Toward energy-efficient heavy-duty vehicles: real-time mass estimation via confidence-embedded data-driven methods

Abstract

The accurate estimation of vehicle mass is critical for optimizing energy-efficient driving strategies and ensuring chassis active safety control in new energy vehicles. However, while data-driven methods achieve high accuracy in training scenarios, they suffer from substantial performance degradation when encountering distribution shifts in input data. To address these challenges, this study proposes a novel deep neural network-based vehicle mass estimation model (τ-DNN) that incorporates input data confidence levels. The confidence levels are quantified by calculating the Euclidean distance between the input data and the clustering centers of the training data, which allows for weighted outputs of the estimation results. To enhance the model's ability to extract features from input sequences, this study integrates the multi-head attention mechanism with convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks, thereby achieving more accurate vehicle mass estimation. Finally, 14 loading conditions and six driving conditions were established to train the τ-DNN model using data collected from heavy-duty vehicles (HDVs) in real-world environments. Offline testing using the self-built dataset and online real-time verification demonstrate that the τ-DNN model can effectively mitigate error peaks when the confidence of input data is low, consistently maintaining the estimated error below 10 % and controlling the RMSE to within 1.09 t. Compared with the existing LSTM and cubature Kalman filter (CKF) methods, the proposed algorithm demonstrates superior estimation performance, exhibiting higher estimation accuracy, stability, and generalization capability.