2022
DOI: 10.1109/tii.2021.3121287
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Cyber-Physical Data Fusion in Surrogate- Assisted Strength Pareto Evolutionary Algorithm for PHEV Energy Management Optimization

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Cited by 29 publications
(12 citation statements)
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“…The smart-r distribution network, on the other hand, depends on communication and information systems for effective realtime control, operational efficiency, and grid resilience [27]. CPCS is one of the options to represent the smart grid paradigm in SDN [28]. By this, real-time bidirectional interaction between the cybernetic and the physical layer can be achieved.…”
Section: Perspective Of Cyber-physical Co-simulation Testbed For Sdnmentioning
confidence: 99%
“…The smart-r distribution network, on the other hand, depends on communication and information systems for effective realtime control, operational efficiency, and grid resilience [27]. CPCS is one of the options to represent the smart grid paradigm in SDN [28]. By this, real-time bidirectional interaction between the cybernetic and the physical layer can be achieved.…”
Section: Perspective Of Cyber-physical Co-simulation Testbed For Sdnmentioning
confidence: 99%
“…In this study, a typical state machine [20] is adopted in the energy management module to control the transition among three operation modes. Here three input parameters: vehicle torque requirement, , speed requirement, , and battery charge status, is input parameter of the state machine controller, while the output is a split power vector defined as: = (6) where and represent trans-motor torque requirement and speed requirement, separately, while stands for the ISG power demand.…”
Section: Energy Management Modulementioning
confidence: 99%
“…Noise widely exists in the fitness evaluation of many problems [1][2][3], which can mislead the direction of optimization. In the past decade, many studies [4,5] on optimization problems in noisy environments have been published, and some strategies have been introduced to traditional evolutionary algorithms (EAs) to tackle the noise.…”
Section: Introductionmentioning
confidence: 99%
“…(3) Numerical experiments demonstrate that LMM is competitive with some state-of-the-art decomposition methods for LSOPs in noisy environments, and the introduction of MDE-DS is efficient for sub-problems optimization. To the best of our knowledge, not much work has been reported on employing the CC framework to solve LSOPs in noisy environments.…”
mentioning
confidence: 95%