Novonix Discovers ‘Breakthrough’ Method for Low-cost Synthesis of Lithium-ion Battery Materials

   A novel method of manufacturing lithium-ion batteries could potentially be on course to make a market impact after Novonix (ASX: NVX) announced it had found a breakthrough.

The breakthrough method applies to manufacturing both battery anode and cathode materials using dry particle microgranulation (DPMG).

Courtesy of a partnership with the Canadian Government in tandem with the NSERC Industrial Research Chair program led by Prof Mark Obrovac from Dalhousie University, Novonix funded research with a view of improving battery yield and cost-efficiency.

According to the company, DPMG provides a method for synthesising highly engineered particles through the consolidation of fine materials – that would otherwise be wasted – into much smaller particles measured in microns and suitable for use in lithium-ion batteries.

More broadly, Novonix hopes its research and consequent patent application will advance its PUREgraphite manufacturing process and create a competitive advantage over other battery manufacturers.

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Battery Cycle Life Laptop Battery Comparison

Laptop Battery Cycle Life Comparison – See How Max Capacity holds Over Time?

Li-Ion Battery Cycle Life is a test performed by most Li-Ion cell manufacturers and can be used to describe the expected performance loss over the battery’s service life. One Cycle is defined to be when a battery pack is completely discharged from a fully charged state and then recharged back to its remaining full capacity.

Each time a battery pack is charged and discharged it loses its ability to store as much energy as it did from the prior charge. The typical lifespan of an average Li-ion battery is about 300 charging cycles. Really cheap battery packs are even less. Typically, poor performance is very noticeable after about 300 charges. By 400 charges most laptop batteries will require replacement.

That said, our Max Capacity replacement laptop batteries are designed to exceed 500 cycles. We provide metrics about our laptop batteries so consumers can understand what they are buying. Also, with numbers like these we are proud to show off!

The following graph shows how our Max Capacity laptop batteries maintain their capacity over 500 charging cycles. When compared against other popular replacement packs our laptop batteries really do shine!

Max Capacity Battery Chart - Our performance vs the competition

Max Capacity Battery Comparison Chart - Our performance vs the competition

Note: The graph above illustrates 80% of original capacity after 300 cycles at an operating temperature between 77° F and 104° F (25° C and 40° C). Higher operating temperatures can result in a 70% or more loss of capacity given the same number of cycles.

 

Written by Max Capacity

New machine learning method could supercharge battery development for electric vehicles

New machine learning method could supercharge battery development for electric vehicles

Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology.

For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent. Although the group tested their method on battery charge speed, they said it can be applied to numerous other parts of the battery development pipeline and even to non-energy technologies.

"In battery testing, you have to try a massive number of things, because the performance you get will vary drastically," said Ermon, an assistant professor of computer science. "With AI, we're able to quickly identify the most promising approaches and cut out a lot of unnecessary experiments."

The study, published by Nature on Feb. 19, was part of a larger collaboration among scientists from Stanford, MIT and the Toyota Research Institute that bridges foundational academic research and real-world industry applications. The goal: finding the best method for charging an EV battery in 10 minutes that maximizes the battery's overall lifetime. The researchers wrote a program that, based on only a few charging cycles, predicted how batteries would respond to different charging approaches. The software also decided in real time what charging approaches to focus on or ignore. By reducing both the length and number of trials, the researchers cut the testing process from almost two years to 16 days.

"We figured out how to greatly accelerate the testing process for extreme fast charging," said Peter Attia, who co-led the study while he was a graduate student. "What's really exciting, though, is the method. We can apply this approach to many other problems that, right now, are holding back battery development for months or years."

A smarter approach to battery testing

Designing ultra-fast-charging batteries is a major challenge, mainly because it is difficult to make them last. The intensity of the faster charge puts greater strain on the battery, which often causes it to fail early. To prevent this damage to the battery pack, a component that accounts for a large chunk of an electric car's total cost, battery engineers must test an exhaustive series of charging methods to find the ones that work best.

The new research sought to optimize this process. At the outset, the team saw that fast-charging optimization amounted to many trial-and-error tests -- something that is inefficient for humans, but the perfect problem for a machine.

"Machine learning is trial-and-error, but in a smarter way," said Aditya Grover, a graduate student in computer science who co-led the study. "Computers are far better than us at figuring out when to explore -- try new and different approaches -- and when to exploit, or zero in, on the most promising ones."

The team used this power to their advantage in two key ways. First, they used it to reduce the time per cycling experiment. In a previous study, the researchers found that instead of charging and recharging every battery until it failed -- the usual way of testing a battery's lifetime -they could predict how long a battery would last after only its first 100 charging cycles. This is because the machine learning system, after being trained on a few batteries cycled to failure, could find patterns in the early data that presaged how long a battery would last.

Second, machine learning reduced the number of methods they had to test. Instead of testing every possible charging method equally, or relying on intuition, the computer learned from its experiences to quickly find the best protocols to test.

By testing fewer methods for fewer cycles, the study's authors quickly found an optimal ultra-fast-charging protocol for their battery. In addition to dramatically speeding up the testing process, the computer's solution was also better -- and much more unusual -- than what a battery scientist would likely have devised, said Ermon.

"It gave us this surprisingly simple charging protocol -- something we didn't expect," Ermon said. Instead of charging at the highest current at the beginning of the charge, the algorithm's solution uses the highest current in the middle of the charge. "That's the difference between a human and a machine: The machine is not biased by human intuition, which is powerful but sometimes misleading."

Wider applications

The researchers said their approach could accelerate nearly every piece of the battery development pipeline: from designing the chemistry of a battery to determining its size and shape, to finding better systems for manufacturing and storage. This would have broad implications not only for electric vehicles but for other types of energy storage, a key requirement for making the switch to wind and solar power on a global scale.

"This is a new way of doing battery development," said Patrick Herring, co-author of the study and a scientist at the Toyota Research Institute. "Having data that you can share among a large number of people in academia and industry, and that is automatically analyzed, enables much faster innovation."

The study's machine learning and data collection system will be made available for future battery scientists to freely use, Herring added. By using this system to optimize other parts of the process with machine learning, battery development -- and the arrival of newer, better technologies -- could accelerate by an order of magnitude or more, he said.

The potential of the study's method extends even beyond the world of batteries, Ermon said. Other big data testing problems, from drug development to optimizing the performance of X-rays and lasers, could also be revolutionized by the use of machine learning optimization. And ultimately, he said, it could even help to optimize one of the most fundamental processes of all.

"The bigger hope is to help the process of scientific discovery itself," Ermon said. "We're asking: Can we design these methods to come up with hypotheses automatically? Can they help us extract knowledge that humans could not? As we get better and better algorithms, we hope the whole scientific discovery process may drastically speed up."

Additional Stanford co-authors include Norman Jin, Yang-Hung Liao, Michael H. Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Stephen Harris and Todor M. Markov. Additional co-authors are from MIT and the Toyota Research Institute.

This work was supported by Stanford, the Toyota Research Institute, the National Science Foundation, the U.S. Department of Energy and Microsoft.

 

Date:February 19, 2020Source:Stanford UniversitySummary:A new machine learning method has slashed battery testing times -- a key barrier to longer-lasting, faster-charging batteries for electric vehicles -- by nearly fifteenfold.

PG&E's massive battery storage project at Moss Landing is approved.

PG&E's massive battery storage project at Moss Landing is approved.

 

The Monterey County Planning Commission unanimously approved PG&E’s proposed battery storage system, one of the largest of its kind in the world, on Feb. 26 adding to another, even larger system approved last year. The projects will be located at the Moss Landing Power Plant and on an adjacent electrical substation. 

Battery technology is important for California’s clean energy transition because of the possibility of storing power generated during the day by solar panels and discharging it when the sun goes down and people flick on the lights at home. 

 
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Cleantech – Next-Generation Solid-State Batteries

Cleantech – Next-Generation Solid-State Batteries

Solid Power CEO, Doug Campbell, has had quite the journey as an entrepreneur. He started early adulthood as a formerly troubled teen without a clue as to what he was going to do with his life. After a stint as a professional mountain biker, he decided to get a college degree and has since founded two CO-based startups – Solid Power (Solid-state batteries for EVs) and Roccor (next-generation deployable spacecrafts reducing the cost of access to space).

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