The Singapore government is setting aside SG$49 million ($36.05 million) to drive research and development (R&D) efforts in low-carbon energy technologies such as hydrogen and carbon capture, utilisation and storage (CCUS). It also announces an initiative to pilot a lithium-ion battery energy storage on a "floating" lab, utilising seawater to cool the battery cells. Spanning five years, the SG$49 million R&D investment aimed to push the "technical and economic viability" of technologies that could help reduce the country's carbon emissions. In particular, it hoped to do so in emission-intensive areas such as the power and industrial sectors, according to a joint statement Monday released by give government agencies involved in the funding efforts: the Agency for Science, Technology and Research (A*Star); Economic Development Board (EDB); Energy Market Authority (EMA); National Climate Change Secretariat (NCCS); and National Research Foundation (NRF). Country's government is setting aside more than SG$500 million ($352.49
Boston Dynamics announced that it has developed a robot arm for its "Spot" robot and also a charging station. Both will be available for purchase this spring. The robot Spot made quite a splash on the internet last year, thanks to its YouTube videos. The four-legged yellow-bodied robot was shown marching its way autonomously and untethered through a wide variety of terrain in ways reminiscent of a dog; hence its name. The robot dog is available for sale.
Tesla CEO Elon Musk announced on Twitter last night that the electric car company's Full Self-Driving beta update is officially being rolled out. "Will be extremely slow and cautious, as it should," Musk added an uncharacteristically serious tone. The update is, according to Musk, a revolutionary rewrite of his car company's controversial self-driving features suite, called Full Self-Driving or FSD. Despite of its name, the $8,000 option hasn't allowed drivers to completely take their hands off the steering wheel -- at least yet. In August, Musk promised that the update will be a "quantum leap, because it's a fundamental architectural rewrite, not an incremental tweak."
General Motors (GM) has finally taken the wraps off its 2022 GMC Hummer EV as it looks to take on Tesla's (TSLA) Cybertruck. While the two trucks will rival each other in the electric truck market, they have distinct differences that may sway consumers one way or the other. GM is selling the first edition of the GMC Hummer EV for $112,595 in contrast to Tesla's Cybertruck, which has a price range of $39,900 to $69,900, depending on the motor configuration. The Cybertruck's self-driving system is another $8,000 more while GM's includes Super Cruise – a driver assist feature – on the Hummer EV. GM said it will begin production of the GMC Hummer EV in late 2021 at its Detroit-Hamtramck Assembly Center in Michigan.
For the lucky few selected to experience "Full Self-Driving" (or FSD) on their Tesla vehicle, Tuesday night is the night. Tesla CEO Elon Musk tweeted Tuesday afternoon that the autonomous mode was "happening tonight" after promising the feature would make it onto cars last week. FSD has been a long time coming. It's been available as a future-ready option on the electric cars for a while, even if you couldn't actually use it. Musk warned that the car's autonomous abilities will be "extremely slow & cautious."
With one stroke of the pen Governor Newsom of California has insured a robust outlook for the future of electric vehicles in California. Finally, electric vehicle manufacturers are in vogue. The executive order requires that starting in 2035 all vehicles sold in the state of California can no longer have an internal combustion engine. There are many electric vehicle companies in the market. Tesla TSLA 0.6%, Nio and Rivian appear to be leading the way with large contracts and/or significant followings in the electric vehicle world.
Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.
New battery materials are constantly being invented, but there are still challenges in producing them at a large scale and at high quality. Through the power of artificial intelligence (AI) and advanced simulation, scientists can dramatically accelerate translating these materials from benchtop to large-scale manufacturing and in the process provide a way to generate higher-performance materials at scale. Argonne researchers are currently using AI to optimize nanomaterials produced from flame-spray pyrolysis (FSP) in a minimum number of trials. Argonne scientists are simultaneously building a comprehensive simulation of FSP to reveal the physics and inform the AI model. An advanced suite of diagnostics available at the FSP facility will provide validation data for the simulations.
Vanadium flow batteries (VFBs) are promising for stationary large-scale energy storage due to their high safety, long cycle life, and high efficiency. The cost of a VFB system mainly depends on the VFB stack, electrolyte, and control system. Developing a VFB stack from lab to industrial scale can take years of experiments due to complex factors, from key materials to battery architecture. Novel methods to accurately predict the performance and cost of a VFB stack and further system are needed in order to accelerate the commercialization of VFBs. Recently, a research team led by Prof. Li Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences proposed a machine learning-based strategy to predict and optimize the performance and cost of VFBs.
Peaxy CEO and President Manuel Terranova joins us to discuss some of the biggest challenges facing the battery industry, and how smart software like Peaxy Lifecycle Intelligence (PLI) for Batteries can solve them. Peaxy's Lifecycle Intelligence offers predictive battery analytics, powered by machine learning. What do you see as the top data challenges in the battery industry, and how can they be solved? Batteries are unique and fickle industrial assets, and yet many companies use fleet-level or system level models to manage them. While that can be helpful, I don't believe such models are good at predicting and optimising industrial equipment, including batteries. Simply put, if you're unable to resolve data down to the individual battery -- a unique serial number -- chances are you won't be able to monetise your analytics.