Development of an GA-RBF based Model for Penetration of Electric Vehicles and its Projections

Yadav, Sameer; Sudman, Md Sakiul Islam; Kumar Dubey, Pankaj; Vijava Srinivas, Romala; Srisainath, R; Chithra Devi, V.

doi:10.1109/icssas57918.2023.10331883

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The robust development of the electric vehicle industry stands as a pivotal measure for effectively mitigating greenhouse gas emissions and diminishing reliance on oil-dependent transportation [18]. Given the challenging factors of harsh driving environments and complex driving conditions, the likelihood of failures in electric vehicles is notably high.…”

Section: Wpd and Feature Vector Extractingmentioning

confidence: 99%

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng,

Wang

2024

Archives of Electrical Engineering

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This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

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Section: Wpd and Feature Vector Extractingmentioning

confidence: 99%

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng,

Wang

2024

Archives of Electrical Engineering

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Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Matrone,

Ogliari,

Nespoli

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Transports is one of the sectors that produce the highest emissions of CO 2 ; in the last ten years, there has been a process of decarbonization which has led to a considerable increase in Electric Vehicles (EVs). However, the sudden introduction of a large number of Electric vehicle supply equipment (EVSE) supplying electrical energy to EVs could cause problems in the management of the electric grid which must cope with the consequent increase in the electrical load demand. In this context, the 24 hour ahead forecast of the power curve associated with the recharge of EVs becomes of vital importance to ensure the reliability of the electric grid. In this paper, different Machine Learning models based on Recurrent Neural Networks (LSTM, GRU) and with different architectures, are compared based on their capability to accurately predict the power curve of an EV charging station one day in advance. A Sequence to Sequence model has been implemented and a thorough analysis of an Attention layer has been detailed. The models are tested on a real world open dataset.

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Development of an GA-RBF based Model for Penetration of Electric Vehicles and its Projections

Cited by 2 publications

References 21 publications

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

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