Energy management algorithm based on average power demand prediction for plug-in hybrid electric trucks
This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavyduty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The
potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO2 and fuel consumption savings on the road.
ALETRAS Nikolaos;
BROEKAERT Stijn;
BITSANIS Evangelos;
FONTARAS Georgios;
SAMARAS Zisis;
NTZIACHRISTOS Leonidas;
2023-11-15
PERGAMON-ELSEVIER SCIENCE LTD
JRC134490
0196-8904 (online),
www.sciencedirect.com/science/article/pii/S0196890423011317,
https://publications.jrc.ec.europa.eu/repository/handle/JRC134490,
10.1016/j.enconman.2023.117785 (online),
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