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Artificial intelligence helps build better lithium batteries

#artificialintelligence

How can artificial intelligence bring us closer to a more efficient, more easily recycled and better batteries? Recharge Industries has just announced it will build a $300 million lithium ion battery "gigafactory" in Geelong, Victoria, targeting 2 GWh of production a year in 2024 and 6 GWh by 2026. Lithium ion batteries are in growing demand worldwide with the expected skyrocketing introduction of electric vehicles. But beyond this news, Recharge Industries will also partner with Deakin University's Applied Artificial Intelligence Institute (A2I2) in Geelong to use artificial intelligence to build a better battery. The idea of using AI to improve batteries is not new, but A2I2 has created an operating system specifically designed for the lithium ion battery project, to speed up the design process.


New Electronics - AI-powered cloud-connected EV battery management system

#artificialintelligence

NXP is using Electra Vehicles' EVE-Ai 360 Adaptive Controls technology to use digital twin models in the cloud to predict and control the physical BMS in real time, to improve battery performance, battery state of health of up to 12% and enable multiple new applications, such as EV fleet management. Batteries remain the costliest element in an electric vehicle (EV), and AI-powered digital twin cloud services have the potential to improve estimations of the battery's state of health (SOH) and state of charge (SOC) to deliver improved efficiency, lifetime and cost. Battery digital twins adapt to ongoing changes in battery health due to operating conditions and provide updated figures back to the BMS for continuously improving control decisions. Carmakers can use the technology to provide driver insights, such as range and speed recommendations. In addition, adaptive battery control can improve the battery's performance and safely extend its lifespan, reducing warranty costs for the carmaker.


The top 100 new technology innovations of 2022

#artificialintelligence

On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...


MEMOGRAM – Time(text)capsule camera

#artificialintelligence

Created by Jamy Herrmann at ECAL, MEMOGRAM is a (non)camera that prints our images in the form of a written description, inviting users to (re)discover those moments in images. Today, for many, the memories that remain are only those of images taken with digital cameras. This project uses many different techniques since it is both tangible and digital. Both versions are made in 3D printing and then wrapped with a paper explaining the steps of use. The electronics are comprised of a thermal printer (and a paper roll) connected to a custom PCB equipped with an Arduino nano and a bluetooth UART module.


Machine learning finds fluoride battery materials that could rival lithium

#artificialintelligence

Machine learning has been used to quickly discover some of the most promising materials for fluoride-ion batteries. The work could accelerate development of these batteries, which are tipped by some to rival, or even replace, lithium-based ones. In theory, fluoride-ion systems are ideal for batteries in everything from electric vehicles to consumer electronics. That's because fluoride ions are lightweight, small and highly stable. Fluoride is also cheaper than lithium and cobalt that are required for lithium-ion batteries.


Digital Twins on AWS: Driving Value with L4 Living Digital Twins

#artificialintelligence

In working with customers, we often hear of a desired Digital Twin use case to drive actionable insights through what-if scenario analysis. These use cases typically include operations efficiency management, fleet management, failure predictions, and maintenance planning, to name a few. To help customers navigate this space, we developed a concise definition and four-level Digital Twin leveling index consistent with our customers' applications. In a prior blog, we described the four-level index (shown in the figure below) to help customers understand their use cases and the technologies required to achieve their desired business value. In this blog, we will illustrate how the L4 Living Digital Twins can be used to model the behavior of a physical system whose inherent behavior evolves over time.


Startup Funding: September 2022

#artificialintelligence

The onshoring and buildout of dozens of fabs, many costing tens of billions of dollars, is beginning to spill over into other areas that are critical for chip manufacturing. Materials, in particular, which often gets little attention outside of chip manufacturing, witnessed a big spike in September 2022. In fact, seven materials companies covered in this report made up more than a third of the month's total reported investments, with three of the companies garnering more than $200 million. Other investment targets were sputtering equipment and evaporation materials for deposition, high-purity polycrystalline silicon, fluorine-containing electronic gases, and silicon carbide. In the AI hardware arena, numerous startups are focusing on in-memory and near-memory compute, reducing the volume of data that needs to be moved back and forth between memory and processing elements. Novel architectures also are appearing, such as one that uses sparse mathematics.


The Download: China's non-coup, and building better batteries

MIT Technology Review

If you're on Twitter and follow news about China, you likely have heard a pretty wild rumor recently: that President Xi Jinping was under house arrest and that there was about to be a major power grab in the country. First of all, let's be very clear: this report is false and should not be taken seriously. No credible sources on China have bought it. But it's interesting to dissect how a ridiculous rumor could be elevated and spread so widely that it made it to Twitter's deeply flawed trending list over the weekend, thanks to influencer translation and amplification from accounts based in India. This story is from China Report, MIT Technology Review's new newsletter giving you the inside scoop on what's happening in China.


How robots and AI are helping develop better batteries

MIT Technology Review

Historically, researchers in materials discovery have devised and tested options through some mix of hunches, informed speculation, and trial by error. But it's a difficult and time-consuming process simply given the vast array of possible substances and combinations, which can send researchers down numerous false paths. In the case of electrolyte ingredients, "you can mix and match them in billions of ways," says Venkat Viswanathan, an associate professor at Carnegie Mellon, a co-author of the Nature Communications paper, and a cofounder and chief scientist at Aionics. He collaborated with Jay Whitacre, director of the university's Wilton E. Scott Institute for Energy Innovation and the co-principal investigator on the project, along with other Carnegie researchers to explore how robotics and machine learning could help. The promise of a system like Clio and Dragonfly is that it can rapidly work through a wider array of possibilities than human researchers can, and apply what it learns in a systematic way.


Researchers train AI to predict EV battery degradation

#artificialintelligence

Lithium-ion batteries have become a key component in the rise of electric mobility, but forecasting their health and lifespans is limiting the technology. While they've proven successful, the capacity of lithium-ion batteries degrades over time, and not just because of the ageing process that occurs during charging and discharging -- known as "cycling ageing." Lithium-ion battery cells also suffer degradation from so-called "calendar ageing," which occurs during storage, or simply when the battery is not in use. It's determined by three main factors: the rest state of charge (SOC), the rest temperature, and the duration of the rest time of a battery. Given that an electric vehicle will spend most of its life parked, predicting the cells' capacity degradation from calendar ageing is crucial; it can prolong battery life and pave the way for mechanisms that could even circumvent the phenomenon.