AI-Guided Robots Are Ready to Sort Your Recyclables

Computer-vision systems use shapes, colors, and even labels to identify materials at superhuman speeds

11 min read
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An animated image of different elements of trash with different markings overlaying it.

The Amp Cortex, a highspeed robotic sorting system guided by artificial intelligence, identifies materials by category on a conveyor belt. To date, systems in operation have recognized more than 50 billion objects in various permutations.

AMP Robotics
Green

It’s Tuesday night. In front of your house sits a large blue bin, full of newspaper, cardboard, bottles, cans, foil take-out trays, and empty yogurt containers. You may feel virtuous, thinking you’re doing your part to reduce waste. But after you rinse out that yogurt container and toss it into the bin, you probably don’t think much about it ever again.

The truth about recycling in many parts of the United States and much of Europe is sobering. Tomorrow morning, the contents of the recycling bin will be dumped into a truck and taken to the recycling facility to be sorted. Most of the material will head off for processing and eventual use in new products. But a lot of it will end up in a landfill.


So how much of the material that goes into the typical bin avoids a trip to landfill? For countries that do curbside recycling, the number—called the recovery rate—appears to average around 70 to 90 percent, though widespread data isn’t available. That doesn’t seem bad. But in some municipalities, it can go as low as 40 percent.

What’s worse, only a small quantity of all recyclables makes it into the bins—just 32 percent in the United States and 10 to 15 percent globally. That’s a lot of material made from finite resources that needlessly goes to waste.

We have to do better than that. Right now, the recycling industry is facing a financial crisis, thanks to falling prices for sorted recyclables as well as policy, enacted by China in 2018, which restricts the import of many materials destined for recycling and shuts out most recyclables originating in the United States.

There is a way to do better. Using computer vision, machine learning, and robots to identify and sort recycled material, we can improve the accuracy of automatic sorting machines, reduce the need for human intervention, and boost overall recovery rates.

My company, Amp Robotics, based in Louisville, Colo., is developing hardware and software that relies on image analysis to sort recyclables with far higher accuracy and recovery rates than are typical for conventional systems. Other companies are similarly working to apply AI and robotics to recycling, including Bulk Handling Systems, Machinex, and Tomra. To date, the technology has been installed in hundreds of sorting facilities around the world. Expanding its use will prevent waste and help the environment by keeping recyclables out of landfills and making them easier to reprocess and reuse.

An animated image of different elements of trash with different markings overlaying it.AMP Robotics

Before I explain how AI will improve recycling, let’s look at how recycled materials were sorted in the past and how they’re being sorted in most parts of the world today.

When recycling began in the 1960s, the task of sorting fell to the consumer—newspapers in one bundle, cardboard in another, and glass and cans in their own separate bins. That turned out to be too much of a hassle for many people and limited the amount of recyclable materials gathered.

In the 1970s, many cities took away the multiple bins and replaced them with a single container, with sorting happening downstream. This “single stream” recycling boosted participation, and it is now the dominant form of recycling in developed countries.

Moving the task of sorting further downstream led to the building of sorting facilities. To do the actual sorting, recycling entrepreneurs adapted equipment from the mining and agriculture industries, filling in with human labor as necessary. These sorting systems had no computer intelligence, relying instead on the physical properties of materials to separate them. Glass, for example, can be broken into tiny pieces and then sifted and collected. Cardboard is rigid and light—it can glide over a series of mechanical camlike disks, while other, denser materials fall in between the disks. Ferrous metals can be magnetically separated from other materials; magnetism can also be induced in nonferrous items, like aluminum, using a large eddy current.

By the 1990s, hyperspectral imaging, developed by NASA and first launched in a satellite in 1972, was becoming commercially viable and began to show up in the recycling world. Unlike human eyes, which mostly see in combinations of red, green, and blue, hyperspectral sensors divide images into many more spectral bands. The technology’s ability to distinguish between different types of plastics changed the game for recyclers, bringing not only optical sensing but computer intelligence into the process. Programmable optical sorters were also developed to separate paper products, distinguishing, say, newspaper from junk mail.

So today, much of the sorting is automated. These systems generally sort to 80 to 95 percent purity—that is, 5 to 20 percent of the output shouldn’t be there. For the output to be profitable, however, the purity must be higher than 95 percent; below this threshold, the value drops, and often it’s worth nothing. So humans manually clean up each of the streams, picking out stray objects before the material is compressed and baled for shipping.

Despite all the automated and manual sorting, about 10 to 30 percent of the material that enters the facility ultimately ends up in a landfill. In most cases, more than half of that material is recyclable and worth money but was simply missed.

We’ve pushed the current systems as far as they can go. Only AI can do better.

Getting AI into the recycling business means combining pick-and-place robots with accurate real-time object detection. Pick-and-place robots combined with computer vision systems are used in manufacturing to grab particular objects, but they generally are just looking repeatedly for a single item, or for a few items of known shapes and under controlled lighting conditions.Recycling, though, involves infinite variability in the kinds, shapes, and orientations of the objects traveling down the conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm.

A photo of a conveyor belt with discarded paper on it and robot gripper grabbing items.

A photo of a robotic gripper on a piece of cardboard.AI-based systems guide robotic arms to grab materials from a stream of mixed recyclables and place them in the correct bins. Here, a tandem robot system operates at a Waste Connections recycling facility [top], and a single robot arm [bottom] recovers a piece of corrugated cardboard. The United States does a pretty good job when it comes to cardboard: In 2021, 91.4 percent of discarded cardboard was recycled, according to the American Forest and Paper Association.AMP Robotics

My company first began using AI in 2016 to extract empty cartons from other recyclables at a facility in Colorado; today, we have systems installed in more than 25 U.S. states and six countries. We weren’t the first company to try AI sorting, but it hadn’t previously been used commercially. And we have steadily expanded the types of recyclables our systems can recognize and sort.

AI makes it theoretically possible to recover all of the recyclables from a mixed-material stream at accuracy approaching 100 percent, entirely based on image analysis. If an AI-based sorting system can see an object, it can accurately sort it.

Consider a particularly challenging material for today’s recycling sorters: high-density polyethylene (HDPE), a plastic commonly used for detergent bottles and milk jugs. (In the United States, Europe, and China, HDPE products are labeled as No. 2 recyclables.) In a system that relies on hyperspectral imaging, batches of HDPE tend to be mixed with other plastics and may have paper or plastic labels, making it difficult for the hyperspectral imagers to detect the underlying object’s chemical composition.

An AI-driven computer-vision system, by contrast, can determine that a bottle is HDPE and not something else by recognizing its packaging. Such a system can also use attributes like color, opacity, and form factor to increase detection accuracy, and even sort by color or specific product, reducing the amount of reprocessing needed. Though the system doesn’t attempt to understand the meaning of words on labels, the words are part of an item’s visual attributes.

We at AMP Robotics have built systems that can do this kind of sorting. In the future, AI systems could also sort by combinations of material and by original use, enabling food-grade materials to be separated from containers that held household cleaners, and paper contaminated with food waste to be separated from clean paper.

Training a neural network to detect objects in the recycling stream is not easy. It is at least several orders of magnitude more challenging than recognizing faces in a photograph, because there can be a nearly infinite variety of ways that recyclable materials can be deformed, and the system has to recognize the permutations.

Inside the Sorting Center

An illustration of inside the Sorting Center.

Chris Philpot

Today’s recycling facilities use mechanical sorting, optical hyperspectral sorting, and human workers. Here’s what typically happens after the recycling truck leaves your house with the contents of your blue bin.

Trucks unload on a concrete pad, called the tip floor. A front-end loader scoops up material in bulk and dumps it onto a conveyor belt, typically at a rate of 30 to 60 tonnes per hour.

The first stage is the presort. Human workers remove large or problematic items that shouldn’t have made it onto collection trucks in the first place—bicycles, big pieces of plastic film, propane canisters, car transmissions.

An illustration of the process in the sorting line.

Sorting machines that rely on optical hyperspectral imaging or human workers separate fiber (office paper, cardboard, magazines—referred to as 2D products, as they are mostly flat) from the remaining plastics and metals. In the case of the optical sorters, cameras stare down at the material rolling down the conveyor belt, detect an object made of the target substance, and then send a message to activate a bank of electronically controllable solenoids to divert the object into a collection bin.

An illustration of the process in the sorting line.

The nonfiber materials pass through a mechanical system with densely packed camlike wheels. Large items glide past while small items, like that recyclable fork you thoughtfully deposited in your blue bin, slip through, headed straight for landfill—they are just too small to be sorted. Machines also smash glass, which falls to the bottom and is screened out.

An illustration of the process in the sorting line.

The rest of the stream then passes under overhead magnets, which collect items made of ferrous metals, and an eddy-current-inducing machine, which jolts nonferrous metals to another collection area.

An illustration of the process in the sorting line.

At this point, mostly plastics remain. More hyperspectral sorters, in series, can pull off plastics one type—like the HDPE of detergent bottles and the PET of water bottles—at a time.

Finally, whatever is left—between 10 to 30 percent of what came in on the trucks—goes to landfill.

An illustration of the process in the sorting line.

In the future, AI-driven robotic sorting systems and AI inspection systems could replace human workers at most points in this process. In the diagram, red icons indicate where AI-driven robotic systems could replace human workers and a blue icon indicates where an AI auditing system could make a final check on the success of the sorting effort.

It’s hard enough to train a neural network to identify all the different types of bottles of laundry detergent on the market today, but it’s an entirely different challenge when you consider the physical deformations that these objects can undergo by the time they reach a recycling facility. They can be folded, torn, or smashed. Mixed into a stream of other objects, a bottle might have only a corner visible. Fluids or food waste might obscure the material.

We train our systems by giving them images of materials belonging to each category, sourced from recycling facilities around the world. My company now has the world’s largest data set of recyclable material images for use in machine learning.

Using this data, our models learn to identify recyclables in the same way their human counterparts do, by spotting patterns and features that distinguish different materials. We continuously collect random samples from all the facilities that use our systems, and then annotate them, add them to our database, and retrain our neural networks. We also test our networks to find models that perform best on target material and do targeted additional training on materials that our systems have trouble identifying correctly.

In general, neural networks are susceptible to learning the wrong thing. Pictures of cows are associated with milk packaging, which is commonly produced as a fiber carton or HDPE container. But milk products can also be packaged in other plastics; for example, single-serving milk bottles may look like the HDPE of gallon jugs but are usually made from an opaque form of the PET (polyethylene terephthalate) used for water bottles. Cows don’t always mean fiber or HDPE, in other words.

There is also the challenge of staying up to date with the continual changes in consumer packaging. Any mechanism that relies on visual observation to learn associations between packaging and material types will need to consume a steady stream of data to ensure that objects are classified accurately.

But we can get these systems to work. Right now, our systems do really well on certain categories—more than 98 percent accuracy on aluminum cans—and are getting better at distinguishing nuances like color, opacity, and initial use (spotting those food-grade plastics).

Now thatAI-basedsystems are ready to take on your recyclables, how might things change? Certainly, they will boost the use of robotics, which is only minimally used in the recycling industry today. Given the perpetual worker shortage in this dull and dirty business, automation is a path worth taking.

AI can also help us understand how well today’s existing sorting processes are doing and how we can improve them. Today, we have a very crude understanding of the operational efficiency of sorting facilities—we weigh trucks on the way in and weigh the output on the way out. No facility can tell you the purity of the products with any certainty; they only audit quality periodically by breaking open random bales. But if you placed an AI-powered vision system over the inputs and outputs of relevant parts of the sorting process, you’d gain a holistic view of what material is flowing where. This level of scrutiny is just beginning in hundreds of facilities around the world, and it should lead to greater efficiency in recycling operations. Being able to digitize the real-time flow of recyclables with precision and consistency also provides opportunities to better understand which recyclable materials are and are not currently being recycled and then to identify gaps that will allow facilities to improve their recycling systems overall.

Sorting Robot Picking Mixed PlasticsAMP Robotics

But to really unleash the power of AI on the recycling process, we need to rethink the entire sorting process. Today, recycling operations typically whittle down the mixed stream of materials to the target material by removing nontarget material—they do a “negative sort,” in other words. Instead, using AI vision systems with robotic pickers, we can perform a “positive sort.” Instead of removing nontarget material, we identify each object in a stream and select the target material.

To be sure, our recovery rate and purity are only as good as our algorithms. Those numbers continue to improve as our systems gain more experience in the world and our training data set continues to grow. We expect to eventually hit purity and recovery rates of 100 percent.

The implications of moving from more mechanical systems to AI are profound. Rather than coarsely sorting to 80 percent purity and then manually cleaning up the stream to 95 percent purity, a facility can reach the target purity on the first pass. And instead of having a unique sorting mechanism handling each type of material, a sorting machine can change targets just by a switch in algorithm.

The use of AI also means that we can recover materials long ignored for economic reasons. Until now, it was only economically viable for facilities to pursue the most abundant, high-value items in the waste stream. But with machine-learning systems that do positive sorting on a wider variety of materials, we can start to capture a greater diversity of material at little or no overhead to the business. That’s good for the planet.

We are beginning to see a few AI-based secondary recycling facilities go into operation, with Amp’s technology first coming online in Denver in late 2020. These systems are currently used where material has already passed through a traditional sort, seeking high-value materials missed or low-value materials that can be sorted in novel ways and therefore find new markets.

Thanks to AI, the industry is beginning to chip away at the mountain of recyclables that end up in landfills each year—a mountain containing billions of tons of recyclables representing billions of dollars lost and nonrenewable resources wasted.

This article appears in the July 2022 print issue as “AI Takes a Dumpster Dive .”

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The Conversation (7)
Frank Morales27 Sep, 2022
SM

Thank you for sharing this insightful information and topic.

Jeffrey Turner19 Nov, 2022
INDV

Industrial Electrician in training here with a failed foray into computer science here: To fully replace a sorter, you need to replace the human hand and nervous system. This involves being able to pick up any object that is moving on a conveyor belt. This may involve significant engineering limits for the following reasons: a program to control the actuators and sensors on a robot hand has to design an algorithm to create an algorithm to control that movement for any irregular shaped item that comes down the line. Not only is the design of such self aware algorithm nearly impossible, but such an engineer would be extremely hard pressed to make it time efficient without running into the hundreds of thousands of lines of code that would cause a half to full second delay before the robot starts actuating on a modern processor, by which point, the item has passed on the conveyor line.

Shrenik Jobanputra21 Oct, 2022
INDV

Thank you for writing this. I am very happy that such AI based technology is being utilised for saving our planet! Good on you AMP Robotics!

Convincing Consumers To Buy EVs

How range, affordability, reliability, and behavioral changes figure into purchase decisions

15 min read
A collage showing four current electric vehicles. The EV's shown are: Mercedes-EQE SUV, Hyundai IONIQ 5, CHEVROLET EQUINOX EV 3LT, and Lucid Air.

Four EVs, from economy to luxury, currently for sale in the U.S. From top left clock wise: The Mercedes-EQE SUV, Hyundai IONIQ 5, CHEVROLET EQUINOX EV 3LT, and Lucid Air.

Credits: Mercedes-Benz Group AG; Hyundai Motor America; Chevrolet; Lucid.

With the combination of requiring all new light-duty vehicles sold in New York State be zero-emission by 2035, investments in electric vehicles charging stations, and state and federal EV rebates, “you’re going to see that you have no more excuses” for not buying an EV, according to New York Governor Kathy Hochul.

The EV Transition Explained

This is the tenth in a series of articles exploring the major technological and social challenges that must be addressed as we move from vehicles with internal-combustion engines to electric vehicles at scale. In reviewing each article, readers should bear in mind Nobel Prize–winning physicist Richard Feynman’s admonition: “For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled.”

Perhaps, but getting the vast majority of 111 million US households who own one or more light duty internal combustion vehicles to switch to EVs is going to take time. Even if interest in purchasing an EV is increasing, close to 70 percent of Americans are still leaning towards buying an ICE vehicles as their next purchase. In the UK, only 14 percent of drivers plan to purchase an EV as their next car.

Even when there is an expressed interest in purchasing a battery electric or hybrid vehicle, it often did not turn into an actual purchase. A 2022 CarGurus survey found that 35 percent of new car buyers expressed an interest in purchasing a hybrid, but only 13 percent eventually did. Similarly, 22 percent expressed interest in a battery electric vehicle (BEV), but only 5 percent bought one.

Each potential EV buyer assesses their individual needs against the benefits and risks an EV offers. However, until mainstream public confidence reaches the point where the perceived combination of risks of a battery electric vehicle purchase (range, affordability, reliability and behavioral changes) match that of an ICE vehicle, then EV purchases are going to be the exception rather than the norm.

How much range is enough?

Studies differ about how far drivers want to be able to go between charges. One Bloombergstudy found 341 miles was the average range desired, while Deloitte Consulting’s2022 Global Automotive Consumer Study found U.S. consumers want to be able to travel 518 miles on a fully charged battery in a BEV that costs $50,000 or less.

Arguments over how much range is needed are contentious. There are some who argue that because 95 percent of American car trips are 30 miles or less, a battery range of 250 miles or less is all that is needed. They also point out that this would reduce the price of the EV, since batteries account for about 30 percent of an EVs total cost. In addition, using smaller batteries would allow more EVs to be built, and potentially relieve pressure on the battery supply chain. If longer trips are needed, well, “bring some patience and enjoy the charging experience” seems to be the general advice.

While perhaps logical, these arguments are not going to influence typical buying decisions much. The first question potential EV buyers are going to ask themselves is, “Am I going to be paying more for a compromised version of mobility?” says Alexander Edwards, President of Strategic Vision, a research-based consultancy that aims to understand human behavior and decision-making.


 Driver\u2019s side view of 2024 Chevrolet Equinox EV 3LT in Riptide Blue driving down a roadDriver’s side view of 2024 Chevrolet Equinox EV 3LT.Chevrolet

Edwards explains potential customers do not have range anxietyper se: If they believe they require a vehicle that must go 400 miles before stopping, “even if once a month, once a quarter, or once a year,” all vehicles that cannot meet that criteria will be excluded from their buying decision. Range anxiety, therefore, is more a concern for EV owners. Edwards points out that regarding range, most BEV owners own at least one ICE vehicle to meet their long-distance driving needs.

What exactly is the “range” of a BEV is itself becoming a heated point of contention. While ICE vehicles driving ranges are affected by weather and driving conditions, the effects are well-understood after decades of experience. This experience is lacking with non-EV owners. Extreme heat and cold negatively affect EV battery ranges and charging time, as do driving speeds and terrain.

Peter Rawlinson serves as the Chief Executive Officer and Chief Technology Officer of Lucid.Peter Rawlinson serves as the CEO and CTO of Lucid.Lucid

Some automakers are reticent to say how much range is affected under differing conditions. Others, like Ford’s CEO Jim Farley, freely admits, “If you’re pulling 10,000 pounds, an electric truck is not the right solution. And 95 percent of our customers tow more than 10,000 pounds.” GM, though, is promising it will meet heavier towing requirements with its 2024 Chevrolet Silverado EV. However, Lucid Group CEO Peter Rawlinson in a non-too subtle dig at both Ford and GM said, “The correct solution for an affordable pickup truck today is the internal combustion engine.”

Ford’s Farley foresees that the heavy-duty truck segment will be sticking with ICE trucks for a while, as “it will probably go hydrogen fuel cell before it goes pure electric.” Many in the auto industry are warning that realistic BEV range numbers under varying conditions need to be widely published, else risk creating a backlash against EVs in general.

Range risk concerns obviously are tightly coupled to EV charging availability. Most charging is assumed to take place at home, but this is not an option for many home or apartment tenants. Even those with homes, their garages may not be available for EV charging. Scarce and unreliable EV charging opportunities, as well as publicized EV road trip horror stories, adds to both the potential EV owners’ current perceived and real range satisfaction risk.

EVs ain’t cheap

Price is another EV purchase risk that is comparable to EV range. Buying a new car is the second most expensive purchase a consumer makes behind buying a house. Spending nearly 100 percent of an annual US median household income on an unfamiliar technology is not a minor financial ask.

That is one reason why legacy automakers and EV start-ups are attempting to follow Tesla’s success in the luxury vehicle segment, spending much of their effort producing vehicles that are “above the median average annual US household income, let alone buyer in new car market,” Strategic Vision’s Edwards says. On top of the twenty or so luxury EVs already or soon to be on the market, Sony and Honda recently announced that they would be introducing yet another luxury EV in 2026.

It is true that there are some EVs that will soon appear in the competitive price range of ICE vehicles like the low-end GM EV Equinox SUV presently priced around $30,000 with a 280-mile range. How long GM will be able to keep that price in the face of battery cost increases and inflationary pressure, is anyone’s guess. It has already started to increase the cost of its Chevrolet Bolt EVs, which it had slashed last year, “due to ongoing industry-related pricing pressures.”

An image of a Lucid  Air electric vehicle.The Lucid Air’s price ranges from $90,000 to $200,000 depending on options.Lucid.

Analysts believe Tesla intends to spark an EV price war before its competitors are ready for one. This could benefit consumers in the short-term, but could also have long-term downside consequences for the EV industry as a whole. Tesla fired its first shot over its competitors’ bows with a recently announced price cut from $65,990 to $52,990 for its basic Model Y, with a range of 330 miles. That makes the Model Y cost-competitive with Hyundai’s $45,500 IONIQ 5 e-SUV with 304 miles of range.

Tesla’s pricing power could be hard to counter, at least in the short term. Ford’s cheapest F-150 Lightning Pro is now $57,869 compared to $41,769 a year ago due to what Ford says are “ongoing supply chain constraints, rising material costs and other market factors.” The entry level F-150 XL with an internal combustion engine has risen in the past year from about $29,990 to $33,695 currently.

Carlos TavaresChief Executive OfficerExecutive Director of StellantisCarlos Tavares, CEO of Stellantis.Stellantis

Automakers like Stellantis, freely acknowledge that EVs are too expensive for most buyers, with Stellantis CEO Carlos Tavares even warning that if average consumers can’t afford EVs as ICE vehicle sales are banned, “There is potential for social unrest.” However, other automakers like BMW are quite unabashed about going after the luxury market which it terms “white hot.” BMW’s CEO Oliver Zipse does say the company will not leave the “lower market segment,” which includes the battery electric iX1 xDrive30 that retails for A$82,900 in Australia and slightly lower elsewhere. It is not available in the United States.

Mercedes-Benz CEO Ola Kallenius also believes luxury EVs will be a catalyst for greater EV adoption—eventually. But right now, 75 percent of its investment has been redirected at bringing luxury vehicles to market.

The fact that luxury EVs are more profitable no doubt helps keep automakers focused on that market. Ford’s very popular Mustang Mach-E is having trouble maintaining profitability, for instance, which has forced Ford to raise its base price from $43,895 to $46,895. Even in the Chinese market where smaller EV sales are booming, profits are not. Strains on profitability for automakers and their suppliers may increase further as battery metals prices increase, warns data analysis company S&P Global Mobility.

Jim Rowan, Volvo Cars' new CEO and President as of 21 March 2022Jim Rowan, Volvo Cars’ CEO and President.Volvo Cars

As a result, EVs are unlikely to match ICE vehicle prices (or profits) anytime soon even for smaller EV models, says Renault Group CEO Luca de Meo, because of the ever increasing cost of batteries. Mercedes Chief Technology Officer Marcus Schäferagrees and does not see EV/ICE price parity “with the [battery] chemistry we have today.” Volvo CEO Jim Rowan, disagrees with both of them, however, seeing ICE-EV price parity coming by 2025-2026.

Interestingly, a 2019 Massachusetts Institute of Technology (MIT) study predicted that as EVs became more widespread, battery prices would climb because the demand for lithium and other battery metals would rise sharply. As a result, the study indicated EV/ICE price parity was likely closer to 2030 with the expectation that new battery chemistries would be introduced by then.

Many argue, however, that total cost of ownership (TCO) should be used as the EV purchase decision criterion rather than sticker price. Total cost of ownership of EVs is generally less than an ICE vehicle over its expected life since they have lower maintenance costs and electricity is less expensive per mile than gasoline, and tax incentives and rebates help a lot as well.

However, how long it takes to hit the break-even point depends on many factors, like the cost differential of a comparable ICE vehicle, depreciation, taxes, insurance costs, the cost of electricity/petrol in a region, whether charging takes place at home, etc. And TCO rapidly loses it selling point appeal if electricity prices go up, however, as is happening in the UK and in Germany.

Even if the total cost of ownership is lower for an EV, a potential EV customer may not be interested if meeting today’s monthly auto payments is difficult. Extra costs like needing to install a fast charger at home, which can add several thousand dollars more, or higher insurance costs, which could add an extra $500-$600 a year, may also be seen as buying impediment and can change the TCO equation.

Reliability and other major tech risks

To perhaps distract wary EV buyers from range and affordability issues, the automakers have focused their efforts on highlighting EV performance. Raymond Roth, a director at financial advisory firm Stout Risius Ross, observes among automakers, “There’s this arms race right now of best in class performance” being the dominant selling point.

This “wow” experience is being pursued by every EV automaker. Mercedes CEO Kallenius, for example, says to convince its current luxury vehicle owners to an EV, “the experience for the customer in terms of the torque, the performance, everything [must be] fantastic.” Nissan, which seeks a more mass market buyer, runs commercials exclaiming, “Don’t get an EV for the ‘E’, but because it will pin you in your seat, sparks your imagination and takes your breath away.”

Ford believes it will earn $20 billion, Stellantis some $22.5 billion and GM $20 to $25 billion from paid software-enabled vehicle features by 2030.

EV reliability issues may also take one’s breath away. Reliability is “extremely important” to new-car buyers, according to a 2022 report from Consumer Reports (CR). Currently, EV reliability is nothing to brag about. CR’s report says that “On average, EVs have significantly higher problem rates than internal combustion engine (ICE) vehicles across model years 2019 and 2020.” BEVs dwell at the bottom of the rankings.

Reliability may prove to be an Achilles heel to automakers like GM and Ford. GM CEO Mary Barra has very publicly promised that GM would no longer build “ crappy cars.” The ongoing problems with the Chevy Bolt undercuts that promise, and if its new Equinox EV has issues, it could hurt sales. Ford has reliability problems of its own, paying $4 billion in warranty costs last year alone. Its e-Mustang has been subject to several recalls over the past year. Even perceived quality-leader Toyota has been embarrassed by wheels falling off weeks after the introduction of its electric bZ4X SUV, the first in a new series “bZ”—beyond zero—electric vehicles.

A vehicle is caught up in a mudslide in Silverado Canyon, Calif., Wednesday, March 10, 2021.A Tesla caught up in a mudslide in Silverado Canyon, Calif., on March 10, 2021. Jae C. Hong/AP Photo

Troubles with vehicle electronics, which has plagued ICE vehicles as well for some time, seems even worse in EVs according to Consumer Report’s data. This should not be surprising, since EVs are packed with the latest electronic and software features to make them attractive, like new biometric capability, but they often do not work. EV start-up Lucid is struggling with a range of software woes, and software problems have pushed back launches years at Audi, Porsche and Bentley EVs, which are part of Volkswagen Group.

Another reliability risk-related issue is getting an EV repaired when something goes awry, or there is an accident. Right now, there is a dearth of EV-certified mechanics and repair shops. The UK Institute of the Motor Industry (IMI) needs 90,000 EV-trained technicians by 2030. The IMI estimates that less than 7 percent of the country’s automotive service workforce of 200,000 vehicle technicians is EV qualified. In the US, the situation is not better. The National Institute for Automotive Service Excellence (ASE), which certifies auto repair technicians, says the US has 229,000 ASE-certified technicians. However, there are only some 3,100 certified for electric vehicles. With many automakers moving to reduce their dealership networks, resolving problems that over-the-air (OTA) software updates cannot fix might be troublesome.

Furthermore, the costs and time needed to repair an EV are higher than for ICE vehicles, according to the data analytics company CCC. Reasons include a greater need to use original equipment manufacturer (OEM) parts and the cost of scans/recalibration of the advanced driver assistance systems, which have been rising for ICE vehicles as well. Furthermore, technicians need to ensure battery integrity to prevent potential fires.

And some of batteries along with their battery management systems need work. Two examples: Recalls involving the GM Bolt and Hyundai Kona, with the former likely to cost GM $1.8 billion and Hyundai $800 million to fix, according to Stout’s 2021 Automotive Defect and Recall Report. Furthermore, the battery defect data compiled by Stout indicates “incident rates are rising as production is increasing and incidents commonly occur across global platforms,” with both design and manufacturing defects starting to appear.

For a time in New York City, one had to be a licensed engineer to drive a steam-powered auto. In some aspects, EV drivers return to these roots. This might change over time, but for now it is a serious issue.” —John Leslie King

CCC data indicate that when damaged, battery packs do need replacement after a crash, and more than 50 percent of such vehicles were deemed a total loss by the insurance companies. EVs also need to revisit the repair center more times after they’ve been repaired than ICE vehicles, hinting at the increased difficulty in repairing them. Additionally, EV tire tread wear needs closer inspection than on ICE vehicles. Lastly, as auto repair centers need to invest in new equipment to handle EVs, these costs will be passed along to customers for some time.

Electric vehicle and charging network cybersecurity is also growing as a perceived risk. A 2021 survey by insurance company HSB found that an increasing number of drivers, not only of EVs but ICE vehicles, are concerned about their vehicle’s security. Some 10 percent reported “a hacking incident or other cyber-attack had affected their vehicle,” HSB reported. Reports of charging stations being compromised are increasingly common.

The risk has reached the attention of the US Office of the National Cyber Director, which recently held a forum of government and automaker, suppliers and EV charging manufacturers focusing on “cybersecurity issues in the electric vehicle (EV) and electric vehicle supply equipment (EVSE) ecosystem.” The concern is that EV uptake could falter if EV charging networks are not perceived as being secure.

A sleeper risk that may explode into a massive problem is an EV owner’s right-to-repair their vehicle. In 2020, Massachusetts passed a law that allows a vehicle owner to take it to whatever repair shop they wish and gave independent repair shops the right to access the real-time vehicle data for diagnosis purposes. Auto dealers have sued to overturn the law, and some auto makers like Subaru and Kia have disabled the advanced telematic systems in cars sold in Massachusetts, often without telling new customers about it. GM and Stellantis have also said they cannot comply with the Massachusetts law, and are not planning to do so because it would compromise their vehicles’ safety and cybersecurity. The Federal Trade Commission is looking into the right-to-repair issue, and President Biden has come out in support of it.

You expect me to do what, exactly?

Failure to change consumer behavior poses another major risk to the EV transition. Take charging. It requires a new consumer behavior in terms of understanding how and when to charge, and what to do to keep an EV battery healthy. The information on the care and feeding of a battery as well as how to maximize vehicle range can resemble a manual for owning a new, exotic pet. It does not help when an automaker like Ford tells its F-150 Lightning owners they can extend their driving range by relying on the heated seats to stay warm instead of the vehicle’s climate control system.

Keeping in mind such issues, and how one might work around them, increases a driver’s cognitive load—things that must be remembered in case they must be acted on. “Automakers spent decades reducing cognitive load with dash lights instead of gauges, or automatic instead of manual transmissions,” says University of Michigan professor emeritus John Leslie King, who has long studied human interactions with machines.

King notes, “In the early days of automobiles, drivers and chauffeurs had to monitor and be able to fix their vehicles. They were like engineers. For a time in New York City, one had to be a licensed engineer to drive a steam-powered auto. In some aspects, EV drivers return to these roots. This might change over time, but for now it is a serious issue.”


The first-ever BMW iX1 xDrive30, Mineral White metallic, 20\u201c BMW Individual Styling 869i The first-ever BMW iX1 xDrive30, Mineral White metallic, 20“ BMW Individual Styling 869i BMW AG

This cognitive load keeps changing as well. For instance, “common knowledge” about when EV owners should charge is not set in concrete. The long-standing mantra for charging EV batteries has been do so at home from at night when electricity rates were low and stress on the electric grid was low. Recent research from Stanford University says this is wrong, at least for Western states.

Stanford’s research shows that electricity rates should encourage EV charging during the day at work or at public chargers to prevent evening grid peak demand problems, which could increase by as much as 25 percent in a decade. The Wall Street Journal quotes the study’s lead author Siobhan Powell as saying if everyone were charging their EVs at night all at once, “it would cause really big problems.”

Asking EV owners to refrain from charging their vehicles at home during the night is going to be difficult, since EVs are being sold on the convenience of charging at home. Transportation Secretary Pete Buttigieg emphasized this very point when describing how great EVs are to own, “And the main charging infrastructure that we count on is just a plug in the wall.”

EV owners increasingly find public charging unsatisfying and is “one of the compromises battery electric vehicle owners have to make,” says Strategic Vision’s Alexander Edwards, “that drives 25 percent of battery electric vehicle owners back to a gas powered vehicle.” Fixing the multiple problems underlying EV charging will not likely happen anytime soon.

Another behavior change risk relates to automakers’ desired EV owner post-purchase buying behavior. Automakers see EV (and ICE vehicle) advanced software and connectivity as a gateway to a software-as-a-service model to generate new, recurring revenue streams across the life of the vehicle. Automakers seem to view EVs as razors through which they can sell software as the razor blades. Monetizing vehicle data and subscriptions could generate $1.5 trillion by 2030, according to McKinsey.

VW thinks that it will generate “triple-digit-millions” in future sales through selling customized subscription services, like offering autonomous driving on a pay-per-use basis. It envisions customers would be willing to pay 7 euros per hour for the capability. Ford believes it will earn $20 billion, Stellantis some $22.5 billion and GM $20 to $25 billion from paid software-enabled vehicle features by 2030.

Already for ICE vehicles, BMW is reportedly offering an $18 a month subscription (or $415 for “unlimited” access) for heated front seats in multiple countries, but not the U.S. as of yet. GM has started charging $1,500 for a three-year “optional” OnStar subscription on all Buick and GMC vehicles as well as the Cadillac Escalade SUV whether the owner uses it or not. And Sony and Honda have announced their luxury EV will be subscription-based, although they have not defined exactly what this means in terms of standard versus paid-for features. It would not be surprising to see it follow Mercedes’ lead. The automaker will increase the acceleration of its EQ series if an owner pays a $1,200 a year subscription fee.

Essentially, automakers are trying to normalize paying for what used to be offered as standard or even an upgrade option. Whether they will be successful is debatable, especially in the U.S. “No one is going to pay for subscriptions,” says Strategic Vision’s Edwards, who points out that microtransactions are absolutely hated in the gaming community. Automakers risk a major consumer backlash by using them.

To get to EV at scale, each of the EV-related range, affordability, reliability and behavioral changes risks will need to be addressed by automakers and policy makers alike. With dozens of new battery electric vehicles becoming available for sale in the next two years, potential EV buyers now have a much great range of options than previously. The automakers who manage EV risks best— along with offering compelling overall platform performance—will be the ones starting to claw back some of their hefty EV investments.

No single risk may be a deal breaker for an early EV adopter, but for skeptical ICE vehicle owners, each risk is another reason not to buy, regardless of perceived benefits offered. If EV-only families are going to be the norm, the benefits of purchasing EVs will need to be above—and the risks associated with owning will need to match or be below—those of today’s and future ICE vehicles.

In the next articles of this series, we’ll explore the changes that may be necessary to personal lifestyles to achieve 2050 climate goals.

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Deploying Data Science and AI to Fight Wildlife Trafficking

NYU Tandon’s Juliana Freire is leading a team aimed at using data science to bring down criminals trafficking humans and exotic animals

5 min read
A colorful parrot behind bars

Wildlife trafficking has an unexpected new foe: computer science, data science, and machine learning.

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This is a sponsored article brought to you by NYU Tandon School of Engineering.

Wildlife trafficking is a lucrative market. While it’s hard to tell exactly how much money it brings in, the U.S. government estimates it’s in the billions of dollars a year. Animals and their parts are traded much like firearms or narcotics — through complex networks of suppliers, dealers, and buyers, who leave a bloody path in their wake. The destruction speaks for itself; species decimated, environments degraded, and innocent people victimized.

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