Low-Emission Maximum-Efficiency Tracking of an Intelligent Bi-Fuel Hydrogen-Gasoline Generator for HEV Applications

This paper presents a bifuel hydrogen-gasoline internal combustion engine (ICE) as an effective strategy for extending the electric vehicle's ranges. The electric power produced by the proposed ICE linked with a generator is a nonlinear function of the engine speed and the proportions of hydrogen and gasoline mixed fuel can be approximated around operating conditions. This nonlinear function is approximated by the Taylor series and a comparative study between the obtained results and the experimental data showed the effectiveness of the proposed approach. Furthermore, we observed that the Taylor series approach can achieve less than 7% error, while the modeling with an artificial neural network or a recursive least square method results in more than 8% error. To enable the ICE operation with maximum efficiency, a nonlinear optimization method is used. The proposed maximum efficiency tracking approach is compared with that of the most used industrial methods based on constant speed. The results show that the proposed approach can result in more than 7% of saving in energy, compared to that of the industrial method.