Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model
Source abstract
Abstract
The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power System Reliability and Maintenance · Machine Fault Diagnosis Techniques · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Microelectronics, Tianjin University, Tianjin 300072, China
Department of Electronic and Communication Technology, Tianjin Electronic Information College, Tianjin 300350, China
Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Banji 43600, Malaysia
School of Business, University of Nottingham, Nottingham NG72RD, UK
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
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