How can artificial intelligence bring us closer to a more efficient, more easily recycled and better batteries? Recharge Industries has just announced it will build a $300 million lithium ion battery "gigafactory" in Geelong, Victoria, targeting 2 GWh of production a year in 2024 and 6 GWh by 2026. Lithium ion batteries are in growing demand worldwide with the expected skyrocketing introduction of electric vehicles. But beyond this news, Recharge Industries will also partner with Deakin University's Applied Artificial Intelligence Institute (A2I2) in Geelong to use artificial intelligence to build a better battery. The idea of using AI to improve batteries is not new, but A2I2 has created an operating system specifically designed for the lithium ion battery project, to speed up the design process.

To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks, and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.

Battery energy storage system (BESS) can effec-tively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the bat-tery degradation per a given scheduled battery operational pro-file. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.

NXP is using Electra Vehicles' EVE-Ai 360 Adaptive Controls technology to use digital twin models in the cloud to predict and control the physical BMS in real time, to improve battery performance, battery state of health of up to 12% and enable multiple new applications, such as EV fleet management. Batteries remain the costliest element in an electric vehicle (EV), and AI-powered digital twin cloud services have the potential to improve estimations of the battery's state of health (SOH) and state of charge (SOC) to deliver improved efficiency, lifetime and cost. Battery digital twins adapt to ongoing changes in battery health due to operating conditions and provide updated figures back to the BMS for continuously improving control decisions. Carmakers can use the technology to provide driver insights, such as range and speed recommendations. In addition, adaptive battery control can improve the battery's performance and safely extend its lifespan, reducing warranty costs for the carmaker.

During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask.

To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics. The conventional SOH estimation approaches with digital twin are end-of-cycle estimation that require the completion of a full charge/discharge cycle to observe the maximum available capacity. However, under dynamic operating conditions with partially discharged data, it is impossible to sense accurate real-time SOH estimation for LIBs. To bridge this research gap, we put forward a digital twin framework to gain the capability of sensing the battery's SOH on the fly, updating the physical battery model. The proposed digital twin solution consists of three core components to enable real-time SOH estimation without requiring a complete discharge. First, to handle the variable training cycling data, the energy discrepancy-aware cycling synchronization is proposed to align cycling data with guaranteeing the same data structure. Second, to explore the temporal importance of different training sampling times, a time-attention SOH estimation model is developed with data encoding to capture the degradation behavior over cycles, excluding adverse influences of unimportant samples. Finally, for online implementation, a similarity analysis-based data reconstruction has been put forward to provide real-time SOH estimation without requiring a full discharge cycle. Through a series of results conducted on a widely used benchmark, the proposed method yields the real-time SOH estimation with errors less than 1% for most sampling times in ongoing cycles.

Robots have been defined as machines that can carry out certain activities or actions without direct contact with them. However, this definition has been referred to as an old definition of robots because the definition actually made drones and other remotely controlled devices be referred to as robots. Many books consulted before writing this post, defined the robots as programmable machines that can carry out complex actions without any external control. This last definition can be attributed to the modern robots as compared to the earlier definition which included drones and early robots. Details obtained from the history of robots show that robots were initially referred to as any mechanized device that can make moves or perform a certain action when activated from a distance with rope or any linking mechanism and such is the belief of early centuries of human history.

On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...

Nanoengineers at the University of California San Diego's Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material--whether existing or new--almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, The project is explored in the Nov. 28 issue of the journal Nature Computational Science. The properties of a material are determined by the arrangement of its atoms. However, existing approaches to obtain that arrangement are either prohibitively expensive or ineffective for many elements.