China New Energy Battery Leakage Detection

Fault diagnosis of new energy vehicles based on improved

The new energy vehicle system is in the initial stage of application, so the probability of fault is greater. Therefore, its reliability urgently needs to be improved. In order to improve the fault diagnosis effect of new energy vehicles, this paper proposes a fault diagnosis system of new energy vehicle electric drive system based on improved machine learning and

DGNet: An Adaptive Lightweight Defect Detection Model for New Energy

In 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.

Quality and Reliability Engineering International

Due to the insignificant anomalies and the nonlinear time-varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low accuracy and an inability to precisely determine the type of fault, a method has been proposed that utilizes the Random Forest algorithm (RF) to select key factors influencing voltage, optimizes model

Multi-Fault Diagnosis of Lithium-Ion Battery Systems

Inspired by this, this paper proposes an improved Euclidean distance method and a cosine similarity method for online diagnosis of multi-fault in series connected battery packs, and compares them with the correlation coefficient method. The

Multi-Fault Diagnosis of Lithium-Ion Battery Systems Based on

Inspired by this, this paper proposes an improved Euclidean distance method and a cosine similarity method for online diagnosis of multi-fault in series connected battery packs, and compares them with the correlation coefficient method. The voltage sensor positions are arranged according to the interleaved voltage measurement design.

Detection Method for Leakage Faults in Lithium-Ion Batteries

Battery thermal runaway is a critical factor limiting the development of the battery industry. Battery electrolytes are flammable, and leakage of the electrolyte can easily trigger thermal runaway. Currently, the detection of leakage faults largely relies on sensors, which are expensive and have poor detection stability. In this study, firstly, the leakage behavior of lithium-ion batteries is

Leak Detection of Lithium-Ion Batteries and Automotive

Testing for leak tightness requires some form of leak detection. Although various leak detection methods are available, helium mass spectrometer leak detection (HMSLD) is the preferred and is being used broadly to ensure low air and water permeation rates in cells.

High Response and Selectivity of the SnO

It is well-known that metal-oxide semiconductors (MOS) have significant gas sensing activity and are widely used in harmful gas monitoring in various environments. With the rapid development of new energy vehicles, the monitoring of the gas composition and concentration in LIB has become an effective way to avoid safety problems. However, the

China''s battery electric vehicles lead the world: achievements in

As a result, China''s new energy vehicle market has ranked first in the world since 2015. To systematically solve the key problems of battery electric vehicles (BEVs) such as "driving range anxiety, long battery charging time, and driving safety hazards", China took the lead in putting forward a "system engineering-based technology system architecture for BEVs" and

DGNet: An Adaptive Lightweight Defect Detection Model for New

In 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

Ultrasensitive Detection for Lithium-Ion Battery

Herein, sensors based on rare-earth Nd-doped SnO 2 nanofibers are reported for detecting DMC vapor in LIB. The excellent sensitivity (distinct response to 20 ppb DMC), high response (∼38.13–50 ppm DMC), and

Journal of Energy Storage

Additionally, sensors are widely used by researchers to monitor the composition of electrolytes in batteries. To improve the performance of sensors in detecting electrolyte composition, researchers often optimize the structure design of the components [18, 19], adjust the thickness of the semiconductor layer [20, 21], and implant functional receptors

Advancing fault diagnosis in next-generation smart battery with

Uncovering 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.

Conductometric sensor for ppb-level lithium-ion battery

As one of the ideal energy storage systems, lithium-ion battery These results showed that our research is promising in the preparation of high-performance LIB leakage detection sensors with high responsiveness and rapid detection capabilities, which is important for monitoring the leakage of LIB electrolyte and improving the safety of LIB. Therefore, our

Detection Method for Leakage Faults in Lithium-Ion Batteries

Battery thermal runaway is a critical factor limiting the development of the battery industry. Battery electrolytes are flammable, and leakage of the electrolyte can easily trigger thermal runaway.

SGNet:A Lightweight Defect Detection Model for New Energy

With 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.

Quality and Reliability Engineering International

Due to the insignificant anomalies and the nonlinear time-varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low

Advancing fault diagnosis in next-generation smart battery with

Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in

Ultrasensitive Detection for Lithium-Ion Battery Electrolyte Leakage

Herein, sensors based on rare-earth Nd-doped SnO 2 nanofibers are reported for detecting DMC vapor in LIB. The excellent sensitivity (distinct response to 20 ppb DMC), high response (∼38.13–50 ppm DMC), and superior selectivity and stability of 3%Nd-SnO 2 suggest that it should be a promising candidate for LIB safety monitors.

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3.1.1 Determination Of Helium Leakage Mass Limit According to the regulation, the allowable leakage of liquid fuel within 60 minutes after collision is 1.7kg, and the average low calorific value of gasoline and diesel is 42.7MJ/kg, thus the allowable leakage energy is 72590kj. The maximum leakage energy of a hydrogen fuel cell vehicle within 60

Multi-scale Battery Modeling Method for Fault Diagnosis

and regions such as China [1], the USA, and Europe. With the continuous support of government policies and subsi-dizing measures, the new energy vehicle industry has been mushrooming. As the core component, battery plays a sig-nicant part in the development of the EV industry. Among * Xinhua Liu liuxinhua19@buaa .cn

Ultrasensitive Detection for Lithium-Ion Battery Electrolyte Leakage

The problems of lithium-ion battery (LIB) failure have attracted growing attention since flammable and explosive electrolyte leakage might lead to serious consequences. However, due to the redox-neutral and volatile nature of main electrolyte components, such as dimethyl carbonate (DMC), trace leakages are difficult to detect. Therefore, research on LIB electrolyte

Recent advances in model-based fault diagnosis for lithium-ion

In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and

Leak Detection of Lithium-Ion Batteries and Automotive

Testing for leak tightness requires some form of leak detection. Although various leak detection methods are available, helium mass spectrometer leak detection (HMSLD) is the preferred

Gas sensing technology as the key to safety warning of lithium-ion

New energy resources applied in electricity generation have attracted great attention nowadays, especially in the auto industry. Because of the high energy density and enduring use life, the lithium-ion battery has been considered an appropriate electrical power resource for electric vehicles. However, cells with high energy density are more inclined to

Recent advances in model-based fault diagnosis for lithium-ion

In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and the identification of system parameters; (2) an elaborate exposition of design principles underlying various model-based state observers and their

SGNet:A Lightweight Defect Detection Model for New Energy

With 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

Battery leakage fault diagnosis based on multi-modality multi

In this paper, the performance abnormalities of normal battery and real-vehicle electrolyte leakage battery are firstly analyzed by experimental comparison, and found that there are behaviors such as the increase of ohmic resistance in the full SOC interval, the decrease and leftward shift of the peak of the incremental capacity curve, the

Conductometric sensor for ppb-level lithium-ion battery

With the increasing installation of battery energy storage systems, the safety of high-energy-density battery systems has become a growing concern. Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional

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