The weights, which are between zero and one, are determined using a fuzzy driving cycle identification agent based on the comparison of preselected driving features. To determine the control output in the unknown driving cycle, the weights are applied to a linear combination of the optimal control decisions generated in each of the reference driving cycles.

Simulation studies in seven different driving cycles showed that, while all driving patterns in every driving cycle are considered for the generation of energy management by online implementation of the proposed intelligent control strategy, some driving patterns would be eliminated by using a non-fuzzy identification agent.

This, they said, leads to a significant reduction in the fuel consumption of the hybrid powertrain utilized with the fuzzy identification agent in some driving cycles in comparison with those without the use of non-fuzzy driving cycle identification. In addition, in some driving cycles, the intelligent control strategy has a performance close to that for the offline optimized control strategy.

Resources

• Ali Safaei, Mohammad Reza Ha’iri-Yazdi, Vahid Esfahanian, Mohsen Esfahanian, Masood Masih Tehrani, Hassan Nehzati (2015) “Designing an intelligent control strategy for hybrid powertrains utilizing a fuzzy driving cycle identification agent” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering vol. 229 no. 9 1169-1188 doi: 10.1177/0954407014556116