Using hybrid powertrains is an attractive idea to reduce the fuel consumption in vehicles. Control strategy is the most challenging subject in designing of a hybrid powertrain. In this paper, an optimized control strategy based on the driving cycle type designed for a hydraulic hybrid bus has been presented. Because of considering the type of the driving cycle, the proposed control strategy can be named as an intelligent one. In this controller, at first, four standard driving cycles have been defined as the reference clusters. Then the optimized control strategy for each cluster has been derived using a dynamic programming algorithm. In addition, several multi-layered perceptron networks are modeled in order to use the output of each optimized control strategy. After that a clustering method with a feature selection algorithm has been implemented to assign degree of similarity to each cluster for the unknown driving cycle. Finally, a linear combination of four optimized control strategy outputs has been used for generating final output of the intelligent control strategy. In this combination, each output is weighted by the corresponding degree of similarity. Here, the hydraulic hybrid bus model is a feed forward one and has been simulated using a compound driving cycle. The compound driving cycle consists of six distinct 100s long portions of the Nuremburg driving cycle. The simulation results show that by using the intelligent control strategy, the fuel consumption of the hybrid bus has been reduced by almost 12% in comparison with the results of a rule-based control strategy.
Optimized Control Strategy Based on the Driving Cycle Type for a Hydraulic Hybrid Bus
Safaei, A, Esfahanian, V, Ha’iri-Yazdi, MR, Esfahanian, M, Tehrani, MM, & Nehzati, H. "Optimized Control Strategy Based on the Driving Cycle Type for a Hydraulic Hybrid Bus." Proceedings of the ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. Volume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and Control. Nantes, France. July 2–4, 2012. pp. 819-827. ASME. https://doi.org/10.1115/ESDA2012-82673
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