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Evaluation of Future Topologies and Architectures for High-Reliability Electrical Distribution Systems
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Brabetz, L., Ayeb, M., Lehmann, J., and Löwer, B., "Evaluation of Future Topologies and Architectures for High-Reliability Electrical Distribution Systems," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(4):2347-2355, 2020, https://doi.org/10.4271/2020-01-1296.
Within the scope of the development of autonomous vehicles, the mandatory reliability requirements of the electrical power supply, and consequently of the electrical distribution system (EDS), are increased considerably. In addition, the overall rising number of electrical functions leads to significantly higher electrical power demands, while strict cost, weight and packaging constraints must be upheld. Current developments focus on adding redundancies, enhancing physical robustness, or dimensioning critical components. New approaches address predictive power management, better diagnostic capabilities, and, the subject of this paper, alternative topologies and architectures . These are derivations of the conventional tree structure, as well as ring- or linear-bus-based zonal architectures, which feature in part distributed storage devices or semiconductor switches that rearrange the power paths in case of a fault [2,3].
The presented approach is a method for both the systematic description of EDS topologies and architectures and the assessment of their reliability. It is based on a data model designed for a simple but unique definition of the EDS, a graphical representation of the topology, an algorithm for the calculation of the reliability, and a straightforward cost assessment. It allows benchmarking and optimization of a large number of architectures, and enables the comparative analysis of the impact of both reliability- and cost-parameters, which often are not well known. In addition to the theoretical description, the paper presents comparative results for various EDS architectures and a Pareto- and sensitivity analysis, followed by a discussion of the next steps, for instance the application of an optimization algorithm using a rule-based generation of alternative architectures.