Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle

Nikolaos Aletras, Stylianos Doulgeris, Zissis Samaras and Leonidas Ntziachristos ()
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Nikolaos Aletras: Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece
Stylianos Doulgeris: Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece
Zissis Samaras: Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece
Leonidas Ntziachristos: Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece

Energies, 2023, vol. 16, issue 3, 1-17

Abstract: The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.

Keywords: fuel consumption optimization; energy management system; hybrid vehicle control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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