Dynamic state estimation for the advanced brake system of electric vehicles by using deep recurrent neural networks

Abstract

Dynamic state estimation is of considerable importance to the system monitoring, advanced control, and energy management of electrified vehicles (EVs). Among the dynamic states of various vehicle systems, the brake pressure is a key state that reflects the braking intent and maneuver of a driver and is highly correlated with the safety and energy performance of an EV. Thus, it is worth formulating a high-precision estimation algorithm for the brake pressure to better identify the braking intent of a driver and further enhance the multiperformance of the EVs. In this article, an integrated time-series model (TSM) based on multivariate deep recurrent neural networks (RNN) with long short-term memory (LSTM) units is developed for the dynamic estimation of the brake pressure of EVs. The naturalistic driving data are collected using a real electric vehicle under standard driving cycle scenarios. The signals of the vehicle and system states are measured using the controller area network (CAN) bus and preprocessed for model training and prediction. Next, a real-time multivariate LSTM-RNN model for brake pressure estimation is constructed based on the integrated speed estimation model. The real-time scheme iteratively estimates the future velocity and integrates this signal with other vehicle states to estimate a precise value of the braking pressure. The proposed integrated TSM approach is compared with several existing baseline methods to demonstrate the advantage of the method. The testing results indicate that the proposed integrated TSM method can achieve a more reliable multistep prediction with a higher accuracy compared to that of the other methods, which demonstrates the feasibility and effectiveness of the proposed approach.