Development and Simulation of Real-Time Early Warning Protection System for Electric Vehicle Charging Based on a Two-Layer Protection Model

Jiang, Linru; Li, Taoyong; Li, Bowen; Diao, Xiaohong; Zhang, Jing

doi:10.3390/wevj13050073

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…It is essential that the rectification and uniform voltage control of electric vehicles adhere to SAEJ1772 [154]. SAEJ2293 [155] defines the specifications for charging an EV from a utility or microgrid employing an off-board charger. In order to integrate the system, one must adhere to SAEJ2836 [155].…”

Section: Standards Of the Charging Stationmentioning

confidence: 99%

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An Extensive Critique on Electric Vehicle Components and Charging Systems

Iqubal

Sathiyan

Stonier³

et al. 2022

International Transactions on Electrical Energy Systems

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Nowadays the demand for electric vehicles has been increasing due to their dropping price range and zero emission of carbon. This article initially discusses the development of various electric vehicles from the past to the present. Since the electric motor plays a significant role in EVs, various motors suitable for EVs have been identified and surveyed in this work. The battery storage system is a critical component of EVs; hence, the article goes over all the different types of batteries, from lead acid to lithium ion. Additionally, the other vehicle components such as converters required for charging the batteries, intelligent controllers, electric vehicle charging process, power management, and battery energy management concepts that are available are discussed in detail. Moreover, we bring our work to a conclusion by outlining the research opportunities that remain for the academic and industrial groups. Therefore, the proposed work intends to assist as a state-of-the-art reference for researchers in the field of various electric vehicle configurations, storage systems, converter configurations, charging techniques, control methods, and modulation methods.

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Section: Standards Of the Charging Stationmentioning

confidence: 99%

“…SAEJ2293 [155] defines the specifications for charging an EV from a utility or microgrid employing an off-board charger. In order to integrate the system, one must adhere to SAEJ2836 [155]. e various standards, their scopes, types of charging, and year of implementation are summarized in Table 6.…”

Section: Standards Of the Charging Stationmentioning

confidence: 99%

An Extensive Critique on Electric Vehicle Components and Charging Systems

Iqubal

Sathiyan

Stonier³

et al. 2022

International Transactions on Electrical Energy Systems

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“…With the development of the industry, the safety of EVs has become one of the hot issues, which is not only the key to ensuring the safety of life and property, but also an important guarantee for the rapid development of the EV market. Realizing the safety early warning of the power battery of EVs and establishing a perfect mechanism have become the focus [4,5]. It has been found that the thermal runaway of the battery is an important cause of the spontaneous combustion of the EV charging [6,7].…”

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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