## 1 Introduction
[19] established a relay life prediction model based on two physical state parameters of relay, namely super-path time and pick-up time, and identified the parameters of the model. The validity of this method is proved by experiments. [PERSON] et al. [20] analyzed the failure mechanism of the relay and the correlation between the physical parameters of the relay. Finally, the prediction model was established based on the selected physical parameters. Prior studies have improved the prediction model or used the combined model for reliability analysis since the single model is not comprehensive enough. For instance, these improved models make up for the shortcomings of the single model to some extent. [PERSON] et al. [21] predicted the remaining service lifetime by extreme learning machine, which could better reflect the degradation process of Rolling Bearings. [PERSON] et al. [22] predicted the remaining lifetime of lithium-ion batteries based on functional principal component analysis and Bayesian method. In sum, the analytical results show that their prediction had higher accuracy. This paper proposes improved fireworks algorithm-grey neural network (IFWA-GNN) model to predict the relay lifetime. The IFWA enhances local search. By adding an improved Gauss transform operator to the traditional fireworks algorithm (FWA), the diversity of fireworks is enhanced and the convergence performance of the algorithm is improved. Secondly, ten sparks with better fitness are selected and each spark is divided into \(p\)-dimension variables. The mean value and standard deviation of each dimension variable are calculated. The distribution of the variable is determined. If the standard deviation is small, the local search should be strengthened. If the standard deviation is large,the position of the optimal solution cannot be determined. The explosion radius should be expanded to carry out the global search. The \(q\) dimensions with better fitness are selected from \(p\)-dimension variables to enhance local search by comparing the standard deviation of each dimension variable. In the traditional FWA, the search and mutation are often performed on the whole sparks, which may lead to insufficient local search ability. The main purpose of this paper is to improve the security and reliability of relay operation by enhancing the prediction accuracy. The experimental results show the IFWA-GNN model has better prediction accuracy on the relay lifetime and can meet the requirements of practical engineering applications. Hence, the objections of this paper are as follows:  
* establish GM (1,5) model to test the temperature, dynamic contact voltage drop peak, static contact voltage drop, dynamic contact resistance duration, and contact bounce time for relay lifetime;
* test the IFWA and FWA by using standard functions; and
* establish the multi-dimensional IFWA-GNN model. The contributions of this paper are as the follows. (1) The IFWA is introduced. (2) The IFWA-GNN model is proposed to predict the relay life. (3) The electric vehicle reliability is improved by improving the prediction effect of the model. The rest of this paper is structured as follows. Section 2 presents FWA and IFWA. Section 3 introduces the relay experiment and parameter analysis. Section 4 introduces the experiment and a detailed analysis of the experimental results. Section 5 presents the conclusions, contributions, limitations, and future studies.