Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.

Nissan Motor Co. is betting that its experience pioneering lithium-ion batteries for electric vehicles over a decade ago will give it an upper hand in producing a new battery type that, despite being new and still relatively unproven, is considered by some as key to unlocking the future potential of EVs. Nissan is producing prototype solid-state battery cells -- which replace the electrical current-conducting liquid found in conventional batteries with a solid substance -- at a facility resembling a pop-up lab inside its research grounds near its Yokohama headquarters. The Japanese automaker plans to bring the new type of batteries to market by fiscal year 2028, readying a pilot plant for them ahead of that around 2024. If they can be manufactured, solid-state batteries would unlock cheaper, safer and faster-charging EVs, according to automotive executives and battery experts. Using different material combinations, Nissan predicts it will eventually be able to produce a solid-state battery pack that costs $65 (¥8,063) per kilowatt-hour -- a level at which analysts say EVs could reach price parity with gasoline-engine cars.

Computer vision has great potential to improve our everyday lives – and there are many applications and uses for it. All of these applications use intelligent video analytics, driven by AI and Machine Learning (ML), to watch video, use intelligence to make decisions, and then take action. However, like many AI-driven applications, computer vision needs bursts of computing power, memory, and energy to do its complex analysis and make decisions. While this is fine in a data center with a lot of computer power, it can prevent the move of AI to the edge. Specifically, small devices that are located far from corporate data centers and operate on small batteries need a new breed of AI that is smaller, faster and "lighter" than traditional approaches.

Today's rechargeable batteries are a wonder, but far from perfect. Eventually, they all wear out, begetting expensive replacements and recycling. "But what if batteries were indestructible?" asks William Chueh, an associate professor of materials science and engineering at Stanford University and senior author of a new paper detailing a first-of-its-kind analytical approach to building better batteries that could help speed that day. The study appears in the journal Nature Materials. Chueh, lead author Haitao "Dean" Deng, PhD '21, and collaborators at Lawrence Berkeley National Laboratory, MIT and other research institutions used artificial intelligence to analyze new kinds of atomic-scale microscopic images to understand exactly why batteries wear out.

Using artificial intelligence to analyse vast amounts of data in atomic-scale images, researchers answered long-standing questions about an emerging type of rechargeable battery posing competition to lithium-ion chemistry.

Battery discharge capacity forecasting is critically essential for the applications of lithium-ion batteries. The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point of view. The streaming sensor data collected by battery management systems (BMS) reflect the usable battery capacity degradation rates under various operational working conditions. The battery capacity in different cycles can be measured with the temporal patterns extracted from the streaming sensor data based on the attention mechanism. The attention-based similarity regarding the first cycle can describe the battery capacity degradation in the following cycles. The deep degradation network (DDN) is developed with the attention mechanism to measure similarity and predict battery capacity. The DDN model can extract the degeneration-related temporal patterns from the streaming sensor data and perform the battery capacity prediction efficiently online in real-time. Based on the MIT-Stanford open-access battery aging dataset, the root-mean-square error of the capacity estimation is 1.3 mAh. The mean absolute percentage error of the proposed DDN model is 0.06{\%}. The DDN model also performance well in the Oxford Battery Degradation Dataset with dynamic load profiles. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.

Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that resolves the critical issues of conventional Li-Ion Batteries (LIBs) such as poor ionic conductivity, interfacial instability, and dendrites growth. In this study, a platform consisting of a high-throughput screening and a machine-learning surrogate model for discovering superionic Li-SSEs among 20,237 Li-containing materials is developed. For the training database, the ionic conductivity of Na SuperIonic CONductor (NASICON) and Li SuperIonic CONductor (LISICON) type SSEs are obtained from the previous literature. Then, the chemical descriptor (CD) and additional structural properties are used as machine-readable features. Li-SSE candidates are selected through the screening criteria, and the prediction on the ionic conductivity of those is followed. Then, to reduce uncertainty in the surrogate model, the ensemble method by considering the best-performing two models is employed, whose mean prediction accuracy is 0.843 and 0.829, respectively. Furthermore, first-principles calculations are conducted for confirming the ionic conductivity of the strong candidates. Finally, six potential superionic Li-SSEs that have not previously been investigated are proposed. We believe that the constructed platform can accelerate the search for Li-SSEs with high ionic conductivity at minimum cost.

There are a half dozen refrigerator-sized cabinets inside a lab at Stanford University's Precourt Institute for energy that is designed for killing bacteria as quickly as possible. Each contains around 100 lithium-ion cells in trays in which the batteries could be charged and discharged dozens of times each day. The batteries used in these electrochemical torture chambers would normally be found in electronics or electric vehicles. Instead, energy is transported in and out of these cells as quickly as possible, generating reams of performance data that artificial intelligence can use to learn how to make a better battery. To estimate how a battery would perform in the future, AI would require data from a battery after it had begun to degrade. It could take months to cycle the battery enough times to get the required data.

We're still quite a ways away from wielding proper Power Loaders but advances in exosuit technology are rapidly changing how people perform physical tasks in their daily lives -- some designed to help rehabilitate spinal injury patients, others created to improve a Marine's warfighting capabilities, and many built simply to make physically repetitive vocations less stressful for the people performing them. But German Bionic claims only one of them is intelligent enough to learn from its users' mistaken movements: its 5th-generation Cray X. The Cray X fits on workers like a 7kg backpack with hip-mounted actuators that move carbon fiber linkages strapped to the upper legs, allowing a person to easily lift and walk with up to 30kg (66 lbs) with both their legs and backs fully supported. Though it doesn't actively assist the person's shoulders and arms with the task, the Cray X does offer a Smart Safety Companion system to help mitigate common lifting injuries. "It's a real time software application that runs in the background and can warn the worker when the ergonomic risk is getting too high," Norma Steller, German Bionic's Head of IoT, told Engadget.

Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussian-softmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussian-softmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.