Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

• Published in: Applied sciences 2020-03, Vol.10 (6), p.1940
• Main Authors: Pang, Xuelian, Li, Zhuo, Tseng, Ming-Lang, Liu, Kaihua, Tan, Kimhua, Li, Hengyi
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alternative energy sources; Convergence; Electric vehicles; Energy industry; Explosions; Fireworks; fireworks algorithm; grey neural network; Impact strength; improved fireworks algorithm; Mapping; Methods; Mutation; Network reliability; Neural networks; Population; Prediction models; Principal components analysis; relay lifetime prediction; Standard deviation; Variables; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10061940

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.