This article deals with the usage of forecast information for an optimized supervisory control strategy in hybrid power trains. In contrast to other researches the influence of the precision of the prediction is discussed. Firstly a supervisory control strategy without using predictive drive trajectories is developed. Thereafter an integration of predictive information is presented to reduce the fuel consumption. The battery size (maximum battery energy) is varied using a 100% save prediction. The simulation results are compared with the global optimum reference fuel consumption calculated by Richard Bellman's Dynamic Programming. In real driving scenarios the prediction of velocity is imprecise. Examining this influence on the fuel consumption of the developed predictive control strategy driving trajectories of former journeys on the same route are used as predictive data source.
Approach to a predictive supervisory control strategy using vehicle history based forecast information
2011
15 Seiten, 10 Bilder, 2 Tabellen, 10 Quellen
Conference paper
English
Supervisory Control Strategy for Mild Hybrid System - A Model Based Approach
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