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Learn more - CREATE AN ACCOUNTSIGN IN JOIN IEEESIGN IN Enjoy more free content and benefits by creating an account Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE. CREATE AN ACCOUNTSIGN IN Energy Topic Type News Study: Recycled Lithium Batteries as Good as Newly Mined Cathodes made with novel direct-recycling beat commercial materials Prachi Patel 15 Oct 2021 3 min read lithium ion battery recycling iStockphoto battery recycling lithium-ion batteries recycling Lithium-ion batteries, with their use of riskily mined metals, tarnish the green image of EVs. Recycling to recover those valuable metals would minimize the social and environmental impact of mining, keep millions of tons of batteries from landfills, and cut the energy use and emissions created from making batteries. But while the EV battery recycling industry is starting to take off, getting carmakers to use recycled materials remains a hard sell. "In general, people's impression is that recycled material is not as good as virgin material," says Yan Wang, a professor of mechanical engineering at Worcester Polytechnic Institute. "Battery companies still hesitate to use recycled material in their batteries." A new study by Wang and a team including researchers from the US Advanced Battery Consortium (USABC), and battery company A123 Systems , shows that battery and carmakers needn't worry. The results, published in the journal Joule, shows that batteries with recycled cathodes can be as good as, or even better than those using new state-of-the-art materials. The team tested batteries with recycled NMC111 cathodes, the most common flavor of cathode containing a third each of nickel, manganese, and cobalt. The cathodes were made using a patented recycling technique that Battery Resourcers, a startup Wang co-founded, is now commercializing. The recycled material showed a more porous microscopic structure that is better for lithium ions to slip in and out of. The result: batteries with an energy density similar to those made with commercial cathodes, but which also showed up to 53% longer cycle life. While the recycled batteries weren't tested in cars, tests were done at industrially relevant scales. The researchers made 11 Ampere-hour industry-standard pouch cells loaded with materials at the same density as EV batteries. Engineers at A123 Systems did most of the testing, Wang says, using a protocol devised by the USABC to meet commercial viability goals for plug-in hybrid electric vehicles. He says the results prove that recycled cathode materials are a viable alternative to pristine materials. EV batteries are complex beasts, and recycling them isn't easy. It involves either burning them using lots of energy, or grinding and dissolving them in acids. Most large recycling companies, which have mainly been recycling consumer electronics batteries, and upcoming battery-recycling startups use these methods to produce separate elements to sell to battery material companies, which will in turn make the high-grade materials for car and battery makers. But the real value of an EV battery is in the cathode, Wang points out. Cathode materials are proprietary combinations of metals including nickel, manganese, and cobalt that are crafted into particles with specific sizes and structures. Battery Resources' recycling technology produces various ready-to-use NMC cathode materials based on what a car company wants. That means selling the recycled materials could turn a profit, something recycling companies say can be hard to do. "We are the only company that gives an output that is a cathode material," he says. "Other companies make elements. So their value added is less." Their technology involves shredding batteries and removing the steel cases, aluminum and copper wires, plastics, and pouch materials for recycling. The remaining black mass is dissolved in solvents, and the graphite, carbon and impurities are filtered out or chemically separated. Using a patented chemical technique, the nickel, manganese and cobalt are then mixed in desired ratios to make cathode powders. A few other researchers, and outfits such as the ReCell Center, a battery-recycling research collaboration supported by the U.S. Department of Energy, are also developing direct recycling technology. But they likely will not be producing high volumes of recycled cathode material any time soon. Battery Resources, meanwhile, is already selling their recycled materials to battery manufacturers at a small scale. The company plans to open its first commercial plant, which will be able to process 10,000 tons of batteries, in 2022. In September, they raised $70 million, with which they plan to launch two more facilities in Europe by the end of 2022. battery recycling lithium-ion batteries recycling Prachi Patel Prachi Patel is a freelance journalist based in Pittsburgh. She writes about energy, biotechnology, materials science, nanotechnology, and computing. The Conversation (0) Illustration of a window with the number "11" behind it and on a blue background. Topic Magazine Type Computing Opinion Windows 11 is Here, But Will It Run on Your PC? 7h 3 min read Left, screenshot from a drone video shows an aerial view of a dark brown and light brown dog in an enclosure. Right, red lava , white smoke and brown ash spew out of a hole. Robotics News Type Topic Drones to Attempt Rescue of Starving Dogs Stranded by Volcano 15h 3 min read Conceptual art of two colleagues sitting at computer desks on different sized stacks of money Topic Careers Type News Tech Salaries Jump in San Diego, Plunge in Dallas 19 Oct 2021 3 min read Related Stories Topic Type News Transportation Is Elon Musk Back In "Production Hell" With Tesla's 4680 Battery? Energy Topic News Type This Startup Says Their Battery Tech Beats Rivals By 10 Percent Energy Topic News Type EVs Will Drive A Lithium Supply Crunch Energy Topic Artificial Intelligence Type Feature Smokey the AI Smart image analysis algorithms, fed by cameras carried by drones and ground vehicles, can help power companies prevent forest fires Vikhyat Chaudhry 19 Oct 2021 7 min read Smokey the AI The 2021 Dixie Fire in northern California is suspected of being caused by Pacific Gas & Electric's equipment. The fire is the second-largest in California history. Robyn Beck/AFP/Getty Images The 2020 fire season in the United States was the worst in at least 70 years, with some 4 million hectares burned on the west coast alone. These West Coast fires killed at least 37 people, destroyed hundreds of structures, caused nearly US $20 billion in damage, and filled the air with smoke that threatened the health of millions of people. And this was on top of a 2018 fire season that burned more than 700,000 hectares of land in California, and a 2019-to-2020 wildfire season in Australia that torched nearly 18 million hectares. While some of these fires started from human carelessness--or arson--far too many were sparked and spread by the electrical power infrastructure and power lines. The California Department of Forestry and Fire Protection (Cal Fire) calculates that nearly 100,000 burned hectares of those 2018 California fires were the fault of the electric power infrastructure, including the devastating Camp Fire, which wiped out most of the town of Paradise. And in July of this year, Pacific Gas & Electric indicated that blown fuses on one of its utility poles may have sparked the Dixie Fire, which burned nearly 400,000 hectares. Until these recent disasters, most people, even those living in vulnerable areas, didn't give much thought to the fire risk from the electrical infrastructure. Power companies trim trees and inspect lines on a regular--if not particularly frequent--basis. However, the frequency of these inspections has changed little over the years, even though climate change is causing drier and hotter weather conditions that lead up to more intense wildfires. In addition, many key electrical components are beyond their shelf lives, including insulators, transformers, arrestors, and splices that are more than 40 years old. Many transmission towers, most built for a 40-year lifespan, are entering their final decade. The way the inspections are done has changed little as well. Historically, checking the condition of electrical infrastructure has been the responsibility of men walking the line. When they're lucky and there's an access road, line workers use bucket trucks. But when electrical structures are in a backyard easement, on the side of a mountain, or otherwise out of reach for a mechanical lift, line workers still must belt-up their tools and start climbing. In remote areas, helicopters carry inspectors with cameras with optical zooms that let them inspect power lines from a distance. These long-range inspections can cover more ground but can't really replace a closer look. Recently, power utilities have started using drones to capture more information more frequently about their power lines and infrastructure. In addition to zoom lenses, some are adding thermal sensors and lidar onto the drones. Thermal sensors pick up excess heat from electrical components like insulators, conductors, and transformers. If ignored, these electrical components can spark or, even worse, explode. Lidar can help with vegetation management, scanning the area around a line and gathering data that software later uses to create a 3-D model of the area. The model allows power system managers to determine the exact distance of vegetation from power lines. That's important because when tree branches come too close to power lines they can cause shorting or catch a spark from other malfunctioning electrical components. Aerial view of power lines surrounded by green vegetation. Two boxes on the left and right are labelled \u201cVegetation Encroachment\ u201d. AI-based algorithms can spot areas in which vegetation encroaches on power lines, processing tens of thousands of aerial images in days.Buzz Solutions Bringing any technology into the mix that allows more frequent and better inspections is good news. And it means that, using state-of-the-art as well as traditional monitoring tools, major utilities are now capturing more than a million images of their grid infrastructure and the environment around it every year. AI isn't just good for analyzing images. It can predict the future by looking at patterns in data over time. Now for the bad news. When all this visual data comes back to the utility data centers, field technicians, engineers, and linemen spend months analyzing it--as much as six to eight months per inspection cycle. That takes them away from their jobs of doing maintenance in the field. And it's just too long: By the time it's analyzed, the data is outdated. It's time for AI to step in. And it has begun to do so. AI and machine learning have begun to be deployed to detect faults and breakages in power lines. Multiple power utilities, including Xcel Energy and Florida Power and Light, are testing AI to detect problems with electrical components on both high- and low-voltage power lines. These power utilities are ramping up their drone inspection programs to increase the amount of data they collect (optical, thermal, and lidar), with the expectation that AI can make this data more immediately useful. My organization, Buzz Solutions, is one of the companies providing these kinds of AI tools for the power industry today. But we want to do more than detect problems that have already occurred--we want to predict them before they happen. Imagine what a power company could do if it knew the location of equipment heading towards failure, allowing crews to get in and take preemptive maintenance measures, before a spark creates the next massive wildfire. It's time to ask if an AI can be the modern version of the old Smokey Bear mascot of the United States Forest Service: preventing wildfires before they happen. Landscape view of water, trees and hilltops. In the foreground are electrical equipment and power lines. On the left, the equipment is labelled in green \u201cPorcelain Insulators Good\u201d and \u201cNo Nest\u201d. In the center is equipment circled in red, labeled \ u201cPorcelain Insulators Broken\u201d. Damage to power line equipment due to overheating, corrosion, or other issues can spark a fire.Buzz Solutions We started to build our systems using data gathered by government agencies, nonprofits like the Electrical Power Research Institute (EPRI), power utilities, and aerial inspection service providers that offer helicopter and drone surveillance for hire. Put together, this data set comprises thousands of images of electrical components on power lines, including insulators, conductors, connectors, hardware, poles, and towers. It also includes collections of images of damaged components, like broken insulators, corroded connectors, damaged conductors, rusted hardware structures, and cracked poles. We worked with EPRI and power utilities to create guidelines and a taxonomy for labeling the image data. For instance, what exactly does a broken insulator or corroded connector look like? What does a good insulator look like? We then had to unify the disparate data, the images taken from the air and from the ground using different kinds of camera sensors operating at different angles and resolutions and taken under a variety of lighting conditions. We increased the contrast and brightness of some images to try to bring them into a cohesive range, we standardized image resolutions, and we created sets of images of the same object taken from different angles. We also had to tune our algorithms to focus on the object of interest in each image, like an insulator, rather than consider the entire image. We used machine learning algorithms running on an artificial neural network for most of these adjustments. Today, our AI algorithms can recognize damage or faults involving insulators, connectors, dampers, poles, cross-arms, and other structures, and highlight the problem areas for in-person maintenance. For instance, it can detect what we call flashed-over insulators--damage due to overheating caused by excessive electrical discharge. It can also spot the fraying of conductors (something also caused by overheated lines), corroded connectors, damage to wooden poles and crossarms, and many more issues. Close up of grey power cords circled in green and labelled \ u201cConductor Good\u201d. A silver piece hanging from it holds two conical pieces on either side, which look burned and are circled in yellow, labelled \u201cDampers Damaged\u201d. Developing algorithms for analyzing power system equipment required determining what exactly damaged components look like from a variety of angles under disparate lighting conditions. Here, the software flags problems with equipment used to reduce vibration caused by winds.Buzz Solutions But one of the most important issues, especially in California, is for our AI to recognize where and when vegetation is growing too close to high-voltage power lines, particularly in combination with faulty components, a dangerous combination in fire country. Today, our system can go through tens of thousands of images and spot issues in a matter of hours and days, compared with months for manual analysis. This is a huge help for utilities trying to maintain the power infrastructure. But AI isn't just good for analyzing images. It can predict the future by looking at patterns in data over time. AI already does that to predict weather conditions, the growth of companies, and the likelihood of onset of diseases, to name just a few examples. We believe that AI will be able to provide similar predictive tools for power utilities, anticipating faults, and flagging areas where these faults could potentially cause wildfires. We are developing a system to do so in cooperation with industry and utility partners. We are using historical data from power line inspections combined with historical weather conditions for the relevant region and feeding it to our machine learning systems. We are asking our machine learning systems to find patterns relating to broken or damaged components, healthy components, and overgrown vegetation around lines, along with the weather conditions related to all of these, and to use the patterns to predict the future health of the power line or electrical components and vegetation growth around them. graphics show analysis of the severity of detections (High 65.2%, Medium 21.7%, Low 6.52%) Buzz Solutions type of detection, particularly Rust (27.9%) and identification of good conductors (23.6%) or porcelain insulators (13.8%). Buzz Solutions' PowerAI software analyzes images of the power infrastructure to spot current problems and predict future ones Right now, our algorithms can predict six months into the future that, for example, there is a likelihood of five insulators getting damaged in a specific area, along with a high likelihood of vegetation overgrowth near the line at that time, that combined create a fire risk. We are now using this predictive fault detection system in pilot programs with several major utilities--one in New York, one in the New England region, and one in Canada. Since we began our pilots in December of 2019, we have analyzed about 3,500 electrical towers. We detected, among some 19,000 healthy electrical components, 5,500 faulty ones that could have led to power outages or sparking. (We do not have data on repairs or replacements made.) Where do we go from here? To move beyond these pilots and deploy predictive AI more widely, we will need a huge amount of data, collected over time and across various geographies. This requires working with multiple power companies, collaborating with their inspection, maintenance, and vegetation management teams. Major power utilities in the United States have the budgets and the resources to collect data at such a massive scale with drone and aviation-based inspection programs. But smaller utilities are also becoming able to collect more data as the cost of drones drops. Making tools like ours broadly useful will require collaboration between the big and the small utilities, as well as the drone and sensor technology providers. Fast forward to October 2025. It's not hard to imagine the western U.S facing another hot, dry, and extremely dangerous fire season, during which a small spark could lead to a giant disaster. People who live in fire country are taking care to avoid any activity that could start a fire. But these days, they are far less worried about the risks from their electric grid, because, months ago, utility workers came through, repairing and replacing faulty insulators, transformers, and other electrical components and trimming back trees, even those that had yet to reach power lines. Some asked the workers why all the activity. "Oh," they were told, "our AI systems suggest that this transformer, right next to this tree, might spark in the fall, and we don't want that to happen." Indeed, we certainly don't. Keep Reading | Show less