An Active and Passive Hybrid Battery Equalization Strategy Used in Group and between Groups

Gao, Mingyu; Qu, Jifeng; Liang, H.; Wu, Qixing; Lin, Huipin; Dong, Zhekang; Zhang, Weizhong

doi:10.3390/electronics9101744

Cited by 22 publications

(14 citation statements)

References 22 publications

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“…The detection of manufacturing faults in batteries is crucial to enhance safety precautions. Batteries are overtaking other energy storage systems in many contexts, including electric vehicles, grid electricity storage, and a variety of electronic devices, due to their superior compactness, power density, long life, low toxicity, and low self-discharge rate [ 9 ]. As with other high-energy storage devices, batteries provide some danger, and there is always the possibility of faults in manufacturing design or material distribution.…”

Section: Introductionmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation.

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Section: Introductionmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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“…For higher equalization efficiency, distributed equalization is widely used. Gao et al divided the battery pack into two layers and combined active and passive equalization, with intergroup equalization at the top layer and intragroup equalization at the bottom layer 14 15 …”

Section: Introductionmentioning

confidence: 99%

Research on serial lithium‐ion battery alternating discharge equalization control systems

Zhang,

Sun,

Huang

et al. 2023

Energy Science & Engineering

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To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge.

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“…Passive equalization [10] dissipates the excess energy through parallel resistors, which is simple and low cost to control, but it has problems such as high losses and significant temperature rise during equalization. Active equalization [11]- [14] uses inductors, capacitors, and converters to achieve inter-cell energy transfer. Vardhan et al presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Equalization Strategy Based on Credibility Factor Inference for Lithium-Ion Battery Packs

et al. 2022

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In this paper, a two-stage equalization topology based on Zero-Voltage Switching Quasi-Resonant Converter (ZVS QRC) is proposed to solve the problems of long equalization time and low energy utilization of series lithium-ion battery packs. The equalization topology is divided into two forms: intra-group and inter-group, ZVS QRCs are adopted in both intra-group and inter-group, which can equalize any single cell within a group, and the equalization circuit within each pack can equalize simultaneously, which greatly reduces the equalization time, and reduces the switching loss during the equalization process by introducing quasi-resonant circuit to improve the energy utilization. Taking the State of Charge (SOC) as the equalization target, a group equalization strategy based on the Credibility Factor (C-F) inference model is proposed. The strategy defines the SOC unbalance degree of a single battery, gets the group unbalance degree according to the C-F inference model, and gets the group equalization state from the unbalance degree, to control the group for equalization. Matlab/Simulink software is used to build the model and simulate it. The experimental results demonstrate that the proposed topology saves approximately 25% of the equalization time compared to the traditional Buck-Boost group equalization topology. Under the same static and charge/discharge conditions, the equalization time is reduced by about 21% and the energy utilization is improved by about 6% after using the C-F inference model for equalization compared to the fixed-threshold method.

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An Active and Passive Hybrid Battery Equalization Strategy Used in Group and between Groups

Cited by 22 publications

References 22 publications

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Research on serial lithium‐ion battery alternating discharge equalization control systems

Research on Equalization Strategy Based on Credibility Factor Inference for Lithium-Ion Battery Packs

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