Fuel consumption reduction in Hybrid Electric Vehicles (HEV) powertrains has been an important area of research over the past few decades. HEV powertrains have two energy sources : fuel and battery. The important task of splitting the energy/power demand between both these sources is performed by the Energy Management Systems (EMS). There are many EMS methods and the focus of
this thesis is on a method called Modular ECMS (MEMS) implemented by TNO. MEMS finds the optimal power split among the subsystems by minimizing the energy loss in each subsystem. This strategy assumes that the operating speed of the subsystems of the powertrains is known and uses this knowledge to find the optimal power split and torque among these subsystems. The objective of this thesis is to find the optimal operating speed of the subsystems as well. This is done by a least squares fitting of the objective function and constraints as functions of subsystems speed and torque. A revised Optimal Control Problem (OCP) is formulated as a quadratic programming problem of speed and torque and is termed as Speed-Torque Coupled MEMS (ST-MEMS). The ST-MEMS algorithm is tested on a series-hybrid wheel loader powertrain model and its performance is compared to MEMS, with the model and data provided by TNO. It is concluded that the ST-MEMS, while adding the speed and torque bounds as degrees of freedom, does not achieve a good distribution of power between the 2 sources. The reason for this behaviour is analyzed and an alternate approachis suggested for future work.