̽��ֱ�� of Cambridge - Winton Programme for the Physics of Sustainability

Machine learning algorithm predicts how to get the most out of electric vehicle batteries

sc604 — Tue, 23 Aug 2022 09:01:34 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimise battery degradation and charging times.

̽��ֱ��team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.

If developed commercially, the algorithm could be used to recommend routes that get drivers from point to point in the shortest time without degrading the battery, for example, or recommend the fastest way to charge the battery without causing it to degrade. ̽��ֱ��results are reported in the journal Nature Communications.

̽��ֱ��health of a battery, whether it’s in a smartphone or a car, is far more complex than a single number on a screen. “Battery health, like human health, is a multi-dimensional thing, and it can degrade in lots of different ways,” said first author Penelope Jones, from Cambridge’s Cavendish Laboratory. “Most methods of monitoring battery health assume that a battery is always used in the same way. But that’s not how we use batteries in real life. If I’m streaming a TV show on my phone, it’s going to run down the battery a whole lot faster than if I’m using it for messaging. It’s the same with electric cars – how you drive will affect how the battery degrades.”

“Most of us will replace our phones well before the battery degrades to the point that it’s unusable, but for cars, the batteries need to last for five, ten years or more,” said Dr Alpha Lee, who led the research. “Battery capacity can change drastically over that time, so we wanted to come up with a better way of checking battery health.”

̽��ֱ��researchers developed a non-invasive probe that sends high-dimensional electrical pulses into a battery and measures the response, providing a series of ‘biomarkers’ of battery health. This method is gentle on the battery and doesn’t cause it to degrade any further.

̽��ֱ��electrical signals from the battery were converted into a description of the battery’s state, which was fed into a machine learning algorithm. ̽��ֱ��algorithm was able to predict how the battery would respond in the next charge-discharge cycle, depending on how quickly the battery was charged and how fast the car would be going the next time it was on the road. Tests with 88 commercial batteries showed that the algorithm did not require any information about previous usage of the battery to make an accurate prediction.

̽��ֱ��experiment focused on lithium cobalt oxide (LCO) cells, which are widely used in rechargeable batteries, but the method is generalisable across the different types of battery chemistries used in electric vehicles today.

“This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows us to capture the health of the battery beyond a single number, and because it’s predictive,” said Lee. “It could reduce the time it takes to develop new types of batteries, because we’ll be able to predict how they will degrade under different operating conditions.”

̽��ֱ��researchers say that in addition to manufacturers and drivers, their method could be useful for businesses that operate large fleets of electric vehicles, such as logistics companies. “ ̽��ֱ��framework we’ve developed could help companies optimise how they use their vehicles to improve the overall battery life of the fleet,” said Lee. “There’s so much potential with a framework like this.”

“It’s been such an exciting framework to build because it could solve so many of the challenges in the battery field today,” said Jones. “It’s a great time to be involved in the field of battery research, which is so important in helping address climate change by transitioning away from fossil fuels.”

̽��ֱ��researchers are now working with battery manufacturers to accelerate the development of safer, longer-lasting next-generation batteries. They are also exploring how their framework could be used to develop optimal fast charging protocols to reduce electric vehicle charging times without causing degradation.

̽��ֱ��research was supported by the Winton Programme for the Physics of Sustainability, the Ernest Oppenheimer Fund, ̽��ֱ��Alan Turing Institute and the Royal Society.

Reference:
Penelope K Jones, Ulrich Stimming & Alpha A Lee. ‘Impedance-based forecasting of lithium-ion battery performance amid uneven usage.’ Nature Communications (2022). DOI: 10.1038/s41467-022-32422-w

For more information on energy-related research in Cambridge, please visit Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come.

Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.

This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows us to capture the health of the battery beyond a single number

Alpha Lee

Monty Rakusen via Getty Images

People charging their electric cars at charging station in York

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AI tackles the challenge of materials structure prediction

Anonymous — Wed, 27 Jul 2022 17:46:49 +0000

̽��ֱ��researchers, from Cambridge and Linkoping Universities, have designed a way to predict the structure of materials given its constitutive elements. ̽��ֱ��results are reported in the journal Science Advances.

̽��ֱ��arrangement of atoms in a material determines its properties. ̽��ֱ��ability to predict this arrangement computationally for different combinations of elements, without having to make the material in the lab, would enable researchers to quickly design and improve materials. This paves the way for advances such as better batteries and photovoltaics.

However, there are many ways that atoms can ‘pack’ into a material: some packings are stable, others are not. Determining the stability of a packing is computationally intensive, and calculating every possible arrangement of atoms to find the best one is not practical. This is a significant bottleneck in materials science.

“This materials structure prediction challenge is similar to the protein folding problem in biology,” said Dr Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research. “There are many possible structures that a material can ‘fold’ into. Except the materials science problem is perhaps even more challenging than biology because it considers a much broader set of elements.”

Lee and his colleagues developed a method based on machine learning that successfully tackles this challenge. They developed a new way to describe materials, using the mathematics of symmetry to reduce the infinite ways that atoms can pack into materials into a finite set of possibilities. They then used machine learning to predict the ideal packing of atoms, given the elements and their relative composition in the material.

Their method accurately predicts the structure of materials that hold promise for piezoelectric and energy harvesting applications, with over five times the efficiency of current methods. Their method can also find thousands of new and stable materials that have never been made before, in a way that is computationally efficient.

“ ̽��ֱ��number of materials that are possible is four to five orders of magnitude larger than the total number of materials that we have made since antiquity,” said co-first author Dr Rhys Goodall, also from the Cavendish Laboratory. “Our approach provides an efficient computational approach that can ‘mine’ new stable materials that have never been made before. These hypothetical materials can then be computationally screened for their functional properties.”

̽��ֱ��researchers are now using their machine learning platform to find new functional materials such as dielectric materials. They are also integrating other aspects of experimental constraints into their materials discovery approach.

̽��ֱ��research was supported in part by the Royal Society and the Winton Programme for the Physics of Sustainability.

Reference:
Rhys A Goodall et al. ‘Rapid discovery of stable materials by coordinate-free coarse graining.’ Science Advances (2022). DOI: 10.1126/sciadv.abn4117

Researchers have designed a machine learning method that can predict the structure of new materials with five times the efficiency of the current standard, removing a key roadblock in developing advanced materials for applications such as energy storage and photovoltaics.

Our approach provides an efficient computational approach that can ‘mine’ new stable materials that have never been made before.

Rhys Goodall

MR.Cole_Photographer via Getty Images

Geometric abstract background with connected line and dots

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Winton Symposium tackles the challenge of energy storage and distribution

sc604 — Wed, 11 Oct 2017 07:13:32 +0000

Storage and distribution of energy is seen as the missing link between intermittent renewable energy and reliability of supply, but current technologies have considerable room for improvements in performance. Speakers at the annual symposium, which is free and open to the public, will discuss some of the new technologies in this important area, and how understanding the basic science of these can accelerate their development.

“As intermittent forms of renewable energies continue to contribute to a larger share of our energy mix, there is an urgent need to store and efficiently distribute energy to ensure the lights stay on,” said Dr Nalin Patel, Winton Programme Manager at the ̽��ֱ�� of Cambridge.

̽��ֱ��one-day event is an opportunity for students, researchers and industrialists from a variety of backgrounds to hear a series of talks given by world-leading experts and to join in the debate. Speakers at the event will include Professor Harold Wilson, Programme Director of the UK Atomic Energy Authority; Professor Katsuhiko Hirose, Professional Partner at Toyota Motor Corporation; and Professor David Larbalestier, Director of the Applied Superconductivity Center, National High Magnetic Field Laboratory at Florida State ̽��ֱ��. ̽��ֱ��full programme of speakers is available online.

̽��ֱ��symposium is organised by Professor Sir Richard Friend, Cavendish Professor of Physics and Director of the Winton Programme for the Physics of Sustainability and Dr Nalin Patel the Winton Programme Manager.

There is no registration fee for the symposium and complimentary lunch and drinks reception will be provided, however participants are required to register online. ̽��ֱ��event is open for all to attend.

̽��ֱ��sixth annual Winton Symposium will be held on 9 November at the ̽��ֱ��’s Cavendish Laboratory on the theme of Energy Storage and Distribution.

There is an urgent need to store and efficiently distribute energy to ensure the lights stay on.

Nalin Patel

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes