Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system's effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils' position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271799 | PMC |
| http://dx.doi.org/10.3390/s21134343 | DOI Listing |
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Article Synopsis
- A new hybrid algorithm called MTS-HACO is introduced to optimize energy flow in wireless rechargeable sensor networks, focusing on maximizing the charging efficiency of mobile vehicles (MVs).
- The algorithm categorizes sensor nodes based on their energy consumption timing, ensuring MVs prioritize charging nodes with lower lifetimes during each time slot.
- Simulation results show that MTS-HACO significantly outperforms other existing algorithms, improving charging benefits by about 48.7% compared to the periodic algorithm and 26.3% over the PE-FWA algorithm.
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