Mass estimation of tractor-semitrailer systems: An approach of dynamics and data fusion-driven in real environments

Abstract

Accurate mass estimation is crucial for improving vehicle active safety control performance. In the context of tractor-semitrailer systems, the precise estimation of mass presents a formidable challenge. This challenge is rooted in traditional methods affected by model uncertainty, model mismatch, and insufficient training data; furthermore, there is also a lack of algorithm generalization to account for real-world traffic scenarios. To address these challenges, a hybrid algorithm for vehicle mass estimation based on bidirectional long short-term memory (BiLSTM) networks and square-root cubature Kalman filter (SCKF) is proposed in this study. Driven by vehicle time series data, the vehicle mass estimator based on the BiLSTM network is constructed to address model uncertainty. To enhance both estimation accuracy and robustness, the hybrid BiLSTM-SCKF method is constructed by incorporating the BiLSTM mass estimation as the initial state value and measured value in the SCKF algorithm while also formulating a fusion rule. The experimental results under real environments show that the hybrid BiLSTM-SCKF algorithm outperforms single algorithms, particularly under medium and low loads, and the estimated error remains consistently below 10% across all working conditions. Additionally, this proposed method exhibits adaptability to various types of semitrailers, effectively enhancing algorithmic accuracy, reliability, robustness, and interpretability.