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      in Energy

      MIT Energy Initiative launches the Future Energy Systems Center

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      The MIT Energy Initiative (MITEI) has launched a new research consortium — the Future Energy Systems Center — to address the climate crisis and the role energy systems can play in solving it. This integrated effort engages researchers from across all of MIT to help the global community reach its goal of net-zero carbon emissions. The center examines the accelerating energy transition and collaborates with industrial leaders to reform the world’s energy systems. The center is part of “Fast Forward: MIT’s Climate Action Plan for the Decade,” MIT’s multi-pronged effort announced last year to address the climate crisis.

      The Future Energy Systems Center investigates the emerging technology, policy, demographics, and economics reshaping the landscape of energy supply and demand. The center conducts integrative analysis of the entire energy system — a holistic approach essential to understanding the cross-sectorial impact of the energy transition.

      “We must act quickly to get to net-zero greenhouse gas emissions. At the same time, we have a billion people around the world with inadequate access, or no access, to electricity — and we need to deliver it to them,” says MITEI Director Robert C. Armstrong, the Chevron Professor of Chemical Engineering. “The Future Energy Systems Center combines MIT’s deep knowledge of energy science and technology with advanced tools for systems analysis to examine how advances in technology and system economics may respond to various policy scenarios.”  

      The overarching focus of the center is integrative analysis of the entire energy system, providing insights into the complex multi-sectorial transformations needed to alter the three major energy-consuming sectors of the economy — transportation, industry, and buildings — in conjunction with three major decarbonization-enabling technologies — electricity, energy storage and low-carbon fuels, and carbon management. “Deep decarbonization of our energy system requires an economy-wide perspective on the technology options, energy flows, materials flows, life-cycle emissions, costs, policies, and socioeconomics consequences,” says Randall Field, the center’s executive director. “A systems approach is essential in enabling cross-disciplinary teams to work collaboratively together to address the existential crisis of climate change.”

      Through techno-economic and systems-oriented research, the center analyzes these important interactions. For example:

      •  Increased reliance on variable renewable energy, such as wind and solar, and greater electrification of transportation, industry, and buildings will require expansion of demand management and other solutions for balancing of electricity supply and demand across these areas.

      •  Likewise, balancing supply and demand will require deploying grid-scale energy storage and converting the electricity to low-carbon fuels (hydrogen and liquid fuels), which can in turn play a vital role in the energy transition for hard-to-decarbonize segments of transportation, industry, and buildings.

      •  Carbon management (carbon dioxide capture from industry point sources and from air and oceans; utilization/conversion to valuable products; transport; storage) will also play a critical role in decarbonizing industry, electricity, and fuels — both as carbon-mitigation and negative-carbon solutions.

      As a member-supported research consortium, the center collaborates with industrial experts and leaders — from both energy’s consumer and supplier sides — to gain insights to help researchers anticipate challenges and opportunities of deploying technology at the scale needed to achieve decarbonization. “The Future Energy Systems Center gives us a powerful way to engage with industry to accelerate the energy transition,” says Armstrong. “Working together, we can better understand how our current technology toolbox can be more effectively put to use now to reduce emissions, and what new technologies and policies will ultimately be needed to reach net-zero.”

      A steering committee, made up of 11 MIT professors and led by Armstrong, selects projects to create a research program with high impact on decarbonization, while leveraging MIT strengths and addressing interests of center members in pragmatic and scalable solutions. “MIT — through our recently released climate action plan — is committed to moving with urgency and speed to help wring carbon dioxide emissions out the global economy to resolve the growing climate crisis,” says Armstrong. “We have no time to waste.”

      The center members to date are: AECI, Analog Devices, Chevron, ConocoPhillips, Copec, Dominion, Duke Energy, Enerjisa, Eneva, Eni, Equinor, Eversource, Exelon, ExxonMobil, Ferrovial, Iberdrola, IHI, National Grid, Raizen, Repsol, Rio Tinto, Shell, Tata Power, Toyota Research Institute, and Washington Gas. More

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      in Environment

      New visions for better transportation

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      We typically experience transportation problems from the ground up. Waiting for a delayed bus, packing ourselves into a subway car, or crawling along in traffic, it is common to see such systems struggling at close range.

      Yet sometimes transportation solutions come from a high-level, top-down approach. That was the theme of the final talk in MIT’s Mobility Forum series, delivered on Friday by MIT Professor Thomas Magnanti, which centered on applying to transportation the same overarching analytical framework used in other domains, such as bioengineering.

      Magnanti’s remarks focused on a structured approach to problem-solving known as the 4M method — which stands for measuring, mining, modeling, and manipulating. In urban transportation planning, for instance, measuring and mining might involve understanding traffic flows. Modeling might simulate those traffic flows, and manipulating would mean engineering interventions: tolls, one-way streets, or other changes.

      “These are four things that interact quite a bit with each other,” said Magnanti, who is an Institute Professor — MIT’s highest faculty distinction — and a professor of operations research at the MIT Sloan School of Management. “And they provide us with a sense of how you can gather data and understand a system, but also how you can improve it.”

      Magnanti, a leading expert in operations research, pointed out that the 4M method can be applied to systems from physics to biomedical research. He outlined how it might be used to analyze transportations-related systems such as supply chains and warehouse movements.

      In all cases, he noted, applying the 4M concept to a system is an iterative process: Making changes to a system will likely produce new flows — of traffic and goods — and thus be subject to a new set of measurements.

      “One thing to notice here, once you manipulate the system, it changes the data,” Magnanti observed. “You’re doing this so you can hopefully improve operations, but it creates new data. So, you want to measure that new data again, you want to mine it, you want to model it again, and then manipulate it. … This is a continuing loop that we use in these systems.”

      Magnanti’s talk, “Understanding and Improving Transportation Systems,” was delivered online to a public audience of about 175 people. It was the 12th and final event of the MIT Mobility Forum in the fall 2021 semester. The event series is organized by the MIT Mobility Initiative, an Institute-wide effort to research and accelerate the evolution of transportation, at a time when decarbonization in the sector is critical.

      Other MIT Mobility Forum talks have focused on topics such as zero-environmental-impact aviation, measuring pedestrian flows in cities, autonomous vehicles, the impact of high-speed rail and subways on cities, values and equity in mobility design, and more.

      Overall, the forum “offers an opportunity to showcase the groundbreaking transportation research occurring across the Institute,” says Jinhua Zhao, an associate professor of transportation and city planning in MIT’s Department of Urban Studies and Planning, and director of the MIT Mobility Initiative.

      The initiative has held 39 such talks since it launched in 2020, and the series will continue again in the spring semester of 2022.

      One of the principal features of the forum, like the MIT Mobility Initiative in general, is that it “facilitates cross-disciplinary exchanges both within MIT and without,” Zhao says. Faculty and students from every school at MIT have participated in the forum, lending intellectual and methodological diversity to a broad field.

      For his part, Magnanti, who is both an engineer and operations researcher by training, embraced that interdisciplinary approach in his remarks, fielding a variety of audience questions after his talk, about research methods and other issues. Magnanti, who served from 2009 to 2017 as the founding president of the Singapore University of Technology and Design (with which MIT has had research collaborations), noted that the setting can heavily influence transportation research and progress.

      In Singapore, he noted, “They measure everything. They measure how people access the subway … and they use their data.” Of course, Singapore’s status as a city-state of modest size, among other factors, makes comprehensive transportation planning more feasible there. Still, Magnanti also noted that the infrastructure bill recently passed by the U.S. federal government is “going to provide lots of opportunities” for transportation improvements.

      And in general, Magnanti added, one of the best things academic leaders and research communities can do is to “continue to create a sense of excitement. Even when things are tough, the problems are going to be interesting.” More

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      in Environment

      New “risk triage” platform pinpoints compounding threats to US infrastructure

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      Over a 36-hour period in August, Hurricane Henri delivered record rainfall in New York City, where an aging storm-sewer system was not built to handle the deluge, resulting in street flooding. Meanwhile, an ongoing drought in California continued to overburden aquifers and extend statewide water restrictions. As climate change amplifies the frequency and intensity of extreme events in the United States and around the world, and the populations and economies they threaten grow and change, there is a critical need to make infrastructure more resilient. But how can this be done in a timely, cost-effective way?

      An emerging discipline called multi-sector dynamics (MSD) offers a promising solution. MSD homes in on compounding risks and potential tipping points across interconnected natural and human systems. Tipping points occur when these systems can no longer sustain multiple, co-evolving stresses, such as extreme events, population growth, land degradation, drinkable water shortages, air pollution, aging infrastructure, and increased human demands. MSD researchers use observations and computer models to identify key precursory indicators of such tipping points, providing decision-makers with critical information that can be applied to mitigate risks and boost resilience in infrastructure and managed resources.

      At MIT, the Joint Program on the Science and Policy of Global Change has since 2018 been developing MSD expertise and modeling tools and using them to explore compounding risks and potential tipping points in selected regions of the United States. In a two-hour webinar on Sept. 15, MIT Joint Program researchers presented an overview of the program’s MSD research tool set and its applications.  

      MSD and the risk triage platform

      “Multi-sector dynamics explores interactions and interdependencies among human and natural systems, and how these systems may adapt, interact, and co-evolve in response to short-term shocks and long-term influences and stresses,” says MIT Joint Program Deputy Director C. Adam Schlosser, noting that such analysis can reveal and quantify potential risks that would likely evade detection in siloed investigations. “These systems can experience cascading effects or failures after crossing tipping points. The real question is not just where these tipping points are in each system, but how they manifest and interact across all systems.”

      To address that question, the program’s MSD researchers have developed the MIT Socio-Environmental Triage (MST) platform, now publicly available for the first time. Focused on the continental United States, the first version of the platform analyzes present-day risks related to water, land, climate, the economy, energy, demographics, health, and infrastructure, and where these compound to create risk hot spots. It’s essentially a screening-level visualization tool that allows users to examine risks, identify hot spots when combining risks, and make decisions about how to deploy more in-depth analysis to solve complex problems at regional and local levels. For example, MST can identify hot spots for combined flood and poverty risks in the lower Mississippi River basin, and thereby alert decision-makers as to where more concentrated flood-control resources are needed.

      Successive versions of the platform will incorporate projections based on the MIT Joint Program’s Integrated Global System Modeling (IGSM) framework of how different systems and stressors may co-evolve into the future and thereby change the risk landscape. This enhanced capability could help uncover cost-effective pathways for mitigating and adapting to a wide range of environmental and economic risks.  

      MSD applications

      Five webinar presentations explored how MIT Joint Program researchers are applying the program’s risk triage platform and other MSD modeling tools to identify potential tipping points and risks in five key domains: water quality, land use, economics and energy, health, and infrastructure. 

      Joint Program Principal Research Scientist Xiang Gao described her efforts to apply a high-resolution U.S. water-quality model to calculate a location-specific, water-quality index over more than 2,000 river basins in the country. By accounting for interactions among climate, agriculture, and socioeconomic systems, various water-quality measures can be obtained ranging from nitrate and phosphate levels to phytoplankton concentrations. This modeling approach advances a unique capability to identify potential water-quality risk hot spots for freshwater resources.

      Joint Program Research Scientist Angelo Gurgel discussed his MSD-based analysis of how climate change, population growth, changing diets, crop-yield improvements and other forces that drive land-use change at the global level may ultimately impact how land is used in the United States. Drawing upon national observational data and the IGSM framework, the analysis shows that while current U.S. land-use trends are projected to persist or intensify between now and 2050, there is no evidence of any concerning tipping points arising throughout this period.  

      MIT Joint Program Research Scientist Jennifer Morris presented several examples of how the risk triage platform can be used to combine existing U.S. datasets and the IGSM framework to assess energy and economic risks at the regional level. For example, by aggregating separate data streams on fossil-fuel employment and poverty, one can target selected counties for clean energy job training programs as the nation moves toward a low-carbon future. 

      “Our modeling and risk triage frameworks can provide pictures of current and projected future economic and energy landscapes,” says Morris. “They can also highlight interactions among different human, built, and natural systems, including compounding risks that occur in the same location.”  

      MIT Joint Program research affiliate Sebastian Eastham, a research scientist at the MIT Laboratory for Aviation and the Environment, described an MSD approach to the study of air pollution and public health. Linking the IGSM with an atmospheric chemistry model, Eastham ultimately aims to better understand where the greatest health risks are in the United States and how they may compound throughout this century under different policy scenarios. Using the risk triage tool to combine current risk metrics for air quality and poverty in a selected county based on current population and air-quality data, he showed how one can rapidly identify cardiovascular and other air-pollution-induced disease risk hot spots.

      Finally, MIT Joint Program research affiliate Alyssa McCluskey, a lecturer at the University of Colorado at Boulder, showed how the risk triage tool can be used to pinpoint potential risks to roadways, waterways, and power distribution lines from flooding, extreme temperatures, population growth, and other stressors. In addition, McCluskey described how transportation and energy infrastructure development and expansion can threaten critical wildlife habitats.

      Enabling comprehensive, location-specific analyses of risks and hot spots within and among multiple domains, the Joint Program’s MSD modeling tools can be used to inform policymaking and investment from the municipal to the global level.

      “MSD takes on the challenge of linking human, natural, and infrastructure systems in order to inform risk analysis and decision-making,” says Schlosser. “Through our risk triage platform and other MSD models, we plan to assess important interactions and tipping points, and to provide foresight that supports action toward a sustainable, resilient, and prosperous world.”

      This research is funded by the U.S. Department of Energy’s Office of Science as an ongoing project. More

    • 63 Shares189 Views

      in Environment

      Making roadway spending more sustainable

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      The share of federal spending on infrastructure has reached an all-time low, falling from 30 percent in 1960 to just 12 percent in 2018.

      While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more sustainable and higher performing roads are still possible even with today’s limited budgets.

      The research, conducted by a team of current and former MIT Concrete Sustainability Hub (MIT CSHub) scientists and published in Transportation Research D, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase.

      The paper presents a novel budget allocation tool and pairs it with three innovative strategies for managing pavement networks: a mix of paving materials, a mix of short- and long-term paving actions, and a long evaluation period for those actions.

      This novel approach offers numerous benefits. When applied to a 30-year case study of the Iowa U.S. Route network, the MIT CSHub model and management strategies cut emissions by 20 percent while sustaining current levels of road quality. Achieving this with a conventional planning approach would require the state to spend 32 percent more than it does today. The key to its success is the consideration of a fundamental — but fraught — aspect of pavement asset management: uncertainty.

      Predicting unpredictability

      The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may fluctuate, budgets may tighten, and traffic levels may intensify. Climate (and climate change), too, can hasten unexpected repairs.

      Managing these uncertainties effectively means looking long into the future and anticipating possible changes.

      “Capturing the impacts of uncertainty is essential for making effective paving decisions,” explains Fengdi Guo, the paper’s lead author and a departing CSHub research assistant.

      “Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance — often resulting in suboptimal spending and outcomes.”

      To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management strategies.

      The planning approach, known as Probabilistic Treatment Path Dependence (PTPD), is based on machine learning and was devised by Guo.

      “Our PTPD model is composed of four steps,” he explains. “These steps are, in order, pavement damage prediction; treatment cost prediction; budget allocation; and pavement network condition evaluation.”

      The model begins by investigating every segment in an entire pavement network and predicting future possibilities for pavement deterioration, cost, and traffic.

      “We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions,” says Guo. “The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network.”

      The PTPD model not only seeks to minimize costs to agencies but also to users — in this case, drivers. These user costs can come primarily in the form of excess fuel consumption due to poor road quality.

      “One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions,” Randolph Kirchain, a principal research scientist at MIT CSHub and MIT Materials Research Laboratory (MRL) and one of the paper’s co-authors. “Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption.”

      After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model.

      A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.

      The MIT team selected two key metrics to compare the effectiveness of various treatment paths: pavement quality and greenhouse gas emissions. These metrics were then calculated for all pavement segments in the Iowa network.

      Improvement through variation

       The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered.

      Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using a mix of paving materials, a variety of long- and short-term paving repair actions (treatments), and longer time periods on which to base paving decisions.

      They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a five-year period for evaluating the outcomes of paving actions.

      With these two approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.

      Their case study found that the MIT approach offered substantial benefits. Pavement-related greenhouse gas emissions would fall by around 20 percent across the network over the whole period. Pavement performance improved as well. To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32 percent greater budget.

      “It’s worth noting,” says Guo, “that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.”

      Much of the improvement derived from the precision of the CSHub planning model. But the three treatment strategies also play a key role.

      “We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” says Kirchain.

      It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.

      The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.

      It’s not surprising that paving decisions are daunting to make; their impacts on the environment, driver safety, and budget levels are long-lasting. But rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.

      This research was supported through the MIT Concrete Sustainability Hub by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Energy

      3 Questions: Daniel Cohn on the benefits of high-efficiency, flexible-fuel engines for heavy-duty trucking

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      The California Air Resources Board has adopted a regulation that requires truck and engine manufacturers to reduce the nitrogen oxide (NOx) emissions from new heavy-duty trucks by 90 percent starting in 2027. NOx from heavy-duty trucks is one of the main sources of air pollution, creating smog and threatening respiratory health. This regulation requires the largest air pollution cuts in California in more than a decade. How can manufacturers achieve this aggressive goal efficiently and affordably?

      Daniel Cohn, a research scientist at the MIT Energy Initiative, and Leslie Bromberg, a principal research scientist at the MIT Plasma Science and Fusion Center, have been working on a high-efficiency, gasoline-ethanol engine that is cleaner and more cost-effective than existing diesel engine technologies. Here, Cohn explains the flexible-fuel engine approach and why it may be the most realistic solution — in the near term — to help California meet its stringent vehicle emission reduction goals. The research was sponsored by the Arthur Samberg MIT Energy Innovation fund.

      Q. How does your high-efficiency, flexible-fuel gasoline engine technology work?

      A. Our goal is to provide an affordable solution for heavy-duty vehicle (HDV) engines to emit low levels of nitrogen oxide (NOx) emissions that would meet California’s NOx regulations, while also quick-starting gasoline-consumption reductions in a substantial fraction of the HDV fleet.

      Presently, large trucks and other HDVs generally use diesel engines. The main reason for this is because of their high efficiency, which reduces fuel cost — a key factor for commercial trucks (especially long-haul trucks) because of the large number of miles that are driven. However, the NOx emissions from these diesel-powered vehicles are around 10 times greater than those from spark-ignition engines powered by gasoline or ethanol.

      Spark-ignition gasoline engines are primarily used in cars and light trucks (light-duty vehicles), which employ a three-way catalyst exhaust treatment system (generally referred to as a catalytic converter) that reduces vehicle NOx emissions by at least 98 percent and at a modest cost. The use of this highly effective exhaust treatment system is enabled by the capability of spark-ignition engines to be operated at a stoichiometric air/fuel ratio (where the amount of air matches what is needed for complete combustion of the fuel).

      Diesel engines do not operate with stoichiometric air/fuel ratios, making it much more difficult to reduce NOx emissions. Their state-of-the-art exhaust treatment system is much more complex and expensive than catalytic converters, and even with it, vehicles produce NOx emissions around 10 times higher than spark-ignition engine vehicles. Consequently, it is very challenging for diesel engines to further reduce their NOx emissions to meet the new California regulations.

      Our approach uses spark-ignition engines that can be powered by gasoline, ethanol, or mixtures of gasoline and ethanol as a substitute for diesel engines in HDVs. Gasoline has the attractive feature of being widely available and having a comparable or lower cost than diesel fuel. In addition, presently available ethanol in the U.S. produces up to 40 percent less greenhouse gas (GHG) emissions than diesel fuel or gasoline and has a widely available distribution system.

      To make gasoline- and/or ethanol-powered spark-ignition engine HDVs attractive for widespread HDV applications, we developed ways to make spark-ignition engines more efficient, so their fuel costs are more palatable to owners of heavy-duty trucks. Our approach provides diesel-like high efficiency and high power in gasoline-powered engines by using various methods to prevent engine knock (unwanted self-ignition that can damage the engine) in spark-ignition gasoline engines. This enables greater levels of turbocharging and use of higher engine compression ratios. These features provide high efficiency, comparable to that provided by diesel engines. Plus, when the engine is powered by ethanol, the required knock resistance is provided by the intrinsic high knock resistance of the fuel itself. 

      Q. What are the major challenges to implementing your technology in California?

      A. California has always been the pioneer in air pollutant control, with states such as Washington, Oregon, and New York often following suit. As the most populous state, California has a lot of sway — it’s a trendsetter. What happens in California has an impact on the rest of the United States.

      The main challenge to implementation of our technology is the argument that a better internal combustion engine technology is not needed because battery-powered HDVs — particularly long-haul trucks — can play the required role in reducing NOx and GHG emissions by 2035. We think that substantial market penetration of battery electric vehicles (BEV) in this vehicle sector will take a considerably longer time. In contrast to light-duty vehicles, there has been very little penetration of battery power into the HDV fleet, especially in long-haul trucks, which are the largest users of diesel fuel. One reason for this is that long-haul trucks using battery power face the challenge of reduced cargo capability due to substantial battery weight. Another challenge is the substantially longer charging time for BEVs compared to that of most present HDVs.

      Hydrogen-powered trucks using fuel cells have also been proposed as an alternative to BEV trucks, which might limit interest in adopting improved internal combustion engines. However, hydrogen-powered trucks face the formidable challenges of producing zero GHG hydrogen at affordable cost, as well as the cost of storage and transportation of hydrogen. At present the high purity hydrogen needed for fuel cells is generally very expensive.

      Q. How does your idea compare overall to battery-powered and hydrogen-powered HDVs? And how will you persuade people that it is an attractive pathway to follow?

      A. Our design uses existing propulsion systems and can operate on existing liquid fuels, and for these reasons, in the near term, it will be economically attractive to the operators of long-haul trucks. In fact, it can even be a lower-cost option than diesel power because of the significantly less-expensive exhaust treatment and smaller-size engines for the same power and torque. This economic attractiveness could enable the large-scale market penetration that is needed to have a substantial impact on reducing air pollution. Alternatively, we think it could take at least 20 years longer for BEVs or hydrogen-powered vehicles to obtain the same level of market penetration.

      Our approach also uses existing corn-based ethanol, which can provide a greater near-term GHG reduction benefit than battery- or hydrogen-powered long-haul trucks. While the GHG reduction from using existing ethanol would initially be in the 20 percent to 40 percent range, the scale at which the market is penetrated in the near-term could be much greater than for BEV or hydrogen-powered vehicle technology. The overall impact in reducing GHGs could be considerably greater.

      Moreover, we see a migration path beyond 2030 where further reductions in GHG emissions from corn ethanol can be possible through carbon capture and sequestration of the carbon dioxide (CO2) that is produced during ethanol production. In this case, overall CO2 reductions could potentially be 80 percent or more. Technologies for producing ethanol (and methanol, another alcohol fuel) from waste at attractive costs are emerging, and can provide fuel with zero or negative GHG emissions. One pathway for providing a negative GHG impact is through finding alternatives to landfilling for waste disposal, as this method leads to potent methane GHG emissions. A negative GHG impact could also be obtained by converting biomass waste into clean fuel, since the biomass waste can be carbon neutral and CO2 from the production of the clean fuel can be captured and sequestered.

      In addition, our flex-fuel engine technology may be synergistically used as range extenders in plug-in hybrid HDVs, which use limited battery capacity and obviates the cargo capability reduction and fueling disadvantages of long-haul trucks powered by battery alone.

      With the growing threats from air pollution and global warming, our HDV solution is an increasingly important option for near-term reduction of air pollution and offers a faster start in reducing heavy-duty fleet GHG emissions. It also provides an attractive migration path for longer-term, larger GHG reductions from the HDV sector. More

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      in Environment

      Smarter regulation of global shipping emissions could improve air quality and health outcomes

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      Emissions from shipping activities around the world account for nearly 3 percent of total human-caused greenhouse gas emissions, and could increase by up to 50 percent by 2050, making them an important and often overlooked target for global climate mitigation. At the same time, shipping-related emissions of additional pollutants, particularly nitrogen and sulfur oxides, pose a significant threat to global health, as they degrade air quality enough to cause premature deaths.

      The main source of shipping emissions is the combustion of heavy fuel oil in large diesel engines, which disperses pollutants into the air over coastal areas. The nitrogen and sulfur oxides emitted from these engines contribute to the formation of PM2.5, airborne particulates with diameters of up to 2.5 micrometers that are linked to respiratory and cardiovascular diseases. Previous studies have estimated that PM2.5  from shipping emissions contribute to about 60,000 cardiopulmonary and lung cancer deaths each year, and that IMO 2020, an international policy that caps engine fuel sulfur content at 0.5 percent, could reduce PM2.5 concentrations enough to lower annual premature mortality by 34 percent.

      Global shipping emissions arise from both domestic (between ports in the same country) and international (between ports of different countries) shipping activities, and are governed by national and international policies, respectively. Consequently, effective mitigation of the air quality and health impacts of global shipping emissions will require that policymakers quantify the relative contributions of domestic and international shipping activities to these adverse impacts in an integrated global analysis.

      A new study in the journal Environmental Research Letters provides that kind of analysis for the first time. To that end, the study’s co-authors — researchers from MIT and the Hong Kong University of Science and Technology — implement a three-step process. First, they create global shipping emission inventories for domestic and international vessels based on ship activity records of the year 2015 from the Automatic Identification System (AIS). Second, they apply an atmospheric chemistry and transport model to this data to calculate PM2.5 concentrations generated by that year’s domestic and international shipping activities. Finally, they apply a model that estimates mortalities attributable to these pollutant concentrations.

      The researchers find that approximately 94,000 premature deaths were associated with PM2.5 exposure due to maritime shipping in 2015 — 83 percent international and 17 percent domestic. While international shipping accounted for the vast majority of the global health impact, some regions experienced significant health burdens from domestic shipping operations. This is especially true in East Asia: In China, 44 percent of shipping-related premature deaths were attributable to domestic shipping activities.

      “By comparing the health impacts from international and domestic shipping at the global level, our study could help inform decision-makers’ efforts to coordinate shipping emissions policies across multiple scales, and thereby reduce the air quality and health impacts of these emissions more effectively,” says Yiqi Zhang, a researcher at the Hong Kong University of Science and Technology who led the study as a visiting student supported by the MIT Joint Program on the Science and Policy of Global Change.

      In addition to estimating the air-quality and health impacts of domestic and international shipping, the researchers evaluate potential health outcomes under different shipping emissions-control policies that are either currently in effect or likely to be implemented in different regions in the near future.

      They estimate about 30,000 avoided deaths per year under a scenario consistent with IMO 2020, an international regulation limiting the sulfur content in shipping fuel oil to 0.5 percent — a finding that tracks with previous studies. Further strengthening regulations on sulfur content would yield only slight improvement; limiting sulfur content to 0.1 percent reduces annual shipping-attributable PM2.5-related premature deaths by an additional 5,000. In contrast, regulating nitrogen oxides instead, involving a Tier III NOx Standard would produce far greater benefits than a 0.1-percent sulfur cap, with 33,000 further avoided deaths.

      “Areas with high proportions of mortalities contributed by domestic shipping could effectively use domestic regulations to implement controls,” says study co-author Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and a faculty affiliate of the MIT Joint Program. “For other regions where much damage comes from international vessels, further international cooperation is required to mitigate impacts.” More

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      in Energy

      Designing better batteries for electric vehicles

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      The urgent need to cut carbon emissions is prompting a rapid move toward electrified mobility and expanded deployment of solar and wind on the electric grid. If those trends escalate as expected, the need for better methods of storing electrical energy will intensify.

      “We need all the strategies we can get to address the threat of climate change,” says Elsa Olivetti PhD ’07, the Esther and Harold E. Edgerton Associate Professor in Materials Science and Engineering. “Obviously, developing technologies for grid-based storage at a large scale is critical. But for mobile applications — in particular, transportation — much research is focusing on adapting today’s lithium-ion battery to make versions that are safer, smaller, and can store more energy for their size and weight.”

      Traditional lithium-ion batteries continue to improve, but they have limitations that persist, in part because of their structure. A lithium-ion battery consists of two electrodes — one positive and one negative — sandwiched around an organic (carbon-containing) liquid. As the battery is charged and discharged, electrically charged particles (or ions) of lithium pass from one electrode to the other through the liquid electrolyte.

      One problem with that design is that at certain voltages and temperatures, the liquid electrolyte can become volatile and catch fire. “Batteries are generally safe under normal usage, but the risk is still there,” says Kevin Huang PhD ’15, a research scientist in Olivetti’s group.

      Another problem is that lithium-ion batteries are not well-suited for use in vehicles. Large, heavy battery packs take up space and increase a vehicle’s overall weight, reducing fuel efficiency. But it’s proving difficult to make today’s lithium-ion batteries smaller and lighter while maintaining their energy density — that is, the amount of energy they store per gram of weight.

      To solve those problems, researchers are changing key features of the lithium-ion battery to make an all-solid, or “solid-state,” version. They replace the liquid electrolyte in the middle with a thin, solid electrolyte that’s stable at a wide range of voltages and temperatures. With that solid electrolyte, they use a high-capacity positive electrode and a high-capacity, lithium metal negative electrode that’s far thinner than the usual layer of porous carbon. Those changes make it possible to shrink the overall battery considerably while maintaining its energy-storage capacity, thereby achieving a higher energy density.

      “Those features — enhanced safety and greater energy density — are probably the two most-often-touted advantages of a potential solid-state battery,” says Huang. He then quickly clarifies that “all of these things are prospective, hoped-for, and not necessarily realized.” Nevertheless, the possibility has many researchers scrambling to find materials and designs that can deliver on that promise.

      Thinking beyond the lab

      Researchers have come up with many intriguing options that look promising — in the lab. But Olivetti and Huang believe that additional practical considerations may be important, given the urgency of the climate change challenge. “There are always metrics that we researchers use in the lab to evaluate possible materials and processes,” says Olivetti. Examples might include energy-storage capacity and charge/discharge rate. When performing basic research — which she deems both necessary and important — those metrics are appropriate. “But if the aim is implementation, we suggest adding a few metrics that specifically address the potential for rapid scaling,” she says.

      Based on industry’s experience with current lithium-ion batteries, the MIT researchers and their colleague Gerbrand Ceder, the Daniel M. Tellep Distinguished Professor of Engineering at the University of California at Berkeley, suggest three broad questions that can help identify potential constraints on future scale-up as a result of materials selection. First, with this battery design, could materials availability, supply chains, or price volatility become a problem as production scales up? (Note that the environmental and other concerns raised by expanded mining are outside the scope of this study.) Second, will fabricating batteries from these materials involve difficult manufacturing steps during which parts are likely to fail? And third, do manufacturing measures needed to ensure a high-performance product based on these materials ultimately lower or raise the cost of the batteries produced?

      To demonstrate their approach, Olivetti, Ceder, and Huang examined some of the electrolyte chemistries and battery structures now being investigated by researchers. To select their examples, they turned to previous work in which they and their collaborators used text- and data-mining techniques to gather information on materials and processing details reported in the literature. From that database, they selected a few frequently reported options that represent a range of possibilities.

      Materials and availability

      In the world of solid inorganic electrolytes, there are two main classes of materials — the oxides, which contain oxygen, and the sulfides, which contain sulfur. Olivetti, Ceder, and Huang focused on one promising electrolyte option in each class and examined key elements of concern for each of them.

      The sulfide they considered was LGPS, which combines lithium, germanium, phosphorus, and sulfur. Based on availability considerations, they focused on the germanium, an element that raises concerns in part because it’s not generally mined on its own. Instead, it’s a byproduct produced during the mining of coal and zinc.

      To investigate its availability, the researchers looked at how much germanium was produced annually in the past six decades during coal and zinc mining and then at how much could have been produced. The outcome suggested that 100 times more germanium could have been produced, even in recent years. Given that supply potential, the availability of germanium is not likely to constrain the scale-up of a solid-state battery based on an LGPS electrolyte.

      The situation looked less promising with the researchers’ selected oxide, LLZO, which consists of lithium, lanthanum, zirconium, and oxygen. Extraction and processing of lanthanum are largely concentrated in China, and there’s limited data available, so the researchers didn’t try to analyze its availability. The other three elements are abundantly available. However, in practice, a small quantity of another element — called a dopant — must be added to make LLZO easy to process. So the team focused on tantalum, the most frequently used dopant, as the main element of concern for LLZO.

      Tantalum is produced as a byproduct of tin and niobium mining. Historical data show that the amount of tantalum produced during tin and niobium mining was much closer to the potential maximum than was the case with germanium. So the availability of tantalum is more of a concern for the possible scale-up of an LLZO-based battery.

      But knowing the availability of an element in the ground doesn’t address the steps required to get it to a manufacturer. So the researchers investigated a follow-on question concerning the supply chains for critical elements — mining, processing, refining, shipping, and so on. Assuming that abundant supplies are available, can the supply chains that deliver those materials expand quickly enough to meet the growing demand for batteries?

      In sample analyses, they looked at how much supply chains for germanium and tantalum would need to grow year to year to provide batteries for a projected fleet of electric vehicles in 2030. As an example, an electric vehicle fleet often cited as a goal for 2030 would require production of enough batteries to deliver a total of 100 gigawatt hours of energy. To meet that goal using just LGPS batteries, the supply chain for germanium would need to grow by 50 percent from year to year — a stretch, since the maximum growth rate in the past has been about 7 percent. Using just LLZO batteries, the supply chain for tantalum would need to grow by about 30 percent — a growth rate well above the historical high of about 10 percent.

      Those examples demonstrate the importance of considering both materials availability and supply chains when evaluating different solid electrolytes for their scale-up potential. “Even when the quantity of a material available isn’t a concern, as is the case with germanium, scaling all the steps in the supply chain to match the future production of electric vehicles may require a growth rate that’s literally unprecedented,” says Huang.

      Materials and processing

      In assessing the potential for scale-up of a battery design, another factor to consider is the difficulty of the manufacturing process and how it may impact cost. Fabricating a solid-state battery inevitably involves many steps, and a failure at any step raises the cost of each battery successfully produced. As Huang explains, “You’re not shipping those failed batteries; you’re throwing them away. But you’ve still spent money on the materials and time and processing.”

      As a proxy for manufacturing difficulty, Olivetti, Ceder, and Huang explored the impact of failure rate on overall cost for selected solid-state battery designs in their database. In one example, they focused on the oxide LLZO. LLZO is extremely brittle, and at the high temperatures involved in manufacturing, a large sheet that’s thin enough to use in a high-performance solid-state battery is likely to crack or warp.

      To determine the impact of such failures on cost, they modeled four key processing steps in assembling LLZO-based batteries. At each step, they calculated cost based on an assumed yield — that is, the fraction of total units that were successfully processed without failing. With the LLZO, the yield was far lower than with the other designs they examined; and, as the yield went down, the cost of each kilowatt-hour (kWh) of battery energy went up significantly. For example, when 5 percent more units failed during the final cathode heating step, cost increased by about $30/kWh — a nontrivial change considering that a commonly accepted target cost for such batteries is $100/kWh. Clearly, manufacturing difficulties can have a profound impact on the viability of a design for large-scale adoption.

      Materials and performance

      One of the main challenges in designing an all-solid battery comes from “interfaces” — that is, where one component meets another. During manufacturing or operation, materials at those interfaces can become unstable. “Atoms start going places that they shouldn’t, and battery performance declines,” says Huang.

      As a result, much research is devoted to coming up with methods of stabilizing interfaces in different battery designs. Many of the methods proposed do increase performance; and as a result, the cost of the battery in dollars per kWh goes down. But implementing such solutions generally involves added materials and time, increasing the cost per kWh during large-scale manufacturing.

      To illustrate that trade-off, the researchers first examined their oxide, LLZO. Here, the goal is to stabilize the interface between the LLZO electrolyte and the negative electrode by inserting a thin layer of tin between the two. They analyzed the impacts — both positive and negative — on cost of implementing that solution. They found that adding the tin separator increases energy-storage capacity and improves performance, which reduces the unit cost in dollars/kWh. But the cost of including the tin layer exceeds the savings so that the final cost is higher than the original cost.

      In another analysis, they looked at a sulfide electrolyte called LPSCl, which consists of lithium, phosphorus, and sulfur with a bit of added chlorine. In this case, the positive electrode incorporates particles of the electrolyte material — a method of ensuring that the lithium ions can find a pathway through the electrolyte to the other electrode. However, the added electrolyte particles are not compatible with other particles in the positive electrode — another interface problem. In this case, a standard solution is to add a “binder,” another material that makes the particles stick together.

      Their analysis confirmed that without the binder, performance is poor, and the cost of the LPSCl-based battery is more than $500/kWh. Adding the binder improves performance significantly, and the cost drops by almost $300/kWh. In this case, the cost of adding the binder during manufacturing is so low that essentially all the of the cost decrease from adding the binder is realized. Here, the method implemented to solve the interface problem pays off in lower costs.

      The researchers performed similar studies of other promising solid-state batteries reported in the literature, and their results were consistent: The choice of battery materials and processes can affect not only near-term outcomes in the lab but also the feasibility and cost of manufacturing the proposed solid-state battery at the scale needed to meet future demand. The results also showed that considering all three factors together — availability, processing needs, and battery performance — is important because there may be collective effects and trade-offs involved.

      Olivetti is proud of the range of concerns the team’s approach can probe. But she stresses that it’s not meant to replace traditional metrics used to guide materials and processing choices in the lab. “Instead, it’s meant to complement those metrics by also looking broadly at the sorts of things that could get in the way of scaling” — an important consideration given what Huang calls “the urgent ticking clock” of clean energy and climate change.

      This research was supported by the Seed Fund Program of the MIT Energy Initiative (MITEI) Low-Carbon Energy Center for Energy Storage; by Shell, a founding member of MITEI; and by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, under the Advanced Battery Materials Research Program. The text mining work was supported by the National Science Foundation, the Office of Naval Research, and MITEI.

      This article appears in the Spring 2021 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      Electrifying cars and light trucks to meet Paris climate goals

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      On Aug. 5, the White House announced that it seeks to ensure that 50 percent of all new passenger vehicles sold in the United States by 2030 are powered by electricity. The purpose of this target is to enable the U.S to remain competitive with China in the growing electric vehicle (EV) market and meet its international climate commitments. Setting ambitious EV sales targets and transitioning to zero-carbon power sources in the United States and other nations could lead to significant reductions in carbon dioxide and other greenhouse gas emissions in the transportation sector and move the world closer to achieving the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius relative to preindustrial levels.

      At this time, electrification of the transportation sector is occurring primarily in private light-duty vehicles (LDVs). In 2020, the global EV fleet exceeded 10 million, but that’s a tiny fraction of the cars and light trucks on the road. How much of the LDV fleet will need to go electric to keep the Paris climate goal in play? 

      To help answer that question, researchers at the MIT Joint Program on the Science and Policy of Global Change and MIT Energy Initiative have assessed the potential impacts of global efforts to reduce carbon dioxide emissions on the evolution of LDV fleets over the next three decades.

      Using an enhanced version of the multi-region, multi-sector MIT Economic Projection and Policy Analysis (EPPA) model that includes a representation of the household transportation sector, they projected changes for the 2020-50 period in LDV fleet composition, carbon dioxide emissions, and related impacts for 18 different regions. Projections were generated under four increasingly ambitious climate mitigation scenarios: a “Reference” scenario based on current market trends and fuel efficiency policies, a “Paris Forever” scenario in which current Paris Agreement commitments (Nationally Determined Contributions, or NDCs) are maintained but not strengthened after 2030, a “Paris to 2 C” scenario in which decarbonization actions are enhanced to be consistent with capping global warming at 2 C, and an “Accelerated Actions” scenario the caps global warming at 1.5 C through much more aggressive emissions targets than the current NDCs.

      Based on projections spanning the first three scenarios, the researchers found that the global EV fleet will likely grow to about 95-105 million EVs by 2030, and 585-823 million EVs by 2050. In the Accelerated Actions scenario, global EV stock reaches more than 200 million vehicles in 2030, and more than 1 billion in 2050, accounting for two-thirds of the global LDV fleet. The research team also determined that EV uptake will likely grow but vary across regions over the 30-year study time frame, with China, the United States, and Europe remaining the largest markets. Finally, the researchers found that while EVs play a role in reducing oil use, a more substantial reduction in oil consumption comes from economy-wide carbon pricing. The results appear in a study in the journal Economics of Energy & Environmental Policy.

      “Our study shows that EVs can contribute significantly to reducing global carbon emissions at a manageable cost,” says MIT Joint Program Deputy Director and MIT Energy Initiative Senior Research Scientist Sergey Paltsev, the lead author. “We hope that our findings will help decision-makers to design efficient pathways to reduce emissions.”  

      To boost the EV share of the global LDV fleet, the study’s co-authors recommend more ambitious policies to mitigate climate change and decarbonize the electric grid. They also envision an “integrated system approach” to transportation that emphasizes making internal combustion engine vehicles more efficient, a long-term shift to low- and net-zero carbon fuels, and systemic efficiency improvements through digitalization, smart pricing, and multi-modal integration. While the study focuses on EV deployment, the authors also stress for the need for investment in all possible decarbonization options related to transportation, including enhancing public transportation, avoiding urban sprawl through strategic land-use planning, and reducing the use of private motorized transport by mode switching to walking, biking, and mass transit.

      This research is an extension of the authors’ contribution to the MIT Mobility of the Future study. More