<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.</p>
</abstract>

New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation,...

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

Pubblicato online: 01 apr 2024

Pagine: -

Ricevuto: 02 feb 2024

Accettato: 12 feb 2024

DOI: https://doi.org/10.2478/amns-2024-0803

Parole chiave
Fault detection, Decision tree algorithm, C4.5 algorithm, Computational efficiency

© 2024 Ping Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

Pubblicato online: 01 apr 2024

Pagine: -

Ricevuto: 02 feb 2024

Accettato: 12 feb 2024

DOI: https://doi.org/10.2478/amns-2024-0803

Parole chiaveFault detection, Decision tree algorithm, C4.5 algorithm, Computational efficiency

© 2024 Ping Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Fault detection, Decision tree algorithm, C4.5 algorithm, Computational efficiency