We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
Customer ServiceAs the main component of the new energy battery, the safety vent usually is welded on the battery plate, which can prevent unpredictable explosion accidents caused by the increasing internal pressure of the battery. The welding quality of safety vent directly affects the safety and stability of the battery; so, the welding-defect detection is of great significance. In
Customer ServiceXu et al propose a deep learning defect detection method based on an enhanced YOLOv5 algorithm, aimed at addressing the low efficiency of manual detection in
Customer ServiceDeveloping new energy vehicles has been a worldwide consensus, and developing new energy vehicles characterized by pure electric drive has been China''s national strategy. After more than 20 years of high-quality development of China''s electric vehicles (EVs), a technological R & D layout of "Three Verticals and Three Horizontals" has been created, and
Customer ServiceHealth monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery
Customer ServiceThis paper leverages Baidu''s New Energy Vehicle (NEV) live operation data as the foundation for experimentation. Multiple sensors are implemented to monitor the new energy battery, taking measurements of the battery pack''s voltage, current, and temperature, and estimating its State of Charge (SOC) and State of Health (SOH). The data
Customer ServiceUncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in
Customer ServiceWith a swift detection time of 0.073 seconds per image, the model meets the stringent requirements for accuracy and real-time performance in identifying battery collector tray defects within real-world industrial environments.
Customer Serviceject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map, Others: Bounding box, Corner map, Density
Customer ServiceWith a swift detection time of 0.073 seconds per image, the model meets the stringent requirements for accuracy and real-time performance in identifying battery collector tray
Customer ServiceAccurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage...
Customer ServiceUncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in battery fault diagnosis driven by AI and multidimensional data.
Customer Serviceject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map,
Customer ServiceIn March 2019, Premier Li Keqiang clearly stated in Report on the Work of the Government that "We will work to speed up the growth of emerging industries and foster clusters of emerging industries like new-energy automobiles, and new materials" [11], putting it as one of the essential annual works of the government the 2020 Report on the Work of the
Customer ServiceAccurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in
Customer ServiceRead the latest articles of Energy Reports at ScienceDirect , Elsevier''s leading platform of peer-reviewed scholarly literature
Customer ServiceStanford researchers have unveiled a new type of frequency comb, a high-precision measurement device, that is innovatively small, ultra-energy efficient, and exceptionally accurate.
Customer ServiceThe 2022 International Symposium on New Energy Technology Innovation and Low Carbon Development Edited by Islam Md Rizwanul Fattah - [email protected] Volume 8, Supplement 7,
Customer ServiceXu et al propose a deep learning defect detection method based on an enhanced YOLOv5 algorithm, aimed at addressing the low efficiency of manual detection in metal surface defect detection. The algorithm demonstrates significant improvements in mAP and FPS on the dataset, enabling quick and accurate identification of metal surface defects with
Customer ServiceThis paper leverages Baidu''s New Energy Vehicle (NEV) live operation data as the foundation for experimentation. Multiple sensors are implemented to monitor the new
Customer ServiceWe conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-r
Customer ServiceAs the ownership of new energy vehicles (NEVs) is experiencing a sustained growth, the safety of NEVs has become increasingly prominent, with power battery faults emerging as the primary cause of fire accidents in NEVs. Successful detection of incipient faults can not only improve the safety and reliability but also provide optimal maintenance
Customer ServiceHealth monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving.
Customer ServiceMarket Research Report Summary. Global New Energy Battery X-Ray Intelligent Detection Equipment Market 2023 by Manufacturers, Regions, Type and Application, Forecast to 2029 report is published on March 8, 2023 and has 95 pages in it. This market research report provides information about Manufacturers, Machinery, Industry & Manufacturing industry.
Customer ServiceIn order to reduce application costs and conduct real-time detection with limited computing resources, we propose an end-to-end adaptive and lightweight defect detection model for the battery current collector (BCC), DGNet. First, we designed an adaptive lightweight backbone network (DOConv and Shufflenet V2 (DOS) module) to adaptively extract
Customer ServiceRead the latest articles of Energy Reports at ScienceDirect , Elsevier''s leading platform of peer-reviewed scholarly literature
Customer ServiceIn order to reduce application costs and conduct real-time detection with limited computing resources, we propose an end-to-end adaptive and lightweight defect detection
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