Performance quantification of signal interfaces in hybridized long vehicle combinations

Abstract

This study develops a predictive control algorithm for hybrid trucks operating under reduced information exchange conditions. In these conditions, the signal interface of an electric converter dolly (E-dolly) transmits limited vehicle state and control signals. This e-dolly connects the first and second trailers in an A-double vehicle combination. The study compares fuel consumption across different power split strategies. Simulation results showed that lower fuel consumption was achieved by formulating nonlinear optimal control problem and solving it using sequential linear programming (SLP). In addition, a grid search method was used in rule-based hybrid truck model to adapt it to different topography and speed limits. The study also introduces the real-time iterative (RTI) method for updating in real time, which achieves lower fuel consumption compared to grid search. This study not only verifies the effectiveness of SLP in dealing with nonlinear programming (NLP) problems, but also explores the potential for real-time updates. This is particularly relevant in scenarios with reduced information exchange through the signal interface. Based on these methods, optimal energy allocation strategies can be provided for different external environments.