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Security software for autonomous vehicles

Before autonomous vehicles participate in road traffic, they must demonstrate conclusively that they do not pose a danger to others. New software developed at the Technical University of Munich (TUM) prevents accidents by predicting different variants of a traffic situation every millisecond.

A car approaches an intersection. Another vehicle jets out of the cross street, but it is not yet clear whether it will turn right or left. At the same time, a pedestrian steps into the lane directly in front of the car, and there is a cyclist on the other side of the street. People with road traffic experience will in general assess the movements of other traffic participants correctly.

“These kinds of situations present an enormous challenge for autonomous vehicles controlled by computer programs,” explains Matthias Althoff, Professor of Cyber-Physical Systems at TUM. “But autonomous driving will only gain acceptance of the general public if you can ensure that the vehicles will not endanger other road users — no matter how confusing the traffic situation.”

Algorithms that peer into the future

The ultimate goal when developing software for autonomous vehicles is to ensure that they will not cause accidents. Althoff, who is a member of the Munich School of Robotics and Machine Intelligence at TUM, and his team have now developed a software module that permanently analyzes and predicts events while driving. Vehicle sensor data are recorded and evaluated every millisecond. The software can calculate all possible movements for every traffic participant — provided they adhere to the road traffic regulations — allowing the system to look three to six seconds into the future.

Based on these future scenarios, the system determines a variety of movement options for the vehicle. At the same time, the program calculates potential emergency maneuvers in which the vehicle can be moved out of harm’s way by accelerating or braking without endangering others. The autonomous vehicle may only follow routes that are free of foreseeable collisions and for which an emergency maneuver option has been identified.

Streamlined models for swift calculations

This kind of detailed traffic situation forecasting was previously considered too time-consuming and thus impractical. But now, the Munich research team has shown not only the theoretical viability of real-time data analysis with simultaneous simulation of future traffic events: They have also demonstrated that it delivers reliable results.

The quick calculations are made possible by simplified dynamic models. So-called reachability analysis is used to calculate potential future positions a car or a pedestrian might assume. When all characteristics of the road users are taken into account, the calculations become prohibitively time-consuming. That is why Althoff and his team work with simplified models. These are superior to the real ones in terms of their range of motion — yet, mathematically easier to handle. This enhanced freedom of movement allows the models to depict a larger number of possible positions but includes the subset of positions expected for actual road users.

Real traffic data for a virtual test environment

For their evaluation, the computer scientists created a virtual model based on real data they had collected during test drives with an autonomous vehicle in Munich. This allowed them to craft a test environment that closely reflects everyday traffic scenarios. “Using the simulations, we were able to establish that the safety module does not lead to any loss of performance in terms of driving behavior, the predictive calculations are correct, accidents are prevented, and in emergency situations the vehicle is demonstrably brought to a safe stop,” Althoff sums up.

The computer scientist emphasizes that the new security software could simplify the development of autonomous vehicles because it can be combined with all standard motion control programs.

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Materials provided by Technical University of Munich (TUM). Note: Content may be edited for style and length.

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Predicting the slow death of lithium-ion batteries

Batteries fade as they age, slowly losing power and storage capacity.

As in people, aging plays out differently from one battery to another, and it’s next to impossible to measure or model all of the interacting mechanisms that contribute to decline. As a result, most of the systems used to manage charge levels wisely and to estimate driving range in electric cars are nearly blind to changes in the battery’s internal workings.

Instead, they operate more like a doctor prescribing treatment without knowing the state of a patient’s heart and lungs, and the particular ways that environment, lifestyle, stress and luck have ravaged or spared them. If you’ve kept a laptop or phone for enough years, you may have seen where this leads firsthand: Estimates of remaining battery life tend to diverge further from reality over time.

Now, a model developed by scientists at Stanford University offers a way to predict the true condition of a rechargeable battery in real-time. The new algorithm combines sensor data with computer modeling of the physical processes that degrade lithium-ion battery cells to predict the battery’s remaining storage capacity and charge level.

“We have exploited electrochemical parameters that have never been used before for estimation purposes,” said Simona Onori, assistant professor of energy resources engineering in Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth). The research appears Sept. 11 in the journal IEEE Transactions on Control Systems Technology.

The new approach could help pave the way for smaller battery packs and greater driving range in electric vehicles. Automakers today build in spare capacity in anticipation of some unknown amount of fading, which adds extra cost and materials, including some that are scarce or toxic. Better estimates of a battery’s actual capacity will enable a smaller buffer.

“With our model, it’s still important to be careful about how we are using the battery system,” Onori explained. “But if you have more certainty around how much energy your battery can hold throughout its entire lifecycle, then you can use more of that capacity. Our system reveals where the edges are, so batteries can be operated with more precision.”

The accuracy of the predictions in this model — within 2 percent of actual battery life as gathered from experiments, according to the paper — could also make it easier and cheaper to put old electric car batteries to work storing energy for the power grid. “As it is now, batteries retired from electric cars will vary widely in their quality and performance,” Onori said. “There has been no reliable and efficient method to standardize, test or certify them in a way that makes them competitive with new batteries custom-built for stationary storage.”

Dropping old assumptions

Every battery has two electrodes — the cathode and the anode — sandwiching an electrolyte, usually a liquid. In a rechargeable lithium-ion battery, lithium ions shuttle back and forth between the electrodes during charging and discharging. An electric car may run on hundreds or thousands of these small battery cells, assembled into a big battery pack that typically accounts for about 30 percent of the total vehicle cost.

Traditional battery management systems typically rely on models that assume the amount of lithium in each electrode never changes, said lead study author Anirudh Allam, a PhD student in energy resources engineering. “In reality, however, lithium is lost to side reactions as the battery degrades,” he said, “so these assumptions result in inaccurate models.”

Onori and Allam designed their system with continuously updated estimates of lithium concentrations and a dedicated algorithm for each electrode, which adjusts based on sensor measurements as the system operates. They validated their algorithm in realistic scenarios using standard industry hardware.

On the road

The model relies on data from sensors found in the battery management systems running in electric cars on the road today. “Our algorithm can be integrated into current technologies to make them operate in a smarter fashion,” Onori said. In theory, many cars already on the road could have the algorithm installed on their electronic control units, she said, but the expense of that kind of upgrade makes it more likely that automakers would consider the algorithm for vehicles not yet in production.

The team focused their experiments on a type of lithium-ion battery commonly used in electric vehicles (lithium nickel manganese cobalt oxide) to estimate key internal variables such as lithium concentration and cell capacity. But the framework is general enough that it should be applicable to other kinds of lithium-ion batteries and to account for other mechanisms of battery degradation.

“We showed that our algorithm is not just a nice theoretical work that can run on a computer,” she said. “Rather, it is a practical, implementable algorithm which, if adopted and used in cars tomorrow, can result in the ability to have longer-lasting batteries, more reliable vehicles and smaller battery packs.”

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Scientists probe the chemistry of a single battery electrode particle both inside and out

The particles that make up lithium-ion battery electrodes are microscopic but mighty: They determine how much charge the battery can store, how fast it charges and discharges and how it holds up over time — all crucial for high performance in an electric vehicle or electronic device.

Cracks and chemical reactions on a particle’s surface can degrade performance, and the whole particle’s ability to absorb and release lithium ions also changes over time. Scientists have studied both, but until now they had never looked at both the surface and the interior of an individual particle to see how what happens in one affects the other.

In a new study, a research team led by Yijin Liu at the Department of Energy’s SLAC National Accelerator Laboratory did that. They stuck a single battery cathode particle, about the size of a red blood cell, on a needle tip and probed its surface and interior in 3D with two X-ray instruments. They discovered that cracking and chemical changes on the particle’s surface varied a lot from place to place and corresponded with areas of microscopic cracking deep inside the particle that sapped its capacity for storing energy.

“Our results show that the surface and the interior of a particle talk to each other, basically,” said SLAC lead scientist Yijin Liu, who led the study at the lab’s Stanford Synchrotron Radiation Lightsource (SSRL). “Understanding this chemical conversation will help us engineer the whole particle so the battery can cycle faster, for instance.”

The scientists describe their findings in Nature Communications today.

Damage both inside and out

A lithium-ion battery stores and releases energy by moving lithium ions through an electrolyte back and forth between two electrodes, the anode and the cathode. When you charge the battery, lithium ions rush into the anode for storage. When you use the battery, the ions leave the anode and flow into the cathode, where they generate a flow of electrical current.

Each electrode consists of many microscopic particles, and each particle contains even smaller grains. Their structure and chemistry are key to the battery’s performance. As the battery charges and discharges, lithium ions seep in and out of the spaces between the particles’ atoms, causing them to swell and shrink. Over time this can crack and break particles, reducing their ability to absorb and release ions. Particles also react with the surrounding electrolyte to form a surface layer that gets in the way of ions entering and leaving. As cracks develop, the electrolyte penetrates deeper to damage the interior.

This study focused on particles made from a nickel-rich layered oxide, which can theoretically store more charge than today’s battery materials. It also contains less cobalt, making it cheaper and less ethically problematic, since some cobalt mining involves inhumane conditions, Liu said.

There’s just one problem: The particles’ capacity for storing charge quickly fades during multiple rounds of high-voltage charging – the type used to fast-charge electric vehicles.

“You have millions of particles in an electrode. Each one is like a rice ball with many grains,” Liu said. “They’re the building blocks of the battery, and each one is unique, just like every person has different characteristics.”

Taming a next-gen material

Liu said scientists have been working on two basic approaches for minimizing damage and increasing the performance of particles: Putting a protective coating on the surface and packing the grains together in different ways to change the internal structure. “Either approach could be effective,” Liu said, “but combining them would be even more effective, and that’s why we have to address the bigger picture.”

Shaofeng Li, a visiting graduate student at SSRL who will be joining SLAC as a postdoctoral researcher, led X-ray experiments that examined a single needle-mounted cathode particle from a charged battery with two instruments — one scanning the surface, the other probing the interior. Based on the results, theorists led by Kejie Zhao, an associate professor at Purdue University, developed a computer model showing how charging would have damaged the particle over a period of 12 minutes and how that damage pattern reflects interactions between the surface and interior.

“The picture we are getting is that there are variations everywhere in the particle,” Liu said. “For instance, certain areas on the surface degrade more than others, and this affects how the interior responds, which in turn makes the surface degrade in a different manner.”

Now, he said, the team plans to apply this technique to other electrode materials they have studied in the past, with particular attention to how charging speed affects damage patterns. “You want to be able to charge your electric car in 10 minutes rather than several hours,” he said, “so this is an important direction for follow-up studies.”

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Governor Wolf Announces Restaurants May Increase Indoor Occupancy to 50 Percent Starting September 21 – PA Department of Community & Economic Development

Restaurants to self-certify that they are in compliance with appropriate orders

Harrisburg, PA — Governor Tom Wolf today announced that restaurants may increase indoor occupancy to 50 percent starting September 21. To ensure that these businesses operate safely as Pennsylvania continues to mitigate the spread of COVID-19, and to instill customers and employees with confidence knowing that they can dine safely, restaurants will commit to strictly complying to all public health safety guidelines and orders through a self-certification process.

“While our aggressive and appropriate mitigation efforts have kept case counts low, we must continue to take important steps to protect public health and safety as we head into the fall. At the same time, we must also support the retail food services industry that has struggled throughout this pandemic,” Gov. Wolf said. “The self-certification ensures that restaurants can expand indoor operations and commit to all appropriate orders so that employees and customers alike can be confident they are properly protected.”

Restaurants that self-certify will appear in the Open & Certified Pennsylvania searchable online database of certified restaurants across the commonwealth. Consumers will be able to access this database and find certified businesses in their area, ensuring that consumers can make more informed choices about the food establishments they are looking to patronize.

The self-certification documents and information about the Open & Certified Pennsylvania program can be found online starting September 21 and will contain the following:

  • A list of requirements contained in the current restaurant industry guidance and enforcement efforts;
  • A statement that the owner has reviewed and agrees to follow these requirements;
  • The business’ maximum indoor occupancy number based on the fire code; and
  • A statement that the owner understands that the certification is subject to penalties for unsworn falsification to authorities.

Any restaurant that wishes to increase to 50 percent indoor capacity on September 21 must complete the online self-certification process by October 5. Business owners should keep a copy of the self-certification confirmation they will receive by e-mail. Social distancing, masking and other mitigation measures must be employed to protect workers and patrons. Further, starting September 21 restaurants that have alcohol sales will close alcohol sales at 10:00 PM.

Additionally, restaurants that self-certify will be mailed Open & Certified Pennsylvania branded materials, such as window clings and other signage designating their certification, which they can display for customers and employees.

The self-certification will be used as part of ongoing enforcement efforts conducted by Department of Agriculture and Pennsylvania State Police Bureau of Liquor Control Enforcement, and will be shared with the departments of State, Labor & Industry and Health, and other enforcement agencies. Restaurants operating at 50 percent capacity will have their self-certification status checked as part of ongoing enforcement by these agencies starting on October 5, and will focus on educating businesses. The commonwealth will continue its measured approach to easing restrictions, keeping the rest of the targeted mitigation tactics specific to the food retail industry in place as restaurants increase capacity to 50 percent.

Further, a restaurant’s listing in the Open & Certified Pennsylvania restaurant database shows it cares about its customers, employees, community and the economic future of the state.

The self-certification process is modelled after a similar mitigation effort in Connecticut, and the alcohol sales limitation is modelled after a similar mitigation effort in Ohio.

Boosting consumer confidence is critical for restaurants, as according to the most recent Longwoods International tracking study of American travelers