This paper presents a wireless power transfer (WPT) technique based on self-resonant Conformal Strongly Coupled Magnetic Resonance (CSCMR) model. The proposed model is compared to a capacitor-loaded mode in terms of their transmission efficiency through simulations. The simulations are run using MATLAB, High Frequency Structure Simulator (HFSS) and OptiSLang. Results confirm that a self-resonant CSCMR-WPT performs better than a capacitor loaded model. To achieve an efficient WPT, a lot of resources may be required for the model which necessitates a high computational time. Hence, compared to using only a 3D simulation software as reported in many literature, a co-simulation is performed between HFSS and OptiSLang to reduce computational resources. Furthermore, using MATLAB to conceptualise the CSCMR resonators gives better guidance and satisfactory results which shortens the simulation times by providing estimated WPT system parameters for an optimal model. The study concludes that, using the co-simulation has reduced computational time by 93% compared to only using the full-wave electromagnetic simulation (HFSS) which translates to quicker design time of WPT.
Reference:
Makhetha, M., Markus, E. & Abu-Mahfouz, A.M. 2022. Efficient wireless power transfer via self-resonant Conformal Strongly Coupled Magnetic Resonance for wireless sensor networks. Energy Reports, vol 8(suppl3). http://hdl.handle.net/10204/12601
Makhetha, M., Markus, E., & Abu-Mahfouz, A. M. (2022). Efficient wireless power transfer via self-resonant Conformal Strongly Coupled Magnetic Resonance for wireless sensor networks. Energy Reports, vol 8(suppl3), http://hdl.handle.net/10204/12601
Makhetha, MJ, ED Markus, and Adnan MI Abu-Mahfouz "Efficient wireless power transfer via self-resonant Conformal Strongly Coupled Magnetic Resonance for wireless sensor networks." Energy Reports, vol 8(suppl3) (2022) http://hdl.handle.net/10204/12601
Makhetha M, Markus E, Abu-Mahfouz AM. Efficient wireless power transfer via self-resonant Conformal Strongly Coupled Magnetic Resonance for wireless sensor networks. Energy Reports, vol 8(suppl3). 2022; http://hdl.handle.net/10204/12601.
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