This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Reverse-Engineering Method for Powertrain Parameters Characterization Applied to a P2 Plug-In Hybrid Electric Vehicle with Automatic Transmission
ISSN: 2641-9637, e-ISSN: 2641-9645
Published June 30, 2020 by SAE International in United States
Citation: DiPierro, G., Galvagno, E., Mari, G., Millo, F. et al., "A Reverse-Engineering Method for Powertrain Parameters Characterization Applied to a P2 Plug-In Hybrid Electric Vehicle with Automatic Transmission," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(1):715-730, 2021, https://doi.org/10.4271/2020-37-0021.
Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm. In this regard, the methodology was applied on a P2 Diesel Plug-in HEV equipped with a 9-speed AT. Voltage and current sensors are used to measure the electrical power exchanged between battery and electric motor; a torque sensor on the propeller shaft measures the total torque coming out from the automatic transmission. The hydraulic pressures in the four brake calipers are measured and CAN data is logged. The results of the testing campaign are then presented and discussed. Functional models of powertrain subsystems are introduced and their parameters estimated using least square method. The good match between models and experimental data proved that the proposed methodology, if properly adapted to the specific layout, is a suitable tool for powertrain parameter estimation.