Onboard learning-based fuel consumption optimization in series hybrid electric vehicles

This paper considers an on-line approach to reducing engine fuel consumption in a series hybrid electric vehicle operated under power smoothing strategy, in which the generator power is slowly varying. An optimization algorithm adjusts the engine speed set-point (and other engine actuator settings as appropriate) on-line, as the vehicle is driven. The actual generator power is estimated, and the battery power output is adjusted to ensure that the wheel power and the drivability are unaffected. With this approach, operating points on the optimal operating line (OOL) can be learned during vehicle driving thus compensating for the effects of the engine aging and variability while reducing upfront calibration time and effort. In the paper, the control approach is defined and an optimization algorithm is examined. Simulation results are reported.