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1. Prognostic‐Based Active Battery Degradation Management in Wireless Sensor Networks for Group Replacement

   https://doi.org/10.1002/qre.3695  

   Wu, Lang; Zhou, Qiang; Jo, Hongki; Saleem, Muhammad_M; Qu, Qiang; Jeong, Jong‐Hyun (December 2024, Quality and Reliability Engineering International)

   ABSTRACT Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio. 

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2. Deep Learning Dataset Generation for Physical Layer Authentication in Wireless Sensor Networks (WSN)

   Dentremont, Christopher; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   Structural Health Monitoring (SHM) uses wireless sensor network (WSN) to monitor a civil construction’s conditions remotely and constantly for its sustainable usage. Security in WSN for SHM is essential to safeguard critical transportation infrastructure such as bridges. While WSN offers cost-effective solutions for Bridge SHM, its wireless nature expands attack surfaces, making security a significant concern. Despite progress in addressing security issues in WSN for Bridge SHM, challenges persist in device authentication due to the unique placement of sensor nodes and their resource constraints, particularly in energy conservation requirements to extend the system’s lifetime. To overcome these limitations, this paper proposes an innovative authentication scheme with deep learning at the physical layer. Our approach steers away from conventional device authentication methods: no challenge-response protocol with heavy communication overhead and no cryptography of intensive computation. Instead, we use radio frequency (RF) fingerprinting to authenticate sensor nodes. Deep learning is chosen for its ability to discover patterns in large datasets without manual feature engineering. We model our scheme on IEEE 802.11ah, Wi-Fi HaLow of long-range communication and low-power consumption for machine-to-machine (M2M) applications. Simulations and experiments using universal software radio peripheral (USRP) demonstrate the effectiveness of the proposed scheme. By integrating security into Cyber-Physical System/the Internet-of-Things (CPS/IoT) design of WSN for Bridge SHM, our work contributes to critical infrastructure protection. 

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3. Electric Vehicle Battery End-of-Use Recovery Management: Degradation Prediction and Decision Making

   https://doi.org/10.1115/MSEC2022-85536  

   Zhao, Yixin; Behdad, Sara (June 2022, the ASME Manufacturing Science and Engineering Conference (MSEC))

   Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery's life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise. 

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4. Are Industrial ML Image Classifiers Robust to Data Affected by Network QoS Degradation?

   https://doi.org/10.1109/WNYISPW60588.2023.10349560  

   Zatsarenko, Raman; Marathe, Chirayu Anil; Chuprov, Sergei; Hyland, Matthew; Reznik, Leon (November 2023, IEEE)

   In industrial applications, Machine Learning (ML) services are often deployed on cloud infrastructure and require a transfer of the input data over a network, which is susceptible to Quality of Service (QoS) degradation. In this paper we investigate the robustness of industrial ML classifiers towards varying Data Quality (DQ) due to degradation in network QoS. We define the robustness of an ML model as the ability to maintain a certain level of performance under variable levels of DQ at its input. We employ the classification accuracy as the performance metric for the ML classifiers studied. The POWDER testbed is utilized to create an experimental setup consisting of a real-world wireless network connecting two nodes. We transfer multiple video and image files between the two nodes under varying degrees of packet loss and varying buffer sizes to create degraded data. We then evaluate the performance of AWS Rekognition, a commercial ML tool for on-demand object detection, on corrupted video and image data. We also evaluate the performance of YOLOv7 to compare the performance of a commercial and an open-source model. As a result we demonstrate that even a slight degree of packet loss, 1% for images and 0.2% for videos, can have a drastic impact on the classification performance of the system. We discuss the possible ways to make industrial ML systems more robust to network QoS degradation. 

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5. Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

   https://doi.org/10.3390/en17051250  

   Nazaralizadeh, Solmaz; Banerjee, Paramarshi; Srivastava, Anurag K; Famouri, Parviz (March 2024, Energies)

   With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics. 

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