Data-driven mass estimation of heavy-duty vehicles

This study investigates mixed model- and learning-based approaches to the mass estimation problem in heavy-duty vehicles. Effective mass estimation allows for more precise adjustments to engine power, braking systems, and suspension settings, leading to improved vehicle handling, fuel efficiency, and overall safety. Although direct mass measurement using sensors is a viable option, the substantial costs and complexities associated with sensor maintenance, integration, and calibration drive the search for alternative solutions. The objective of this thesis is to introduce a methodology to mass estimation problem in heavy-duty vehicles, which harnesses the advantages of model-based and learning-based estimation methods. The proposed methodology builds upon the integration of Long-Short Term Memory (LSTM), which is a type of recurrent neural network, that supervises a Recursive Least Squares (RLS) vehicle mass estimator. The RLS estimator relies on a longitudinal vehicle dynamical model. The supervisory LSTM network is offline trained to recognize when the vehicle is operated such that the RLS estimator leads to an estimate with the desired accuracy while online enables the mass estimate update by the RLS estimator based on signals that include vehicle speed, longitudinal acceleration, engine torque, and engine speed. The LSTM network is trained and tested in this thesis work using datasets artificially generated by a widely used simulation environment called TruckMaker. The simulation results demonstrate that the proposed methodology effectively forecasts the reliability of the RLS mass estimator, showcasing the potential of LSTM networks in enhancing the accuracy and trustworthiness of mass estimation of heavy-duty vehicles. This thesis also presents a benchmark learning-based approach to mass estimation in heavy vehicles, that uses an LSTM network. In this case, a two-layer LSTM network is designed that utilizes vehicle speed, longitudinal acceleration, engine speed and engine torque to estimate the vehicle mass.