Hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology leads to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology [1].
In this paper, a previously proposed energy management strategy based on an offline optimization using dynamic programming is investigated. This is then coupled with an online model predictive control strategy to follow the predetermined optimal battery state of charge trajectory prescribed by the dynamic program. This work explores the effects of drive cycle segmentation and simplification on the optimality of the results and investigates the effect of reduced prediction accuracy on the optimality of the MPC controller. As the dynamic program relies on future predictions of speed and load, potentially provided from navigation data, the actual drive cycle is likely to vary from the prediction used to perform the offline optimization. The test vehicle modelled in Simulink is a P2 parallel hybrid configuration based on experimental powertrain data.
The results of the analysis are then compared to the globally optimal solution using key performance criteria like fuel and energy consumption. Our investigation shows that the energy consumption increase due to poorer prediction accuracy can be up to 19% of the optimal value but also shows that the robustness of the strategy is more acceptable provided certain features of the driveline input can be predicted with a certain degree of accuracy.