Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data

Jiang, Lulu; Deng, Zhongwei; Hu, Luojia; Lin, Xianke; Hu, Xiaosong

doi:10.1016/j.energy.2021.121266

Cited by 116 publications

(28 citation statements)

References 34 publications

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“…Figure shows this diagnosis results in an alarm for the single #Cell 31 only at the 312th moment (corresponding to the 352nd moment of voltage data), while the feature extracted in this paper is a warning signal at the 280th moment (for the 320th moment of voltage data), 32 sampling points ahead. By comparing with the normalized cell voltage features in ref , the effectiveness of the proposed features in this paper for the amplification and early warning of minor fault features is shown by Figure in Section .…”

Section: Resultsmentioning

confidence: 99%

“…The current research on battery for electric vehicles has been mentioned in many types of literature, such as battery fault diagnosis, estimation of remaining useful life for batteries, state of the health estimation, etc. − And the research approaches in the literature about fault diagnosis can be broadly classified into three categories: knowledge-based, model-based, and data-driven fault diagnosis approaches. − Among them, the knowledge-based fault diagnosis method uses some historical and empirical knowledge of the battery to design some diagnostic rules for fault diagnosis . The model-based approach is to establish a physical model of the battery, which is generally capable of accurately calculating the values of the parameters of the battery.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

Zhong

et al. 2022

ACS Omega

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Battery failure has traditionally been a major concern for electric vehicle (EV) safety, and early fault diagnosis will reduce many EV safety accidents. However, the short-circuit signal is generally very weak, so it is still a challenge to achieve a timely warning of battery failure. In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning. Third, a reference cell selection method based on K-means clustering is proposed, which can effectively reduce the false alarms caused by the inconsistency of each cell. Fourth, the Fréchet algorithm is introduced into the field of battery pack fault diagnosis and combined with thresholds for battery pack fault diagnosis and localization to accomplish the diagnosis and early warning of minor faults. Finally, the fault diagnosis method is validated by three actual running electric vehicles to verify the effectiveness, reliability, and robustness of the method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

Zhong

et al. 2022

ACS Omega

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show abstract

“…Lyu et al [9] designed an online dynamic impedance measurement device for realtime overcharge warning and early thermal runaway prediction of lithium-ion batteries, which can effectively reduce the failure rate of thermal runaway. Jiang et al [10] proposed a fault diagnosis and thermal runaway warning method of the lithium-ion battery pack with standard voltage as the identification object, which can achieve not only accurate World Electr. Veh.…”

Section: Introductionmentioning

confidence: 99%

An Early Warning Protection Method for Electric Vehicle Charging Based on the Hybrid Neural Network Model

Zheng

Gao

Zhu

et al. 2022

WEVJ

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During the charging process of the electric vehicle (EV), a spontaneous combustion accident may occur due to overheating of the battery, causing personal danger and property damage. To address the charging safety of EVs, this paper proposes a new hybrid EV charging process early warning protection method by combining Convolutional Long-Short Term Memory (ConvLSTM), the sliding window method, and the residual analysis method. The method is fully trained by extracting the deep features of EV charging data through ConvLSTM, eliminating the influence of erroneous transmission data through the sliding window method, and setting a reasonable warning threshold through the residual analysis method. The cross-validation results showed that among the four training sets, the ConvLSTM model of training, set three, had the highest prediction accuracy compared with the CNN, LSTM, BiLSTM and CNN-LSTM models, with RMSE reaching 0.029, MAPE reaching 11.37, and r2 reaching 0.89. Training set one had the worst prediction in the four training sets, and after using it to set the warning threshold, the alarm task was completed five sampling points earlier. Therefore, the hybrid model can quickly complete the safety warning task, thereby ensuring the safety of EV charging.

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“…Gokmen et al [29] designed a novel method based on wavelet-neural network (WNN) utilizing the actual voltage value for the lithium-ion battery pack in series online. Jiang et al [30] proposed a novel data-driven method for lithium-ion battery pack fault diagnosis and normalized battery voltages, which are used to achieve the accurate identification of battery early faults. However, these approaches lack the ability to detect and locate the position of faulty cells, nor can it detect potential abnormal changes without obvious faults.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiscale Permutation Entropy-Based Fault Diagnosis and Inconsistency Evaluation Approach for Lithium Battery of E-Vehicles

2022

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Lithium battery is a complex nonlinear time-varying system with several inconsistencies. The fault diagnosis method has difficulty making an early diagnosis of battery faults without obvious abnormalities. In fact, voltage inconsistency is a representative fault response. Therefore, the monitoring of voltage inconsistency is highly important for the safe and reliable operation of lithium batteries in Evehicles. The entropy method does not rely on an accurate analysis model and expert experience. Moreover, it does not consider the complex fault mechanism and system structure. Hence, it has gradually attracted widespread attention. Given such attention, a hybrid fault diagnosis method combining multiscale permutation entropy (MPE) and coefficient of variation (CV) is presented in this paper, and the improved MPE fault diagnosis model based on 3-sigma is emphasized. First, MPE and the 3-sigma rule are used to calculate the threshold, and the voltage inconsistency of the battery is judged by the threshold. Then, the location of the faulty cells is located by the CV. The superiority of the proposed method is proven by experimental data from the Yunzhitong platform of CRRC Electric Vehicle Co., Ltd. and a comparison of frontier methods. The proposed approach is feasible and promising in real E-vehicle applications. INDEX TERMS Electric vehicles;Lithium battery; multi-scale permutation entropy (MPE); coefficient of variation (CV) rule; 3-sigma rule

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Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data

Cited by 116 publications

References 34 publications

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

An Early Warning Protection Method for Electric Vehicle Charging Based on the Hybrid Neural Network Model

A Hybrid Multiscale Permutation Entropy-Based Fault Diagnosis and Inconsistency Evaluation Approach for Lithium Battery of E-Vehicles

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