ScholarCross Ref
Index Terms
- Cloud-Based Battery Digital Twin Middleware Using Model-Based Development
Recommendations
Digital Twin for Cybersecurity Incident Prediction: A Multivocal Literature Review
ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering WorkshopsThe advancements in the field of internet of things, artificial intelligence, machine learning, and data analytics has laid the path to the evolution of digital twin technology. The digital twin is a high-fidelity digital model of a physical system or ...
Time series behavior modeling with digital twin for Internet of Vehicles
AbstractElectric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging ...
Digital twin for electric vehicle battery management with incremental learning
AbstractThe current Industry 4.0 revolution promotes the use of cyber–physical systems to enhance manufacturing and other industrial processes via automation, real-time analysis, etc. Data communication between individual systems plays an ...
Comments