A Methodology for the Reverse Engineering of the Energy Management Strategy of a Plug -In Hybrid Electric Vehicle for Virtual Test Rig Development

Nowadays, the need for more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables. Furthermore, the impact that Vehicle-to-Infrastructure (V2I) connections have on the control law is assessed on several tests performing the same real-world route with the vehicle navigation system alternatively switched on and off. Finally, a virtual test rig of the tested vehicle, developed in the GT- SUITE environment, is used to validate the set of extracted rules against the experimental data. An error of about 1-2% on the prediction of the vehicle CO2 emissions and good matching of the State of Charge (SoC) profile in both Charge Depleting (CD) and Charge Sustaining (CS) phases prove the effectiveness of the proposed methodology.

Automated summary

This article proposes a novel methodology to reverse engineer the control strategy of a P2 Plug-in Hybrid Electric Vehicle (PHEV) through data analysis. The impact of Vehicle-to-Infrastructure (V2I) connections on the control law is also assessed. The results show the effectiveness of the proposed methodology in predicting vehicle emissions and state of charge.