Predictive supervisory control strategy for parallel HEVs using former velocity trajectories

This article deals with the usage of forecast information for an optimized supervisory control strategy in hybrid power trains. In contrast to other publications the influence of the prediction quality towards fuel consumption is discussed. Firstly a supervisory control strategy without using predictive driving trajectories is developed. Secondly the integration of predictive information is presented to reduce fuel consumption. The battery size (maximum battery energy) is varied using a 100% ideal prediction. The simulation results are compared with the global optimum reference fuel consumption calculated by Richard Bellman's Dynamic Programming. Velocity prediction is imprecise in real driving scenarios. Therefore recorded driving trajectories of former journeys on the same route were used as prediction source to examine the influence of imprecise prediction to the resulting fuel consumption of the developed predictive control strategy.