Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints

Advanced computer-aided engineering tools are urgently needed to foster the development of electrified road vehicles that would enable abating fuel consumption and pollutant emissions of the transport sector. Concerning plug-in hybrid electric vehicles (HEVs), implementing an energy management strategy that can rapidly estimate near-optimal powertrain control trajectories while effectively dealing with broaded battery state-of-charge (SOC) window utilization and smooth HEV driving requirements still needs extensive development. To overcome the highlighted drawback, this paper introduces a formulation of the slope-weighted energy-based rapid control analysis (SERCA) algorithm which can rapidly identify near-optimal plug-in HEV control trajectories while complying with SOC boundaries and limiting the number of thermal engine activations and gear shifts. The HEV numerical model is introduced first, followed by formulating the optimal plug-in HEV control problem with smooth driving constraints and describing the dedicated SERCA based control approach. A performed case study demonstrates that SERCA can identify smooth driving constrained near-optimal HEV control trajectories for a 1.5 hours-long real-world driving mission within two minutes on a desktop computer, while a global optimal control approach such as dynamic programming (DP) is found to require more than 10 hours to perform the same task. On the other hand, compared with the global optimal reference provided by DP, the increase in estimated plug-in HEV operative cost in terms of fuel and electrical energy consumption associated to SERCA is always contained within few percentage points. The proposed methodology can accelerate HEV powertrain design and on-board supervisory controller development procedures.

Automated summary

An algorithm named SERCA is introduced in this study to rapidly identify near-optimal control trajectories for plug-in HEVs, showing good performance in real-world driving tasks. By optimizing control strategies, it accelerates the process of HEV powertrain design and controller development.