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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lzzb2pezhjglzjrb2bzaainogu" style="color: black;">Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering</a>
In this paper, a new idea for designing the control strategy for the energy management of hybrid powertrains based on the driving cycle type is presented. Here, every instance of an unknown driving cycle is considered to be similar to the reference driving cycles using similarity weights. 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. The weights<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1177/0954407014556116">doi:10.1177/0954407014556116</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7asxz7jlzzdahidnczaesi2cz4">fatcat:7asxz7jlzzdahidnczaesi2cz4</a> </span>
more »... h are between zero and one are determined using a fuzzy driving cycle identification agent based on the comparison of preselected driving features. The simulation studies in seven different driving cycles show 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 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.
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