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

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