Research on braking intention identification of electric vehicles based on COA-ELM algorithm

Abstract

The identification of braking intention is crucial for enhancing driver assistance features, enhancing braking safety, and maximizing energy recovery efficiency of electric vehicles. To accurately identify braking intention, a novel identification model utilizing an extreme learning machine (ELM) and optimized by the crayfish optimization algorithm (COA) is proposed. Based on extensive braking test data, data processing, model training, and verification are conducted. The initial braking data are denoised using variational mode decomposition (VMD) and Shannon entropy, and the Gaussian mixture model (GMM) is employed to label the braking intention. The brake pedal opening, its change rate, and vehicle speed serve as inputs for the ELM model, with the braking intention label as the output. The COA is utilized to optimize the hidden layer parameters of ELM, thereby enhancing the precision of the intention identification model. The results indicate that, compared with the LSTM model, GRU model and ELM model, the accuracy of the COA-ELM model improves by 2.73%, 1.56%, and 0.39% respectively, with identification accuracy reaching over 99.02%. This offers a reliable modeling basis for the development of subsequent braking strategies.