Rapid temperature prediction method for electronic equipment cabin

Qu, Hongquan; Fu, Shuo; Pang, Liping; Ding, Chen; Zhang, Helin

doi:10.1016/j.applthermaleng.2018.04.001

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…The sliding window residual statistics method can continuously detect changes in the residual statistics characteristics in real-time. When abnormal conditions occur in temperature, its operating characteristics will change so that the new observation vector deviates from the normal operating state space; the charging status of EVs can be determined by calculating the warning and alarm thresholds of EVs [32][33][34]. The width N was chosen to be 100.…”

Section: Analysis Of Prediction Experiments Resultsmentioning

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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Section: Analysis Of Prediction Experiments Resultsmentioning

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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“…Incremental (online) RVFL also succeeds on various time series [114,189,117,118,178]. Incremental RVFL updates its structure or weights when new observations are available.…”

Section: Other Datamentioning

confidence: 92%

“…In [114], RVFL and online sequential RVFL are compared on rainfall prediction, and the results demonstrate OS-RVFL's superiority for rainfall forecasting. In [189], an online RVFL based on slidingwindow is trained to temperature forecasting. In [178], a stacked auto-encoder is trained in offline fashion first, and then an incremental RVFL is established based on the SAE's output when a concept drift is detected.…”

Section: Other Datamentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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“…Thermal prediction, as a major part of thermal management, is an important mean to avoid the high-temperature environment to bring harm to the electronic devices. [4][5][6][7] In recent years, thermal prediction methods for electronic systems have been divided into two main approaches based on mathematical descriptions: physical modeling methods and black-box prediction methods. 8 Usually, physical modeling approaches ignore the high complexity of the model and require the determination of many physical parameters, 9 which is a difficult task to determine.…”

Section: Introductionmentioning

confidence: 99%

“…Thermal prediction, as a major part of thermal management, is an important mean to avoid the high-temperature environment to bring harm to the electronic devices. 4–7…”

Section: Introductionmentioning

confidence: 99%

An on-site temperature prediction method for passive thermal management of high-temperature logging apparatus

Zhi-bo¹,

Zhang

et al. 2022

Measurement and Control

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In the downhole oil and gas industry, temperature prediction is an important means to avoid the hazards brought by the high-temperature environment to electronic devices. An improved adaptive Kalman filter (IAKF) temperature prediction method, used here as a virtual sensor, can predict the instrument temperature in real time. It uses the temperature state transfer matrix as a system adaptive discriminant parameter to improve the prediction accuracy of the model. This approach is a data-driven prediction method for practical, field-deployable application, it does not require modeling of heat transfer mechanisms and does not require data sets. Experiments results show that the IAKF model can effectively predict the changing trend of the apparatus in the next 30 min, and the maximum temperature prediction error is within 6.5°C. Its predictions are more stable and accurate than the extended Kalman filter, and it consumes very little CPU resources to run in embedded devices.

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Rapid temperature prediction method for electronic equipment cabin

Cited by 12 publications

References 24 publications

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

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

Random vector functional link network: recent developments, applications, and future directions

An on-site temperature prediction method for passive thermal management of high-temperature logging apparatus

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