% Once again people who don't drive Electric are making huge-impact
decisions that only serve to waste/spent tax$. Their data is skewed, their
models are not an EV-driver's reality, and they have not used existing
sources that have all the info they need to make wise choices: plugshare
listings and their comment hits, all the EV driver forums, (+much more). %
DoE Maps Out 4 Possible Charging Station Scenarios For U.S.
20171202 Mark Kane
Geographies Considered: Cities, Towns, and Interstates (source: energy.gov)
Market Conditions for the Central Scenario and Sensitivities Explored
Summary of Station and Plug Count Estimates for the Central Scenario (15M
PEVs in 2030) (source: NREL)
EVgo inaugurates its 100th California Freedom Station by increasing
capacity to serve four times more drivers at a single site
Tesla’s new Urban Supercharger
Geographies Considered: Cities, Towns, and Interstates (source:
Scenario: Mega-regions Fast-Charging Coverage (source: energy.gov)
Scenario: U.S. Interstate Highway System Fast-Charging Coverage (source:
Scenario: National Fast-Charging Coverage (source: energy.gov)
Appendix A: Supplementary Statistics on Existing EVSE (source: NREL)
Appendix C: Central Scenario PEV/EVSE Estimates by State (source: NREL)
source: National Plug-In Electric Vehicle Infrastructure Analysis (NREL) via
The US DOE’s Office of Energy Efficiency & Renewable Energy recently
highlighted the National Renewable Energy Laboratory’s (NREL) report on
several potential scenarios of how the electric vehicle charging
infrastructure will look like in 2030.
The report assumes some 15 million plug-in vehicles to be on the roads in
U.S. within the next 13 years. And as such, a few hundred corridors would
need to be covered with fast charging stations to enable long-distance
travel, while roughly 8,000 are envisioned to serve urban and rural areas
This report categorizes PEV charging infrastructure requirements by area
served (cities, towns, rural areas, and Interstate corridors) and role
during early PEV market growth (providing coverage to early PEVs and
satisfying demand due to increased PEV adoption).
The analysis makes no assumptions about the likelihood of particular PEV
market or technology scenarios. Rather, a range of plausible scenarios
explores the relationship between the evolution of the PEV fleet and
Communities are expected to have significantly larger charging
infrastructure requirements than Interstate corridors under both the
coverage and demand assessments. About 4,900 DCFC stations are required
across cities with an additional 3,200 DCFC stations required in towns to
provide a minimum level of nationwide coverage in the communities where 81%
of people live. Such a network would dampen range anxiety concerns by
providing drivers with a safety net for emergency charging situations.
Intracommunity charging demand analysis demonstrates how utilization of
the DCFC coverage network would be expected to grow in increased PEV
adoption scenarios based on a home-dominant charging assumption. Results for
a 15-million PEV market estimate a DCFC plug requirement of 25,000 in
communities (approximately 3.1 plugs per average DCFC station and 3.4 plugs
required to support 1,000 BEVs). Demand for non-residential L2 EVSE
(including work and public charging) is estimated as 600,000 plugs necessary
to support 15 million PEVs (approximately 40 plugs per 1,000 PEVs).
Sensitivity analysis of the community results for consumer charging
demand indicates a strong relationship between the evolution of the PEV and
EVSE markets. As this analysis attempts to arrive at charging infrastructure
solutions that fill the eVMT gaps between consumer travel patterns and PEV
electric ranges, infrastructure requirements are not only proportional to
the total number of PEVs in the system, but also inversely proportional to
PEV electric range. Manufacturer and consumer preferences with respect to
electric range, charging power, and utilization of residential EVSE have
direct and dramatic consequences on the level of charging demand calculated
in this analysis.
Results suggest that approximately 400 corridor DCFC stations are needed
to enable long-distance BEV travel along Interstate highways between cities
(where the majority of BEVs are likely to be concentrated). Understanding
driving patterns, vehicle characteristics, and charging behavior and then
prioritizing corridors and setting station spacing accordingly—as
illustrated in the network scenarios—could help optimize the utility and
economics of early-market corridor charging stations.
Regardless of geographic scope, the analysis suggests that organizations
planning for charging infrastructure to support consumer adoption of PEVs
need to be aware of the importance of consumer preferences with respect to
electric range and charging behavior. Furthermore, planners should focus on
providing consumers with adequate charging coverage (particularly DCFC
supporting adoption of BEVs) while monitoring station utilization over time
and increasing charging capacity (both in terms of rated power and number of
plugs) as the PEV market continues to grow.
The report focuses on non-residential charging equipment to meet
consumer coverage expectations and demand. Coverage and charging demand
estimates are made for where people live and the highways they travel. They
include four specific geographic areas: cities (represented in yellow),
towns (in purple), rural areas and the U.S. Interstate Highway System (red
Mega-regions are large networks of cities with shared natural resources
and common transportation systems. Most of the nation’s rapid population
growth is expected to take place in these large networks. Providing coverage
for PEV charging on interstates within each mega-region would result in the
fast-charging network shown above and would need 96 to 239 fast-charging
stations depending on station spacing.
To provide convenient access to PEV drivers across the U.S. Interstate
Highway System (red lines), the analysis finds that 285 to 713 corridor
fast-charging stations would be needed, depending on spacing. This map shows
the coverage that would result from using fast-charging stations, spaced 70
miles apart on average.
Although full community-based infrastructure may take longer to
establish—projected by NREL as roughly 8,000 fast-charging stations in U.S.
cities and towns—it could provide travel corridors with charging backup
options, route flexibility, and additional coverage along U.S. highways and
state routes. This map shows the national fast-charging station coverage
enabled by community-based charging stations.
And here we find some very interesting data (however the numbers of charging
stations are at least several month old in this case [Appendix A & C]):
SteveSeattle December 2, 2017 at 10:14 am
It is interesting that their analysis does not show CA being top on %BEV at
54% compared to say GA at 77%. Did they use sales data before GA ditched
their very generous incentives as a baseline? Based on the current political
climate I would expect these numbers to be flipped.
Rightofthepeople December 2, 2017 at 10:28 am
Yeah something seems off. I’m not sure how they came up the the %BEV but
it doesn’t seem realistic that GA would be that much higher than CA. I’m in
GA and own a Leaf, and we do have a lot of them around, but these are
forward looking numbers so who knows.
jelloslug December 2, 2017 at 10:39 am
Lots of their data seems to be old. Their numbers for Tesla
Supercharger plugs seem to be from many months ago ...
Kdawg December 2, 2017 at 11:52 am
This seems like something that would be a good problem for [supercomputer]
Watson to try & tackle. We know most Americans driving habits, from those
that don’t drive more than 5000 miles a year, to those that drive 100,000
miles a year. And there’s a ton more variables: EV range of car, does it
have a range extender, does the person charge at home, does the car have
DCFC, at what rate, how is range affected by winter, etc. Put in all of the
variables into Watson and let it kick out several different models of what
would be required for national charging, for different amounts of plug-ins
on the roads.
] ... [© 2017 MOTORSPORT NETWORK]
Watson is a question answering computer system capable of answering
questions posed in natural language, developed in IBM's DeepQA project by a
research team led by principal investigator David Ferrucci ...
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