Cyber-Physical Data Fusion in Surrogate- Assisted Strength Pareto Evolutionary Algorithm for PHEV Energy Management Optimization

نویسندگان

چکیده

This article proposes a new form of algorithm environment for the multiobjective optimization an energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary (SSPEA) is developed to optimize power-split control parameters guided by data from physical PHEV and its digital twins (DTs). By introducing “confidence factor,” SSPEA uses fused physically measured virtually simulated vehicle performances (energy consumption remaining battery state charge) converge process. Gaussian noisy models are adopted emulate real on hardware-in-the-loop platform experimental evaluation. testing results suggest that proposed requires less R&D costs than model-free method only information, more 44.6% can be saved during Driven SSPEA, optimized surpasses other non-DT-assisted systems saving 4.8% energy.

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ژورنال

عنوان ژورنال: IEEE Transactions on Industrial Informatics

سال: 2022

ISSN: ['1551-3203', '1941-0050']

DOI: https://doi.org/10.1109/tii.2021.3121287