ha! So, because it's tesla, the datum doesn't count?!lorenfb wrote:The overall numbers are shewed because of the M3. We'll have to wait for the Q2 M3 data. Until Tesla can repeat 2018 Q4 thru 2019,Oils4AsphaultOnly wrote:Since lorenfb's looking for a crystal ball, how about hard data? https://www.cncda.org/wp-content/upload ... -1Q-19.pdflpickup wrote:
Actually, it's more like "hockey stick" growth, but exponential will be a suitable proxy.
My crystal ball? Battery costs. Battery costs have been falling at a very consistent 19% per year, and is forecast to continue this trend. Maybe you have a reason to believe this will not be the case, but with all the research going on in the battery world, I don't really see an end to this trend in the next 5 years.
Now so far, the cost savings on the battery side have not transitioned directly to cost savings on the vehicle. This is because automakers have focused on adding larger format batteries to address a perceived range limitation. But there have been significant increases in range over the past 8 years, while keeping the price constant. I believe that once we hit an affordable 300 mile (maybe 350) EV, that there will be little reason to keep adding more battery, and instead we will see a shift towards making vehicles more affordable. And eventually there will be a crossover point where the unsubsidized cost of an EV will be less than an equivalent ICE.
And at that point, to the buying public, it simply won't matter whether it's an EV or an ICE. That's the problem I have with people that are saying this or that about "EV demand". Most people are sticker-price focused. They don't necessarily care what's under the hood. They only care about what the sticker says. And the day the sticker on an EV is less than the sticker on an ICE, they are going to get the EV. At this point it stops being a ramp and turns a sharp corner.
Yes, EVs have to become available in the form factors they want. And yes, there will need to be education of the masses about how and where to charge their cars. But the growing number of EV owners are going to be the teachers. And the more EVs that people see on the road, the more people are going to be willing to ask their friends, neighbors and co-workers about them.
If you think any part of this "crystal ball" is wrong, let me know, but you'll be disagreeing with the technology adoption curve that has been repeated throughout history, so it's a tough argument to make. About the only legitimate difference of opinion we can have is the timescale. Yes, with falling battery prices, the percentage of the cost of the battery to the vehicle becomes smaller, so the gains in vehicle price become less significant. But I don't think this is going to take more than another 4-5 years. We now have 3 mass produces vehicles with ranges over 300 miles, with several more knocking on the door at 250 (unfortunately in somewhat limited quantities). 2020 should be a big year in terms of new EV releases. Once we get those vehicles with 300 miles on the market, we can start the process of driving the costs down.
It kinda mimics Norway's EV adoption doesn't it?
Edit: The relevant graph is on page 2.
most OEMs would rather error on conservative BEV build forecasts for 2020, e.g. marginal 2019 Bolt & Leaf numbers.
Alright, then I'll share with everyone my favorite statistician (jhm), and his plug-in EV predictive envelope model (based on historical data): https://teslamotorsclub.com/tmc/posts/3683239/
If you believe nothing else of his, pay attention to how his model was more accurate than BNEF's (Bloomberg NEF), which many in the oil industry rely on to make their production plans on:
" In April, 2018, BNEF forecast 2018 to come in at 1.56M or about 1.67% share. The actual was 2.018M or 2.12% share. Let's what my method would have predicted using just 2012 thru 2015 data (a sample size of just 3). My 2015 forecast of 2018 would have centered on 2.2% with a 90% predictive interval from 1.3% to 3.5%. Yeah, a lot of uncertainty, but nailed it. My 2016 forecast of 2018 centered on 1.8% ranging from 1.3% to 2.5%. This was a little more pessimistic, but less uncertain. Then the 2017 forecast of 2018 centered around 2.0% ranging from 1.7% to 2.4%. So the predictive envelope nicely closed in on the actual. Meanwhile, BNEF low forecast was ruled out of the predictive interval by the time it was made using 2017 as the last historical datum. Presumably, BNEF's forecast had that benefit of highly granular data, their proprietary data, and a multi-industry team of analysts. Surely with all that going for them, they should have been able to produce a forecast with much less uncertainty, but in fact a sample size of 5 historical observations could have alerted them to the possibility that their prediction was high improbably. "
RTFA, he writes well and should open many people's eyes on their short-sightedness.