The general acceptance of electric vehicles in both hybrid and fully electric are increasingly part of the automotive landscape and a cornerstone of all the major automotive manufacturers strategic plans. A Venn diagram of EV growth, geographic location and relative prosperity would indicate that growth will be concentrated in markets that are both dense population centers and smaller geographies. Undoubtedly the most recognized name in EV today is Tesla whose products are full EV not hybrid and it is that type of product, the full EV that is the most interesting. While energy storage (battery) research continues at a fevered pace not only for electric vehicles but for other applications, the reality is that EV’s have to be recharged.
The impact on EV’s in particular to the existing power infrastructure (the grid) is already having a significant impact. A single charging station for an EV devastate the existing local power distribution of a neighborhood because of the instantaneous load placed on a transformer that may have been installed to service 3, 4, 5 homes, but now overloaded by a single home. As EV’s range between charges increases, the availability of charging stations and multilayered rates look very similar to the bad old days of cellular roaming charge models.
The potential and promise of EV’s outweigh the potential negatives but require, planning and insight into patterns, real-time data analytics and the kind of machine learning at the edge that QiO’s solutions are ideal for. The grid is a massive digital twin opportunity. Well summarized from the Institute for Energy Research: https://www.instituteforenergyresearch.org/the-grid/study-electric-vehicle-charging-present-grid-challenges/
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