This paper proposes adaptive and situation-dependent charging strategies that aim to resolve the conflict between high efficiency and optimal driving performance in hybrid electric vehicles. By using inputs from devices such as navigation system, radar and camera, as well as from standard sensors, driving situations challenging the electrical energy storage device are identified and predicted in real time, onboard the vehicle. In each driving situation a specific charging strategy is selected to manage the limited available electrical energy, in order to better satisfy energy requirements and the need for energy storage capability. Overtaking manoeuvres, entering a highway or a dynamic driving style are examples of driving situations this paper focuses on. In addition to providing an overview of the simulation of energy potentials of such adaptive energy functions, this paper describes a method for driving situation classification using fuzzy probabilistic networks. Using predictive driver assistance systems as well as standard sensors, Bayes networks coupled with fuzzy membership functions were shown to be capable of providing quantitative information about the probability of driving situations in the immediate future. Simulation results show significant improvements in driving performance without deteriorating the overall efficiency of hybrid electric vehicles. Adapting the charging strategy to calm drivers can even result in less fuel consumption.
Adaptive energy management in hybrid electric vehicles: Driving situation and driving style dependent charging strategy for optimal power availability
2008
16 Seiten, 12 Bilder, 8 Quellen
Conference paper
Storage medium
English
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