Title :
Power Smoothing Energy Management and Its Application to a Series Hybrid Powertrain
Author :
Di Cairano, Stefano ; Wei Liang ; Kolmanovsky, Ilya V. ; Kuang, Ming L. ; Phillips, Anthony M.
Author_Institution :
Powertrain Control R&A, Ford Motor Co., Dearborn, MI, USA
Abstract :
Energy management strategies in hybrid electric vehicles determine how much energy is produced/stored/used in each powertrain component. We propose an approach for energy management applied to a series hybrid electric vehicle that aims at improving the powertrain efficiency rather than the total fuel consumption. Since in the series configuration the engine is mechanically decoupled from the traction wheels, for a given power request the steady-state engine operating point is chosen to maximize the efficiency. A control algorithm regulates the transitions between different operating points by using the battery to smoothen the engine transients, thereby improving efficiency. Because of the constrained nature of the transient-smoothing problem, we implement the control algorithm by model predictive control. The control strategy feedback law is synthesized and integrated with the powertrain control software in the engine control unit. Simulations of the urban dynamometer driving schedule (UDDS) and US06 cycles using a complete vehicle system model and experimental tests of the UDDS cycle show improved fuel economy with respect to baseline strategies.
Keywords :
control system synthesis; energy conservation; feedback; hybrid electric vehicles; internal combustion engines; power transmission (mechanical); predictive control; wheels; UDDS; control algorithm; control strategy feedback law synthesis; energy production; energy storage; energy use; engine series configuration; engine transients; fuel consumption; hybrid electric vehicles; model predictive control; power smoothing energy management strategy; powertrain efficiency; series hybrid powertrain; traction wheels; transient-smoothing problem; urban dynamometer driving schedule; Energy management; Hybrid electric vehicles; Mechanical power transmission; Power smoothing; Predictive control; Transient analysis; Automotive applications; energy management; hybrid electric vehicles; model predictive control;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2012.2218656