Mixed Learning- and Model-Based Mass Estimation of Heavy Vehicles

Abstract

This research utilized long short-term memory (LSTM) to oversee an RLS-based mass estimator based on longitudinal vehicle dynamics for heavy-duty vehicles (HDVs) instead of using the predefined rules. A multilayer LSTM network that analyzed parameters such as vehicle speed, longitudinal acceleration, engine torque, engine speed, and estimated mass from the RLS mass estimator was employed as the supervision method. The supervisory LSTM network was trained offline to recognize when the vehicle was operated so that the RLS estimator gave an estimate with the desired accuracy and the network was used as a reliability flag. High-fidelity simulation software was employed to collect data used to train and test the network. A threshold on the error percentage of the RLS mass estimator was used by the network to check the reliability of the algorithm. The preliminary findings indicate that the reliability of the RLS mass estimator could be predicted by using the LSTM network.