Equivalent consumption minimization strategies of series hybrid city buses

With ever growing concerns on energy crisis and environmental issues, alternative clean and energy efficient vehicles are favoured for public applications. Internal combustion engine(ICE)-powered series hybrid buses and fuel cell (FC) hybrid buses, respectively as a near-term and long-term strategy, have a very promising application prospect. The series hybrid vehicle utilizes an ICE/FC as the main power source and a battery/ultra capacity (UC) as the auxiliary power source. The main power source supplies the average vehicle power, and the auxiliary power source functions during accelerating and decelerating. Because the battery/UC fulfills the transient power demand fluctuations, the ICE/FC can work steadly. Thus, the durability of the fuel cell stack could be improved compared with a pure FC-powered bus in the FC series hybrid bus. And the PM and NOx can be greatly lowered in the ICE series hybrid bus compared with a traditional city bus. Besides, the ability of the energy storage source to recover braking energy enhances the fuel economy greatly. The hybrid configuration raises the question of energy management strategy, which chooses the power split between the two. The strategy is developed to achieve system-level objectives, e.g. fuel economy, low emission and battery charge-sustaining, while satisfying system constraints. Energy management strategies in the recent literature can be generally categorized into two types: rule-based strategies and optimal strategies. A rule based strategy can be easily implemented for the real-time applications based on heuristics (N.Jalil, N.A.Kheir & M.Salman, 1997). Such a strategy could be further improved by extracting optimal rules from optimal algorithms (S.Aoyagi, Y.Hasegawa & T.Yonekura, 2001). Optimal strategies differ from each other in the time range. Fuel consumption in a single control cycle is minimized in an instantaneous optimal strategy (G.Paganelli, S.Delprat & T.M.Guerra, 2002). And a global optimal strategy minimises it over a whole determined driving cycle using determined dynamic programming method (DDP) (Chan Chiao Lin et al., 2003), or over a undetermined driving cycle using stochastic dynamic programming method (SDP) (Andreas Schell et al., 2005). Other strategies minimize fuel consumption over an adaptive time span, which could be adjusted on the basis of vehicular speed, pedal 7