Fault diagnosis of new energy vehicles based on PSO-IBP neural network

Abstract This paper proposes a fault diagnosis method for wireless charging system (WCS) of new energy vehicles based on Particle Swarm Optimization (PSO)-IBP. Firstly, the momentum factor and learning rate of back propagation (BP) neural network are optimized. Secondly, the improved BP neural network and PSO algorithm are fused to improve the fault diagnosis accuracy. Finally, taking the fault of charging equipment of new energy vehicles as an example, it is verified that the proposed method can achieve fast and accurate fault diagnosis in the WCS of new energy vehicles.

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Publication Details

Journal
International Journal of Low-Carbon Technologies
Published
2025-01-01
DOI
https://doi.org/10.1093/ijlct/ctaf052
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Fault diagnosis of new energy vehicles based on PSO-IBP neural network

Yinyuan Qiu, Han Liu, C. X. Liu
International Journal of Low-Carbon Technologies
Advanced Sensor and Control Systems
article

Fault diagnosis of new energy vehicles based on PSO-IBP neural network

Yinyuan Qiu, Han Liu, C. X. Liu
article en

Abstract

Abstract This paper proposes a fault diagnosis method for wireless charging system (WCS) of new energy vehicles based on Particle Swarm Optimization (PSO)-IBP. Firstly, the momentum factor and learning rate of back propagation (BP) neural network are optimized. Secondly, the improved BP neural network and PSO algorithm are fused to improve the fault diagnosis accuracy. Finally, taking the fault of charging equipment of new energy vehicles as an example, it is verified that the proposed method can achieve fast and accurate fault diagnosis in the WCS of new energy vehicles.

International Journal of Low-Carbon TechnologiesVol. 20
National University of Singapore (SG), Changzhou Institute of Technology (CN), Fujian University of Technology (CN)
Openalex Percentile: Top 94%
Advanced Sensor and Control Systems
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