The former site of the We Energies Power Plant on Nov. 13, 2025, in Pleasant Prairie, Wis. (Photo by Joe Timmerman/Wisconsin Watch)

By some measures, the Pleasant Prairie Power Plant, once regarded locally as an “iconic industrial landmark,” had a good run.

Opened in 1980 near Lake Michigan in Kenosha County, it became Wisconsin’s largest generating plant, burning enough Wyoming coal, some 13,000 tons a day, to provide electricity for up to 1 million homes.

But over time, the plant became too expensive to operate. The owner, We Energies, shut it down after 38 years, in 2018.

We Energies customers, however, are still on the hook.

A portion of their monthly bills will continue to pay for Pleasant Prairie until 2039 — 21 years after the plant stopped producing electricity.

In fact, residential and business utility customers throughout Wisconsin owe nearly $1 billion on “stranded assets” — power plants like Pleasant Prairie that have been or will soon be shut down, a Wisconsin Watch investigation found.

That total will likely grow over the next five years with additional coal plants scheduled to cease operations.

Customers must pay not only for the debt taken on to build and upgrade the plants themselves, but also an essentially guaranteed rate of return for their utility company owners, long after the plants stop generating revenue themselves.

“We really have a hard time with utilities profiting off of dead power plants for decades,” said Todd Stuart, executive director of the Wisconsin Industrial Energy Group.

The $1 billion tab looms as Wisconsin utility companies aim to generate unprecedented amounts of electricity for at least seven major high-tech data centers that are proposed, approved or under construction. By one estimate, just two of the data centers, which are being built to support the growth of artificial intelligence, would use more electricity than all Wisconsin homes combined.

All of which raises an important question in Wisconsin, where electricity rates have exceeded the Midwest average for 20 years.

What happens to residents and other ratepayers if AI and data centers don’t pan out as planned, creating a new generation of stranded assets?

How much do Wisconsin ratepayers owe on stranded assets?

Of the five major investor-owned utilities operating in Wisconsin, two — We Energies and Wisconsin Public Service Corp. — have stranded assets on the books. Both companies are subsidiaries of Milwaukee-based WEC Energy Group.

As of December 2024, when the company released its most recent annual report, We Energies estimated a remaining value of more than $700 million across three power plants with recently retired units: Pleasant Prairie, Oak Creek and Presque Isle, a plant on Michigan’s Upper Peninsula.

Wisconsin Public Service Corp.’s December 2024 report listed roughly $30 million in remaining value on recently retired units at two power plants.

In total, utilities owned by WEC Energy Group will likely have over $1 billion in recently retired assets by the end of 2026.

The company also noted a remaining value of just under $250 million for its share of units at Columbia Generating Station slated to retire in 2029, alongside a remaining value of roughly $650 million for units at Oak Creek scheduled to retire next year.

Its customers will pay off that total, plus a rate of return, for years to come.

The company estimates that closing the Pleasant Prairie plant alone saved $2.5 billion, largely by avoiding future operating and maintenance costs and additional capital investments.

Both Wisconsin Power and Light and Madison Gas and Electric also own portions of the Columbia Energy Center, and Wisconsin Power and Light also operates a unit at the Edgewater Generating Station scheduled for retirement before the end of the decade. Neither company provided estimates of the values of those facilities at time of retirement. Andrew Stoddard, a spokesman for Alliant Energy, Wisconsin Power and Light’s parent company, argued against treating plants scheduled for retirement with value on the books as future stranded assets.

How stranded assets occurred: overcommitting to coal

In 1907, Wisconsin became one of the first states to regulate public utilities. The idea was that having competing companies installing separate gas or electric lines was inefficient, but giving companies regional monopolies would require regulation.

Utility companies get permission to build or expand power plants and to raise rates from the three-member state Public Service Commission. The commissioners, appointed by the governor, are charged with protecting ratepayers as well as utility company investors.

Stranded assets have occurred across the nation, partly because of the cost of complying with pollution control regulations. But another factor is that, while other utilities around the country moved to alternative sources of energy, Wisconsin utilities and, in turn, the PSC overbet on how long coal-fired plants would operate efficiently:

• In the years before We Energies pulled the plug on Pleasant Prairie, the plant had mostly gone dark in spring and fall. Not only had coal become more expensive than natural gas and renewables, but energy consumption stayed flat. By 2016, two years before Pleasant Prairie’s closure, natural gas eclipsed coal for electricity generation nationally.

• In 2011, We Energies invested nearly $1 billion into its coal-fired Oak Creek plant south of Milwaukee to keep it running for 30 more years. The plant, which began operating in 1965 and later became one of the largest in the country, is now scheduled to completely retire in 2026 — with $650 million on the books still owed. That will cost individual ratepayers nearly $30 per year for the next 17 years, according to RMI, a think tank specializing in clean energy policy. The majority of the debt tied to those units stems from “environmental controls we were required to install to meet federal and state rules,” WEC Energy Group spokesperson Brendan Conway said.

• In 2013, to settle pollution violations, Alliant Energy announced an investment of more than $800 million in the Columbia Energy Center plant in Portage, north of Madison. But by 2021, Alliant announced plans to begin closing the plant, though now it is expected to operate until at least 2029.

Various factors encourage construction and upgrades of power plants.

Building a plant can create upwards of 1,000 construction jobs, popular with politicians. Moreover, the Public Service Commission, being a quasi-judicial body, is governed by precedent. For example, if the PSC determined it was prudent to allow construction of a utility plant, that finding would argue in favor of approving a later expansion of that plant.

The PSC allowed utility companies “to overbuild the system,” said Tom Content, executive director of the Wisconsin Citizens Utility Board, a nonprofit advocate for utility customers. “I think the mistake was that we allowed so much investment, and continuing to double down on coal when it was becoming less economic.”

Utilities “profit off of everything they build or acquire,” Stuart said, “and so there is a strong motivation to put steel in the ground and perhaps to even overbuild.”

Conway, the WEC Energy Group spokesperson, argued that the utilities’ plans to retire plants amount to a net positive for customers.

“We began our power generation reshaping plan about a decade ago,” he wrote in an email. “That includes closing older, less-efficient power plants and building new renewable energy facilities and clean, efficient natural gas plants. This plan reduces emissions and is expected to provide customers significant savings — hundreds of millions of dollars — over the life of the plan.”

Guaranteed profits add to ratepayer burden

The built-in profits that utility companies enjoy, typically 9.8%, add to the stranded assets tab.

When the Public Service Commission approves construction of a new power plant, it allows the utility company to levy electricity rates high enough to recover its investment plus the specified rate of return — even after a plant becomes a stranded asset.

“We give them this license to have a monopoly, but the challenge is there’s no incentive for them to do the least-cost option,” Content said. “So, in terms of building new plants, there’s an incentive to build more … and there’s incentive to build too much.”

When the Pleasant Prairie plant was shut down in 2018, the PSC ruled that ratepayers would continue to pay We Energies to cover the cost of the plant itself, plus the nearly 10% profit. The plant’s remaining value, initially pegged at nearly $1 billion, remained at roughly $500 million as of December 2024.

Eliminating profits on closed plants would save ratepayers $300 million on debt payments due to be made into the early 2040s, according to Content’s group.

New ‘stranded assets’ threat: data centers

As artificial intelligence pervades society, it’s hard to fathom how much more electricity will have to be generated to power all of the data centers under construction or being proposed in Wisconsin.

We Energies alone wants to add enough energy to power more than 2 million homes. That effort is largely to serve one Microsoft data center under construction in Mount Pleasant, between Milwaukee and Racine, and a data center approved north of Milwaukee in Port Washington to serve OpenAI and Oracle AI programs. Microsoft calls the Mount Pleasant facility “the world’s most powerful data center.”

Data centers are also proposed for Beaver Dam, Dane County, Janesville, Kenosha and Menomonie.

The energy demand raises the risk of more stranded assets, should the data centers turn out to be a bubble rather than boom.

“The great fear is, you build all these power plants and transmission lines and then one of these data centers only is there for a couple years, or isn’t as big as promised, and then everybody’s left holding the bag,” Stuart said.

In an October Marquette Law School poll, 55% of those surveyed said the costs of data centers outweigh the benefits. Environmental groups have called for a pause on all data center approvals. Democratic and Republican leaders are calling for data centers to pay their own way and not rely on utility ratepayers or taxpayers to pay for their electricity needs.

Opposition in one community led nearly 10,000 people to become members of the Stop the Menomonie Data Center group on Facebook. In Janesville, voters are trying to require referendums for data centers. In Port Washington, opposition to the data center there led to three arrests during a city council meeting.

Utilities are scheduled in early 2026 to request permission from the Public Service Commission to build new power plants or expand existing plants to accommodate data centers.

Some states, such as Minnesota, have adopted laws prohibiting the costs of stranded assets from data centers being passed onto ratepayers.

Wisconsin has no such laws.

Shifting cost burden to utility companies

Currently, ratepayers are on the hook for paying off the full debt of stranded assets — unless a financial tool called securitization reduces the burden on ratepayers.

Securitization is similar to refinancing a mortgage. With the state’s permission, utilities can convert a stranded asset — which isn’t typically a tradeable financial product — into a specialized bond.

Utility customers must still pay back the bond. But the interest rate on the bond is lower than the utility’s standard profit margin, meaning customers save money.

A 2024 National Association of Regulatory Utility Commissioners report noted that utilities’ shareholders may prefer a “status quo” scenario in which customers pay stranded asset debts and the standard rate of return. Persuading utilities to agree to securitization can require incentives from regulators or lawmakers, the report added.

In some states, utilities can securitize the remaining value of an entire power plant. Michigan utility Consumers Energy, for instance, securitized two coal generating units retired in 2023, saving its customers more than $120 million.

In Wisconsin, however, utilities can securitize only the cost of pollution control equipment on power plants — added to older coal plants during the Obama administration, when utilities opted to retrofit existing plants rather than switching to new power sources.

In 2023, two Republican state senators, Robert Cowles of Green Bay and Duey Stroebel of Saukville, introduced legislation to allow the Public Service Commission to order securitization and allow securitization to be used to refinance all debt on stranded assets. The bill attracted some Democratic cosponsors, but was opposed by the Wisconsin Utilities Association and did not get a hearing.

Democratic Gov. Tony Evers proposed additional securitization in his 2025-27 budget, but the Legislature’s Republican-controlled Joint Finance Committee later scrapped the provision.

Even Wisconsin’s narrow approach to securitization is optional, however, and most utilities have chosen not to use it.

We Energies was the first Wisconsin utility to do so, opting in 2020 to securitize the costs of pollution control equipment at the Pleasant Prairie plant. Wisconsin’s Public Service Commission approved the request, saving an estimated $40 million. “We will continue to explore that option in the future,” Conway said.

But the PSC expressed “disappointment” in 2024 when We Energies “was not willing to pursue securitization” to save customers $117.5 million on its soon-to-retire Oak Creek coal plant. The utility noted state law doesn’t require securitization.

Stuart said that if utilities won’t agree to more securitization, they should accept a lower profit rate once an asset becomes stranded.

“It would be nice to ease that burden,” he said. “Just to say, hey, consumers got to suck it up and deal with it, that doesn’t sound right. The issue of stranded assets, like cost overruns, is certainly ripe for investigation.”

Comprehensive planning required elsewhere — but not Wisconsin

Avoiding future stranded assets could require a level of planning impossible under Wisconsin’s current regulatory structure.

When the state’s utilities propose new power plants, PSC rules require the commission to consider each new plant alone, rather than in the context of other proposed new plants and the state’s future energy needs. Operating without what is known as an integrated resource plan, or IRP, opened the PSC to overbuilding and creating more stranded assets. IRPs are touted as an orderly way to plan for future energy needs.

“There’s no real comprehensive look in Wisconsin,” Stuart said. “We’re one of the few regulated states that really doesn’t have a comprehensive plan for our utilities.

”We’ve been doing some of these projects kind of piecemeal, without looking at the bigger picture.”

Structured planning tools like IRPs date back to the 1980s, when concerns about cost overruns, fuel price volatility and overbuilding prompted regulators to step in. Minnesota and Michigan require utilities to file IRPs, as do a majority of states nationwide.

Evers proposed IRPs in his 2025-27 state budget, but Republican lawmakers removed that provision because it was a nonfiscal policy issue.

Northern States Power Company, which operates in Wisconsin and four other Midwestern states, is required by both Michigan and Minnesota to develop IRPs. “Because of these rules, we create a multi-state IRP every few years,” said Chris Ouellette, a spokesperson for Xcel Energy, the utility’s parent company.

Madison Gas and Electric, which only operates in Wisconsin, argued that its current planning process is superior to the IRP requirements in neighboring states. “A formal IRP mandate would add process without improving outcomes,” spokesperson Steve Schultz said. “Wisconsin’s current framework allows us to move quickly, maintain industry-leading reliability and protect customer costs during a period of rapid change.”

How to influence decisions relating to stranded assets

The devil will be in the details on whether the Public Service Commission adopts strong policies to prevent the expected wave of new power plant capacity from becoming stranded assets, consumer advocates say.

The current members, all appointed by Evers, are: chairperson Summer Strand, Kristy Nieto and Marcus Hawkins.

The public can comment on pending cases before the PSC via its website, by mail or at a public hearing. The commission posts notices of its public hearings, which can be streamed via YouTube.

Among the upcoming hearings on requests by utilities to generate more electricity for data centers:

Feb. 12: We Energies’ request to service data centers in Mount Pleasant and Port Washington. We Energies says the fees it proposes, known as tariffs, will prevent costs from being shifted from the data centers to other customers. The “party” hearing is not for public comment, but for interaction between PSC staff and parties in the case, such as We Energies and public interest groups.

Feb. 26: Another party hearing for a case in which Alliant Energy also said its proposed tariffs won’t benefit the data center in Beaver Dam at the expense of other customers.

To keep abreast of case developments, the PSC offers email notifications for document filings and meetings of the commission.

The PSC would not provide an official to be interviewed for this article. It issued a statement noting that utilities can opt to do securitization to ease the financial burden on ratepayers, adding:

“Beyond that, the commission has a limited set of tools provided under state law to protect customers from costs that arise from early power plant retirements. It would be up to the state Legislature to make changes to state law that would provide the commission with additional tools.”

On Nov. 6, state Sen. Jodi Habush Sinykin, D-Whitefish Bay, and Rep. Angela Stroud, D-Ashland, announced wide-ranging data center legislation. One provision of their proposal aims to ensure that data centers don’t push electricity costs onto other ratepayers.

But there is no provision on stranded assets.

This article first appeared on Wisconsin Watch and is republished here under a Creative Commons Attribution-NoDerivatives 4.0 International License. To republish, go to the original and consult the Wisconsin Watch republishing guidelines.

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Advancements in battery innovation are transforming both mobility and energy systems alike, according to Kurt Kelty, vice president of battery, propulsion, and sustainability at General Motors (GM). At the MIT Energy Initiative (MITEI) Fall Colloquium, Kelty explored how GM is bringing next-generation battery technologies from lab to commercialization, driving American battery innovation forward. The colloquium is part of the ongoing MITEI Presents: Advancing the Energy Transition speaker series.

At GM, Kelty’s team is primarily focused on three things: first, improving affordability to get more electric vehicles (EVs) on the road. “How do you drive down the cost?” Kelty asked the audience. “It's the batteries. The batteries make up about 30 percent of the cost of the vehicle.” Second, his team strives to improve battery performance, including charging speed and energy density. Third, they are working on localizing the supply chain. “We've got to build up our resilience and our independence here in North America, so we're not relying on materials coming from China,” Kelty explained.

To aid their efforts, resources are being poured into the virtualization space, significantly cutting down on time dedicated to research and development. Now, Kelty’s team can do modeling up front using artificial intelligence, reducing what previously would have taken months to a couple of days.

“If you want to modify … the nickel content ever so slightly, we can very quickly model: ‘OK, how’s that going to affect the energy density? The safety? How’s that going to affect the charge capability?’” said Kelty. “We can look at that at the cell level, then the pack level, then the vehicle level.”

Kelty revealed that they have found a solution that addresses affordability, accessibility, and commercialization: lithium manganese-rich (LMR) batteries. Previously, the industry looked to reduce costs by lowering the amount of cobalt in batteries by adding greater amounts of nickel. These high-nickel batteries are in most cars on the road in the United States due to their high range. LMR batteries, though, take things a step further by reducing the amount of nickel and adding more manganese, which drives the cost of batteries down even further while maintaining range.

Lithium-iron-phosphate (LFP) batteries are the chemistry of choice in China, known for low cost, high cycle life, and high safety. With LMR batteries, the cost is comparable to LFP with a range that is closer to high-nickel. “That’s what’s really a breakthrough,” said Kelty.

LMR batteries are not new, but there have been challenges to adopting them, according to Kelty. “People knew about it, but they didn’t know how to commercialize it. They didn’t know how to make it work in an EV,” he explained. Now that GM has figured out commercialization, they will be the first to market these batteries in their EVs in 2028.

Kelty also expressed excitement over the use of vehicle-to-grid technologies in the future. Using a bidirectional charger with a two-way flow of energy, EVs could charge, but also send power from their batteries back to the electrical grid. This would allow customers to charge “their vehicles at night when the electricity prices are really low, and they can discharge it during the day when electricity rates are really high,” he said.

In addition to working in the transportation sector, GM is exploring ways to extend their battery expertise into applications in grid-scale energy storage. “It’s a big market right now, but it’s growing very quickly because of the data center growth,” said Kelty.

When looking to the future of battery manufacturing and EVs in the United States, Kelty remains optimistic: “we’ve got the technology here to make it happen. We’ve always had the innovation here. Now, we’re getting more and more of the manufacturing. We’re getting that all together. We’ve got just tremendous opportunity here that I’m hopeful we’re going to be able to take advantage of and really build a massive battery industry here.”

This speaker series highlights energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. Visit MITEI’s Events page for more information on this and additional events.

© Photo: Gretchen Ertl

There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”

The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.

Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”

© Image: Igor Borisenko/iStock

Registration is now open for the 2026 ACT Expo, which returns to Las Vegas, Nevada, in the spring.

The 16th ACT Expo, held May 4-7 at the Las Vegas Convention Center, will feature sessions on AI and autonomy as well as zero-emission vehicles. Originally called the Advanced Clean Transportation, ACT Expo for short, will now be known solely as ACT Expo, which event producers TRC Companies, said reflects the “expanded scope across advanced, autonomous, connected, and clean transportation technologies.”

TRC noted that ACT Expo can no longer “be simply defined as the clean or advanced technology show — it has become so much more.”

ACT now stands for the following:

• Advanced, Autonomous, Alternative, AI, Analytics, Adaptable, Assets
• Clean, Commercial, Connected, Cost-Effective, Compliant, Charged, Carbon-free
• Transportation, Technology, Transition, Trailers, Telematics, TCO, Tires

The event, which annually attracts over 12,000 attendees and 500 exhibitors, “offers end-users the most current insight into the key technology trends driving the market today and in the years ahead, practical lessons from peers, direct access to every major OEM and industry supplier in the market, strategies to boost competitiveness and accelerate the use of high-tech and clean vehicles and fuel, and the relationships that drive long-term success,” a press release on the event states.

The ACT Expo traditionally has hosted one school-bus-specific session each year and features school buses on the trade floor from various manufacturers. This year, however, TRC Companies said ACT Expo will place a greater emphasis on the digital frontier, reflecting industry investment in software-defined vehicles, real-time data collection and analysis via the use of AI and autonomy.

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“Through end-user case studies, the event will highlight how these cutting-edge technologies are improving performance, safety, and ROI, while giving attendees a clear view of where and how they are scaling,” the release states.

In addition to the technologies, the conference will continue to highlight ultra-clean vehicles and low-carbon fuels, spotlighting infrastructure.

“The pace of change and acceleration of advanced technologies in commercial transportation is phenomenal; it’s unlike anything we have seen before,” stated Erik Neandross, president of Clean Transportation Solutions at TRC. “From the boardroom to the show floor, ACT Expo is the one place where C-suite representatives from fleets, OEMs, and infrastructure partners engage directly to shape real-world progress and the future of their businesses. It’s where fleet leaders learn what’s actually working in the field, what’s just around the corner, and where they can better understand proven strategies that can deliver both economic and environmental results.”

School Transportation News is a media sponsor of the event.

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As the school transportation industry wrestles with complex challenges—driver shortages, safety concerns and operational inefficiencies—artificial intelligence (AI) is often positioned as a silver bullet. Fleet management systems tout data optimization. Dash cams promise incident reduction. Digital platforms claim to centralize and simplify operations.

But in the rush to innovate, we risk forgetting what matters most: People. Specifically, the drivers, dispatchers and front-line staff who make student transportation possible every day. If AI is to truly move this industry forward, it must be rooted in empathy—not just algorithms.

Coaching, Not Surveillance
Take the growing adoption of AI-powered dash cameras. When framed solely as surveillance tools, these systems can alienate drivers. No one wants to feel like they’re being watched without context or support. However, when implemented with a focus on coaching rather than punishment, these same tools can become allies. Cameras that detect risky behaviors—such as distracted driving, hard-braking or rolling stops—can deliver real-time feedback and personalized training opportunities. This helps drivers improve their performance without feeling policed.

It’s a shift in mindset from compliance to confidence-building. Drivers begin to feel supported, not scrutinized. And fleets often see measurable improvements in safety outcomes and morale as a result.

Retention Through Respect
The transportation industry has a retention problem. Nationally, school bus operators report chronic shortages, with turnover rates frequently exceeding 50 percent. Recruitment incentives and signing bonuses help, but they rarely address the deeper issue: How drivers feel on the job.

This is where AI can play a powerful role, if used thoughtfully. Integrated platforms that
offer real-time route data, reliable communication and automated scheduling aren’t just operational tools. They’re stress reducers. When a school bus driver knows their route will be accurate, when help is one tap away, and that their feedback is acknowledged and
acted upon, it builds trust. And trust builds tenure. In some operations, these changes have reduced driver turnover by double digits. Not because of gimmicks or grand gestures but because the technology made drivers feel valued and protected.

The Quiet Power of Automation AI’s most human impact may come behind the scenes. The administrative burdens on drivers and staff, from payroll questions to incident reporting, can erode time, focus and job satisfaction. Enter virtual assistants, workflow automations and smart self-service tools. When designed well, they give employees 24/7 access to the information they need, cut response times and free up staff to focus on meaningful, person-to-person support.

This isn’t just about operational efficiency, it’s about respect. Respect for employees’ time. Respect for their need to focus on their core responsibilities. Respect for their mental bandwidth. It’s tempting to think of automation as impersonal. But when deployed with the employee experience in mind, it can be one of the most empathetic forms of technology.

Start With the End User Too often, transportation tech is built from the top down and optimized for operations managers, IT leaders, or compliance teams. But the most successful implementations flip that script. They ask, what do drivers actually need? What do dispatchers struggle with? Where do mechanics waste the most time? Empathy, in this sense, becomes a design principle. And when it is, adoption skyrockets. Engagement rises. Feedback loops get shorter. And frontline staff begin to see technology not as a burden—but as a partner.

The Bigger Opportunity We’re at a crossroads. AI and automation are poised to reshape school transportation over the next decade. But the question isn’t whether we’ll adopt these tools. It’s how we’ll use them. Will we chase efficiency at the cost of human connection? Or will we use technology to elevate the people who make the system work? The path forward requires us to recognize a simple truth: Buses don’t move students—people do. And when we center those people in our digital transformation efforts, everyone wins: the organization, the employees and most importantly, the children we’re entrusted to transport safely every day.

Editor’s Note: As reprinted from the September 2025 issue of School Transportation News.

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Gaurav Sharda is the chief technology officer for Beacon Mobility companies and in July won the SchoolTransportation News Innovator of the Year Award for his direction of new human-focused AI solutions.

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On May 6, MIT AgeLab’s Advanced Vehicle Technology (AVT) Consortium, part of the MIT Center for Transportation and Logistics, celebrated 10 years of its global academic-industry collaboration. AVT was founded with the aim of developing new data that contribute to automotive manufacturers, suppliers, and insurers’ real-world understanding of how drivers use and respond to increasingly sophisticated vehicle technologies, such as assistive and automated driving, while accelerating the applied insight needed to advance design and development. The celebration event brought together stakeholders from across the industry for a set of keynote addresses and panel discussions on critical topics significant to the industry and its future, including artificial intelligence, automotive technology, collision repair, consumer behavior, sustainability, vehicle safety policy, and global competitiveness.

Bryan Reimer, founder and co-director of the AVT Consortium, opened the event by remarking that over the decade AVT has collected hundreds of terabytes of data, presented and discussed research with its over 25 member organizations, supported members’ strategic and policy initiatives, published select outcomes, and built AVT into a global influencer with tremendous impact in the automotive industry. He noted that current opportunities and challenges for the industry include distracted driving, a lack of consumer trust and concerns around transparency in assistive and automated driving features, and high consumer expectations for vehicle technology, safety, and affordability. How will industry respond? Major players in attendance weighed in.

In a powerful exchange on vehicle safety regulation, John Bozzella, president and CEO of the Alliance for Automotive Innovation, and Mark Rosekind, former chief safety innovation officer of Zoox, former administrator of the National Highway Traffic Safety Administration, and former member of the National Transportation Safety Board, challenged industry and government to adopt a more strategic, data-driven, and collaborative approach to safety. They asserted that regulation must evolve alongside innovation, not lag behind it by decades. Appealing to the automakers in attendance, Bozzella cited the success of voluntary commitments on automatic emergency braking as a model for future progress. “That’s a way to do something important and impactful ahead of regulation.” They advocated for shared data platforms, anonymous reporting, and a common regulatory vision that sets safety baselines while allowing room for experimentation. The 40,000 annual road fatalities demand urgency — what’s needed is a move away from tactical fixes and toward a systemic safety strategy. “Safety delayed is safety denied,” Rosekind stated. “Tell me how you’re going to improve safety. Let’s be explicit.”

Drawing inspiration from aviation’s exemplary safety record, Kathy Abbott, chief scientific and technical advisor for the Federal Aviation Administration, pointed to a culture of rigorous regulation, continuous improvement, and cross-sectoral data sharing. Aviation’s model, built on highly trained personnel and strict predictability standards, contrasts sharply with the fragmented approach in the automotive industry. The keynote emphasized that a foundation of safety culture — one that recognizes that technological ability alone isn’t justification for deployment — must guide the auto industry forward. Just as aviation doesn’t equate absence of failure with success, vehicle safety must be measured holistically and proactively.

With assistive and automated driving top of mind in the industry, Pete Bigelow of Automotive News offered a pragmatic diagnosis. With companies like Ford and Volkswagen stepping back from full autonomy projects like Argo AI, the industry is now focused on Level 2 and 3 technologies, which refer to assisted and automated driving, respectively. Tesla, GM, and Mercedes are experimenting with subscription models for driver assistance systems, yet consumer confusion remains high. JD Power reports that many drivers do not grasp the differences between L2 and L2+, or whether these technologies offer safety or convenience features. Safety benefits have yet to manifest in reduced traffic deaths, which have risen by 20 percent since 2020. The recurring challenge: L3 systems demand that human drivers take over during technical difficulties, despite driver disengagement being their primary benefit, potentially worsening outcomes. Bigelow cited a quote from Bryan Reimer as one of the best he’s received in his career: “Level 3 systems are an engineer’s dream and a plaintiff attorney’s next yacht,” highlighting the legal and design complexity of systems that demand handoffs between machine and human.

In terms of the impact of AI on the automotive industry, Mauricio Muñoz, senior research engineer at AI Sweden, underscored that despite AI’s transformative potential, the automotive industry cannot rely on general AI megatrends to solve domain-specific challenges. While landmark achievements like AlphaFold demonstrate AI’s prowess, automotive applications require domain expertise, data sovereignty, and targeted collaboration. Energy constraints, data firewalls, and the high costs of AI infrastructure all pose limitations, making it critical that companies fund purpose-driven research that can reduce costs and improve implementation fidelity. Muñoz warned that while excitement abounds — with some predicting artificial superintelligence by 2028 — real progress demands organizational alignment and a deep understanding of the automotive context, not just computational power.

Turning the focus to consumers, a collision repair panel drawing Richard Billyeald from Thatcham Research, Hami Ebrahimi from Caliber Collision, and Mike Nelson from Nelson Law explored the unintended consequences of vehicle technology advances: spiraling repair costs, labor shortages, and a lack of repairability standards. Panelists warned that even minor repairs for advanced vehicles now require costly and complex sensor recalibrations — compounded by inconsistent manufacturer guidance and no clear consumer alerts when systems are out of calibration. The panel called for greater standardization, consumer education, and repair-friendly design. As insurance premiums climb and more people forgo insurance claims, the lack of coordination between automakers, regulators, and service providers threatens consumer safety and undermines trust. The group warned that until Level 2 systems function reliably and affordably, moving toward Level 3 autonomy is premature and risky.

While the repair panel emphasized today’s urgent challenges, other speakers looked to the future. Honda’s Ryan Harty, for example, highlighted the company’s aggressive push toward sustainability and safety. Honda aims for zero environmental impact and zero traffic fatalities, with plans to be 100 percent electric by 2040 and to lead in energy storage and clean power integration. The company has developed tools to coach young drivers and is investing in charging infrastructure, grid-aware battery usage, and green hydrogen storage. “What consumers buy in the market dictates what the manufacturers make,” Harty noted, underscoring the importance of aligning product strategy with user demand and environmental responsibility. He stressed that manufacturers can only decarbonize as fast as the industry allows, and emphasized the need to shift from cost-based to life-cycle-based product strategies.

Finally, a panel involving Laura Chace of ITS America, Jon Demerly of Qualcomm, Brad Stertz of Audi/VW Group, and Anant Thaker of Aptiv covered the near-, mid-, and long-term future of vehicle technology. Panelists emphasized that consumer expectations, infrastructure investment, and regulatory modernization must evolve together. Despite record bicycle fatality rates and persistent distracted driving, features like school bus detection and stop sign alerts remain underutilized due to skepticism and cost. Panelists stressed that we must design systems for proactive safety rather than reactive response. The slow integration of digital infrastructure — sensors, edge computing, data analytics — stems not only from technical hurdles, but procurement and policy challenges as well. 

Reimer concluded the event by urging industry leaders to re-center the consumer in all conversations — from affordability to maintenance and repair. With the rising costs of ownership, growing gaps in trust in technology, and misalignment between innovation and consumer value, the future of mobility depends on rebuilding trust and reshaping industry economics. He called for global collaboration, greater standardization, and transparent innovation that consumers can understand and afford. He highlighted that global competitiveness and public safety both hang in the balance. As Reimer noted, “success will come through partnerships” — between industry, academia, and government — that work toward shared investment, cultural change, and a collective willingness to prioritize the public good.

© Photo: Kelly Davidson Studio

The cleantech world is experiencing a quiet revolution. Artificial intelligence is no longer knocking at the door, it’s quietly remodeling the entire house....

The post The Ultimate Guide to AI in Cleantech appeared first on Cleantech Group.

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie