A Neural Network-Based Control Algorithm for a Hydraulic Hybrid Powertrain System

Significant research efforts are invested in the quest for solutions that will increase the fuel economy and reduce the environmental impacts of ICE-powered vehicles.The main objective of the study presented in this paper has been to analyze and assess the performance of a control methodology for a parallel hydraulic hybrid powertrain system of a transit bus.A simulation model of the vehicle has been calibrated by analyzing data obtained during an experiment conducted in real-world traffic conditions aboard a Belgrade transit bus.A Dynamic Programming optimization procedure has been applied on the calibrated powertrain model and an optimal configuration that minimizes the fuel consumption has been selected.A Neural Network-based, implementable control algorithm has then been formed through a machine learning process involving data from the optimal, non-implementable Dynamic Programming-based control.Several Neural Network configurations have been tested to obtain the best fuel economy for the range of conditions encountered during normal transit bus operation.It has been shown that a considerable fuel consumption reduction on the order of 30% could be achieved by implementing such a system and calibration method.