Plug-In hybrid vehicles have a complex propulsion system management, trying to manage the conventional and electric motorization in the most energy efficient way according to the driving dynamics, topography and battery charge state. In this sense, the aim of this work is to analyze the energy performance of plug-in hybrid vehicles, based on road tests, under real conditions of use, focusing on the management system of the two energy sources present, varying the level of battery charge at the start of the test to visualize the impact of this change. To complement the analysis and in order to better understand the operation of the management system, a methodology for applying the VSP parameter is used, which allows the load state to be approximated according to the vehicle’s operating mode, alternating between the three modes according to the conditions at the time in question, prioritizing the electric motor when the state of charge of the battery is maximum. These results confirm the fact that plug-in hybrid vehicles allow better electricity management due to the diversity of external or internal charging sources, which makes this type of vehicle more efficient and versatile than conventional hybrids, allowing a reduction in fossil fuel consumption and consequently a reduction in the emission of pollutant gases, making this type of vehicle a very competitive alternative in the transport sector in view of the current challenges due to the goals present in the current European regulations. Keywords: Plug-in hybrid vehicles, Energy assessment, Climatization systems, Load support, State of charge

<div class="section abstract"><div class="htmlview paragraph">Plug-in hybrid vehicles have complex energy management and are very influenced by charging behavior. Since current on-road data analysis methodologies do not take into consideration propulsion management, it is difficult to correctly estimate their energy and emissions impacts. This paper presents a methodology (ABCD Method) based only on dynamic data to assess the battery state of charge, identifying, second by second, which propulsion source is working and estimating the correspondent energy consumption, pollutant gas emissions and utility factor. Using a Portable Emission Measurement System, this methodology was developed based on real driving data obtained on four trips in two different PHEV. Excellent correlations were obtained between the estimated and measured SOC (R2&gt;0.98). The ABCD Method uses a generic algorithm to allocate points of different propulsion combinations, correctly identifying the energy source used in over 81.9% of trip points. The ABCD method and Vehicle Specific Power (VSP) method were compared with the measurement data from the four trips, indicating that the ABDC method presents in all situations better estimates, except for HC emission, standing out in the estimate of the electric energy use (9.8% ± 6.5% vs 42.1% ± 20.8%, compared with VSP) and utility factor (5.0% ± 3.4% vs 24.7% ± 1.7%). The ABCD method is of major importance for improving the VSP methodology and the current RDE driving data analysis tools used by the European Commission since this method allows knowing which propulsion system is used at each instant.</div></div>