Researchers have developed a machine learning method that can predict battery health with ten times higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics. The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery's health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported here.

Metal–air batteries, consisting of a metal anode and an air cathode, have been attracted significant interest by the research community as energy storage devices, because of their high energy density (in particular, compared to lithium ion batteries -LIBs-). A wide diversity of active metals can be used as anode material such as Li, Ca, Mg, Al, Fe, and Zn. However, they have so far found their use only in very particular markets requiring high energy density such as hearing aids. Indeed, despite very significant experimental research efforts, recharging them electrochemically constitute a significant challenge, that if unlocked, will pave the way to a wider diversity of ZAB applications such as Electric Vehicles. Reversing this process to recharge electrochemically a ZAB would imply a heterogeneous deposition of Zn in the anode and the formation of dendrites that can short-circuit the cell, similarly to what can happen in lithium metal batteries.

Researchers at two U.K. universities have developed a way to predict battery health with 10 times greater accuracy than the current industry standard. A machine learning method developed by researchers at the University of Cambridge and Newcastle University in the U.K. can predict battery health with 10 times greater accuracy than the current industry standard. The new method could help develop safer, more reliable batteries for electric vehicles and consumer electronics. The researchers trained the model by performing more than 20,000 experimental measurements. Said Cambridge's Alpha Lee, "By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance."

When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system. As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks. The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet PhD '19, Sahasrajit Ramesh, and graduate student Chenru Duan.

The battery sector is turning to artificial intelligence for clues on how to improve recharging rates without increasing the degradation of lithium-ion batteries. Last month, a team from Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute published findings from battery testing aimed at cutting electric-vehicle charging times down to 10 minutes. The research, published in Nature, revealed how artificial intelligence could speed up the testing process required for novel charging techniques. The researchers wrote a program that predicted how batteries would respond to different charging approaches and was able to cut the testing process from almost two years to 16 days, Stanford reported. The technique was used to evaluate 224 possible high-cycle-life charging processes in just over two weeks, the researchers said.

The goal of this challenge is to test the potential of Reinforcement Learning (RL) to control electrical power transmission, in the most cost-effective manner, while keeping people and equipment safe from harm. Solving this challenge may have very positive impacts on society, as governments move to decarbonize the electricity sector and to electrify other sectors, to help reach IPCC climate goals. Existing software, computational methods and optimal powerflow solvers are not adequate for real-time network operations on short temporal horizons in a reasonable computational time. With recent changes in electricity generation and consumption patterns, system operation is moving to become more of a stochastic rather than a deterministic control problem. In order to overcome these complexities, new computational methods are required. The intention of this challenge is to explore RL as a solution method for electricity network control. There may be under-utilized, cost-effective flexibility in the power network that RL techniques can identify and capitalize on, that human operators and traditional solution techniques are unaware of or unaccustomed to. An RL agent that can act in conjunction, or in parallel with human network operators, will optimize grid security and reliability, allowing more renewable resources to be connected while minimizing the cost and maintaining supply to customers, and preventing damage to electrical equipment. Another aim of the project is to broaden the audience for the problem of electricity network control and to foster collaboration between experts in both the power systems community and the wider RL/ML community.

We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and $5^{th}$ percentile user rates throughout a range of network configurations.

TOKYO/SAN, FRANCISCO – Tesla Inc.'s stock surged 20 percent on Monday in its largest one-day gain since 2013, fueled by a quarterly profit at Panasonic's battery business with the U.S. carmaker and an investor report predicting its shares would rise more than ten-fold by 2024. Shares of Tesla have rallied by over 30 percent since the car maker run by Chief Executive Elon Musk posted its second consecutive quarterly profit last Wednesday, which was viewed as a milestone for the company competing against established heavyweights including General Motors Co. and BMW. The stock is up over 300 percent since early June, helped by Tesla's better-than-expected financial results and ramped up production at its new car factory in Shanghai. Monday's rise came after Panasonic Corp. reported the first quarterly profit in its U.S. battery business with Tesla, which followed years of production troubles and delays. "We are catching up as Tesla is quickly expanding production," Panasonic Chief Financial Officer Hirokazu Umeda told an earnings briefing, referring to battery cell production. "Higher production volume is helping to push down materials costs and erase losses."

GreenWaves Technologies, a fabless semiconductor vendor focused on ultra-low power edge-based AI processing, recently announced a new member of its GAP IoT application processor family, the GAP9. This latest member combines architectural enhancements using Global Foundries 22-nm FDX process to deliver a peak cluster memory bandwidth of 41.6 Gbytes/s and up to 50 GOPS combined compute power at an overall power consumption of 50 mW. GAP9 lets OEMs embed machine learning and signal processing capabilities into battery-powered or energy-harvesting devices.

People like to call millennials "lazy" when in fact we're just a bunch of tech-savvy innovators who increasingly show that you don't have to do everything the same way your parents or grandparents did. Case in point: You don't have to have cable. You don't actually have to call people on the phone, ever. And splitting monthly bills with strangers can actually be normal. Adults born in the 1980s and early 1990s have practically become experts at finding alternatives to everyday tasks so they can get more done with less physical exertion.