In my last post, I talked about the challenge that low oil prices pose for the electric vehicle industry. The following chart from a 2012 McKinsey battery study shows the key tipping points (click for larger image):
With US gasoline (petrol) prices currently running at $2.5 per gallon, we are falling into the bottom left corner of the chart. In short, the battery price for battery electric vehicles (BEVs in the chart) must plummet to keep EVs in the game. As stated yesterday, Nissan and Tesla are getting their battery costs down to around $300 per kilowatt-hour (kWh), but this is still far above the current sweet spot of $150-$200.
Previously, I also talked about the ‘learning rate': the rate at which battery prices could fall due to learning from experience manufacturing cost savings for every doubling of battery volume. The industry is in the ‘Catch 22′ position of not being able to crank up volume sufficiently to get down its cost curve since EVs are just too far adrift from internal combustion engine vehicles price-wise to secure volume sales. So what is to be done?
What would break this logjam is if the auto battery industry could make the next technological leap. The problem for batteries is that oil is so damn energy efficient. A litre of gasoline (petrol) can deliver 10 kWh of energy; the Nissan Leaf battery holds, per one litre by volume, only a hundredth of that. As the chart below shows, even the top-of-the-line Tesla battery is far inferior (source: here; click for larger image).
Once the next generation of batteries arrive, however, things will get more interesting. The irony of both traditional vehicles and EVs is that not much energy is actually used to move humans. For current cars, most gasoline is burnt in order to carry a heavy internal combustion energy around; for EVs, the energy is used to transport the battery. Nonetheless, the energy density of gasoline means that traditional cars get the better of EVs in this particular trade-off. But once a new generation of batteries arrives, EVs can push into the top right-hand corner of the chart above. A that point things will change dramatically–a transition that I will tackle in my next post.
With impeccable timing (for my current blogging theme), Nature Climate Change has just published a commentary by Bjorn Nkyvist and Mans Nilsson reviewing the falling cost of battery packs for electric vehicles (source: here, but apologies as the article is behind a paywall). Bottom line: costs have been falling faster than predicted a few years ago (click for larger image).
In line with Tony Seba’s estimates I blogged on two days ago (here), Nykvist and Nilsson saw total battery pack costs fall 14% per annum between 2007 and 2014 from $1,000 per kilowatt-hour (kWh) to $410. The market leaders in terms of auto battery technology, Tesla and Nissan, saw a slightly lower rate of decline of 6 to 9% since they have been at the cutting edge of improvements and have had less potential for catch-up than the industry as a whole. However, their costs per kWh are now seen at around $300 per kWh of battery capacity. Note that a BMW i3 has battery capacity of approximately 19 kWh, a Nissan Leaf 24 kWh and a top of the range Tesla 85 kWh. Continue reading
A few days ago, a good friend of mine pointed me toward a presentation on disruptive technologies given by Tony Seba. A youtube video is available here:
The entire video is worth watching, but today I will restrict myself to the issues he raises relating to battery technology.
Seba stresses that technological change in the transport sector could happen at breakneck speed. With a pair of compelling photos of early-last-century New York, we are asked to remember that a grand disruption in transport has happened before. In the first photo, dating from April 1900, we play a game of spot the car (click for larger image).
In the second, a mere 13 years later, the challenge is to spot the horse.
The lesson here is that once a disruptive technology reaches a particular tipping point, it doesn’t just take market share from the incumbent industry but rather completely replaces it. For Seba, we are close to reaching that point with electric vehicles.
In the words of the Roman philosopher Seneca:
Increases are of sluggish growth, but the way to ruin is rapid
Lucius Annaeus Seneca was musing on the accelerated rate of decline and fall of empires a couple of thousand years ago. The chemist and scholar of the post-growth world Ugo Bardi has borrowed the philosopher’s name for his idea of a Seneca Cliff–the precipice over which our complex society will likely (according to him) tip and fall.
While such ideas gained considerable traction a few years ago (fanned by rocketing fossil fuel prices and the impact of the Great Recession), they are now deeply out of fashion. Doesn’t Bardi know that we live in an age of abundance, or so the shale oil and gas story goes.
Befitting the name of his blog, Bardi remains a committed Cassandra, warning all those who will listen. To my shale oil production chart of yesterday, Bardi responds with this first (all is well in the world of cod):
And then this (perhaps it was not as well as it seemed):
Full blog post by Bardi on this theme is here. But does the argument “so goes cod, so will go shale” hold true?
This is certainly the view of the geoscientist J. David Hughes, who maintains a web site called “shalebubble.org“. On it, you will find a number of Hughes’ reports published under the imprint of the Post Carbon Institute, the latest going under the title of “Drilling Deeper‘. The full report is 300 pages long, but Hughes concludes that the US Energy Information Administration has built a production forecast on the back of a series of three false premises. Further, based on these, the US economy has taken as truisms a series of false promises (click for larger image).
Should Hughes’ analysis be correct, then Seneca’s Cliff may beckon. Within a decade we will know one way or another. Never forget: Cassandra was proved right in the end.
I regularly report on the Energy Information Administration‘s monthly US oil production statistics, which show no slowdown in output as yet (see here for latest numbers). Bloomberg, however, has a series of multimedia offerings giving more colour as to what is going on.
First, a nice chart juxtaposing production and rig count numbers (source: here).
And for a great animated graphic showing rig count through time and space, this offering (again from Bloomberg) is superb. Below is my screen shot, but to get the full effect click this link here.
Finally, an animation explaining why the crashing rig count has yet to stop production rising. In Bloomberg‘s view, the divergence between rig count and production has many months to run.
National Geographic recently had an article titled “How Long Can the US Oil Boom Last?” which emphasises the longer view. They argue that the US fracking boom is a multi-year phenomenon not a multi-decade one.
But in the long term, the U.S. oil boom faces an even more serious constraint: Though daily production now rivals Saudi Arabia’s, it’s coming from underground reserves that are a small fraction of the ones in the Middle East.
Both the EIA and the International Energy Agency see US oil production peaking out by the end of the decade regardless of short-term oil price fluctuations. Nonetheless, both organisations have underestimated the upswing in tight oil production to date. Overall, it is very difficult to gauge where US production will be in five years time. This is a bigger story than the current spectacular rig count crash, and one I intend to return to in future posts.
The International Energy Agency (IEA) announced today that CO2 emissions in 2014 were flat year on year at 32.3 billion tonnes. This is undoubtedly good news–particularly if it marks the start of a trend.
The chart below is from an article from the FT here (free registration for access). Note, the three previous occasions when emissions flatlined or fell were all associated with recessions or economic crises (click for larger image).
The IEA also points out that global GDP growth in 2014 was around 3%, so the better emission performance was the result of lower GDP-to-energy intensity and reduced energy-to-carbon emissions intensity (the so called Kaya Identify, which maps GDP to emissions, can be found in my post here). Continue reading