Construction of load spectrum for pure electric bulldozers based on new K-ICPO clustering algorithm

In construction machinery, constructing an actual working cycle is of great significance for the research of construction machinery control strategy. This paper proposes a novel method for constructing a bulldozer’s working cycle load spectrum, using Improved Crested Porcupine Optimizer (ICPO) to improve the K-means clustering algorithm and cluster the actual working data of bulldozers. The ICPO is obtained by incorporating the Grey Wolf Optimizer and Ant Colony Optimizer into the first and third defense strategies of the Crested Porcupine Optimizer, respectively, and the convergence speed and accuracy of the ICPO are significantly improved. The K-ICPO clustering algorithm is developed by optimizing the K-means clustering algorithm using ICPO. The K-ICPO clustering algorithm achieves an average accuracy of 90.1%, representing an improvement of 2.42%. Using the K-ICPO clustering algorithm, a typical working cycle of pure electric bulldozers is constructed, and the load spectrum constructed is highly similar to the “load change rate” distribution of the original data, proving the accuracy of the load spectrum construction method proposed in this paper.