In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging.
In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging.
Then, it is assumed that aging effects are time-varying. Therefore, the fault detection scheme can detect faults of new battery cells as well as aged cells. Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack, to demonstrate the performance of the proposed approach in more real-world scenarios.
Different fault detection approaches based on model, signal-processing, or knowledge can be applied for the battery. The model-based approaches consider an electrochemical model or an equivalent circuit model, to detect faults.
Fault detectors are designed considering battery aging effects: capacity fading and resistance growth. Aging effects are considered in two cases: time-invariant and time varying parameters. In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed.
The SoC residual signal is defined as output estimation error: r soc = y ̃ 1 = y ^ 1 − y 1 By SoC estimation fault occurrence in the battery system, output estimation error will be greater than zero and the residual signal will detect the SoC estimation fault. 3.2. The proposed voltage sensor fault detector 3.2.1.
Conclusion A fault diagnosis scheme considering battery aging effects, is presented in this paper, which is applicable to new battery cells and aged cells. Adaptive observer is an efficient approach which can estimate the aging effects of lithium-ion batteries in the fault detection scheme.
In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging.
Abstract: 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 …
To combat climate change, humanity needs to transition to renewable energy sources [1] nsequently, batteries, which can store and discharge energy from renewable sources on demand [2], have become increasingly central to modern life [3].Battery management systems are critical to maximizing battery performance, safety, and lifetime; monitoring currents and …
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.
,（BCC）DGNet。 , …
The quality of the current collector, an essential component in new energy vehicle batteries, is crucial for battery performance and significantly impacts the safety of vehicle occupants. However, detecting defects in battery current collector in real-time industrial applications with limited computational resources poses a major challenge. To address this, our paper proposes …
,（BCC）DGNet。 ,（DOConv Shufflenet V2 (DOS) ）,,。 , …
To enhance the performance of deep learning-based defect detection models for new energy vehicle battery current collectors, this paper designs inspiration from existing …
DOI: 10.1109/NEESSC59976.2023.10349292 Corpus ID: 266236766; Early Anomaly Detection of Power Battery Based on Time-series Features @article{Li2023EarlyAD, title={Early Anomaly Detection of Power Battery Based on Time-series Features}, author={Heng Li and Zhijun Liu and Xiaolong Chen and Wei Yuan and Muaaz Bin Kaleem and Weirong Liu}, journal={2023 3rd …
Abstract: This paper introduces a new energy battery active-passive hybrid binocular intelligent inspection system, using structured light and laser line-scan instruments to acquire battery surface image information. Based on the existing 3D reconstruction technology, the active-passive hybrid binocular system is designed. In order to reduce ...
Compared with a single battery cell, the new energy battery pack has the following characteristics: 1. Large battery capacity. A battery Pack contains multiple battery cells. So it can provide more stored energy. And its total volume and weight are not too large. 2. The battery management system is perfect. the number of battery cells inside ...
As a high-energy carrier, a battery can cause massive damage if abnormal energy release occurs. Therefore, battery system safety is the priority for electric vehicles (EVs) [9].The most severe phenomenon is battery thermal runaway (BTR), an exothermic chain reaction that rapidly increases the battery''s internal temperature [10].BTR can lead to overheating, fire, …
ject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map, …
With the development of power battery technology, new energy vehicles are receiving more and more attention. The power battery is the only source of driving energy for battery electric vehicle (BEV), which directly affects the power performance, endurance and safety of BEV [44].To ensure the safety of power battery, the functional evaluation has to be done through power battery …
Sun et al. [10] proposed a two-layer fault detection strategy like Gan et al., with the difference that they monitored voltage and temperature and other parameters simultaneously in the first layer strategy, which improved the reliability of battery thermal fault detection. And it was applied to single cell and battery pack with different ...
SGNet (ShuffleNet V2 + G_GFPN), a lightweight model for current collector defect detection, utilizes ShuffleNet V2 as the backbone feature extraction network and a designed G_GFPN as the feature fusion network to ensure high detection accuracy while minimizing computational costs. The quality of the current collector, an essential component in …
Features • Orginal level detection of battery pack : support reading the current SOC/SOH, single/ module voltage, input/output current and power, battery temperature and other parameters of the battery pack. Support reading the …
Abstract: 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. Existing manufacturers usually rely on human eye observation to complete PBD, which makes it difficult to balance the ...
Abstract: This paper introduces a new energy battery active-passive hybrid binocular intelligent inspection system, using structured light and laser line-scan instruments to acquire battery …
With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this ...
This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using …
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 …
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 …
ject 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
To combat climate change, humanity needs to transition to renewable energy sources [1] nsequently, batteries, which can store and discharge energy from renewable sources on …
This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using the basic structure of RBF neural network and the advantages of the reduced clustering algorithm, for a single power battery, the power battery power abnormality detection scheme ...
To enhance the performance of deep learning-based defect detection models for new energy vehicle battery current collectors, this paper designs inspiration from existing literature and designs a defect detection model based on deformable convolution and attention mechanisms: DCS-YOLO.
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. First, we designed an adaptive lightweight backbone network (DOConv and Shufflenet V2 (DOS) module) to adaptively extract ...
In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state …
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