This data-file tabulates the greatest challenges for lithium ion batteries in electric vehicles, which have been cited in 2020’s patent literature. Specifically, the work contains a sample of 100 patents aiming to overcome these challenges, as filed by companies including Tesla, CATL, GM, GS Yuasa, LG, Nissan, Panasonic, Sanyo, Sumitomo, Toyota, et al.
Our notes and conclusionsare spelled out in detail. We find the industry is clearly entering execution mode, and less focused on radical breakthroughs in energy density. CATL and Tesla’s pursuit of a “million mile battery” is substantiated, but includes trade-offs. The patent disclosures also suggest great difficulties in ever achieving a battery-powered semi-truck.
This data-file quantifies the cost per mile of vehicle ownership for different categories of vehicles. Our methodology looks across the prices of 1,200 second hand vehicles, to correlate how the re-sale value of each make and model degrades per mile that has accumulated on its odometer (chart above).
Hybrids and basic passenger cars are most economical. Trucks and SUVs are 2x more costly. EVs are another 25% more costly again, and will have lost c60% of their value after 100,000 miles. Hydrogen cars have the highest costs and will have lost over 90% of their value after 100,000 miles (chart below).
Underlying data are shown in the input tabacross ten makes and models, to see how the re-sale value of each vehicle degrades with mileage. This may help you appraise what a particular second hand purchase “should” cost (example below) if you are among the many non-drivers considering a vehicle purchase as a result of the COVID crisis.
This data-file compiles all of Tesla’s patents, classifies them across 1,000 patent families, and describes their innovations.
Our conclusionis that Tesla holds less patented IP than rival auto-companies. However, where it has filed patents, it is more focused on pure EV technologies, such as batteries, electric circuitry, electric propulsion and digital features (chart above).
Patent filings since 2019have focused on big data/digital technologies, solar and improved batteries (including novel electrolyte systems using lithium fluorates, borates and other improved additives).
Supercapacitors may eclipse lithium ion batteries in the hybridization of transport and industry. Their energy density is improving. Potential CO2 savings could surpass 1bn tons per year. IRRs of 10-50% can be achieved, even prior to CO2 prices. These are our conclusions after reviewing 2,000 Western patents. GE, Siemens, Skeleton and ZapGo are among the leading companies exposed to the theme.
What are the top technologies to transform the global energy industry and the world? This data-file summarises where we have conducted differentiated analysis, across c80 technologies (and counting).
For each technology, we summarise the opportunity in two-lines. Then we score its economic impact, its technical maturity (TRL), and the depth of our work to-date. The output is a ranking of the top technologies, by category; and a “cost curve” for the total costs to decarbonise global energy.
Download this data-fileand you will also receive updates for a year, as we add more technologies; and we will also be happy to dig into any technologies you would like to see added to the list.
This data-file quantifies the number of patents filed into autonomous vehicles, by year, by geography and by patent family, looking across 37,000 patent filings since the year 2000. Patent activity has risen at a 27% CAGR over the past decade, indicating a rapid pace of research activity.
The leading patent filersare ranked, including some of the world’s leading automotive companies, tech companies and retail companies. It is interesting to compare the relative activity levels among companies such as Denso, MobilEye, TuSimple, Uber, Waymo and Zoox (recently acquired by Amazon), versus Ford, GM, Honda, Toyota, Volvo et al.
Our notes and a data-pull of all the underlying 2019 patents follow. We find autonomous vehicles could entrench a 10% acceleration in road travel post-COVID, and displace c15% of all air-miles on sub-1,000 mile journeys.
This data-file models the energy economics of constructing new electric rail lines, to displace automobile traffic and accelerate the energy transition.
Under our base case forecasts, a mid-sized electric rail project would struggle economically, without tax-support, while saving around 1kT of CO2 per track-mile per year.
The economics depend heavilyupon prices, costs and passenger numbers. Double-digit returns are achievable outside the United States, based on >75% lower apparent capex costs, especially for lines carrying c10,000 passengers per day.
CO2 pricesdo not materially change the picture, only adding around c1.5pp to our base case IRRs, even at a CO2 price of $500/ton, near the top of our cost-curve.
This data-file tabulates statistics on the US aviation sector, from the Bureau of Transport Statistics, to compute the fuel economy of US air travel, per plane-mile and per passenger-mile.
In 2019, 10M US flights carried 930M passengers 1.1 trn passenger-miles. The latest data in the file run to February-2020.
Fuel economy per passenger milehas risen at a 2.8% CAGR since 2003. Flight numbers have fallen by -0.4% pa and flights have become 0.8% longer. But load factors have improved by 0.7pp each year, spreading 0.5 plane miles per gallon across more passengers.
We have quantified the average speed of automobiles on a dozen highways and expressways flowing into New York City from Long Island, CT and New Jersey, to quantify how traffic ebbs and flows over time.
Traffic is most severe at 4-5pm, second worst at 8-9am, but least severe at 4-5am. The data in the file are from 2H19.
We can compute average vehicle fuel economy, as a function of these traffic speeds. Moderate-severe traffic congestion curtails average vehicle fuel economy by 15-45% on highways leading in to a city.
This database breaks down long-distance travel (defined by a distance greater than 100-miles) by purpose, by transportation type and by distance category, for the average person in the US, and in aggregate.
It is based on over 1.2GB of raw data, collected by the US FHWA and US DOT in 2007-11, which is still widely cited around the Academic literature and thus relevant to assessing the post-COVID landscape.
The data show the full breakdown of long-distance travel by plane, car, bus and train; for business, leisure activities and commuting; from 100-miles to 4,000-miles; how these different factors co-vary; and how they have changed between 2010 and 2017.
The chart belowillustrates the headline data, aggregating all modes of transport by purpose and travel distance. Alternate versions of the chart are available for just planes, automobiles, buses and trains.