Desain dan Simulasi Model Predictive Control pada Sistem Pembagian Daya untuk Kendaraan Listrik Hibrida Fuel Cell – Baterai

Design and Simulation of Model Predictive Control for Power Distribution in Hybrid Fuel Cell - Battery Electric Vehicles

  • Arini Latifah Universitas Muhammadiyah Gresik
  • Kusnnuri Aditya Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Gresik
  • Satrio Sarwo Mumpuni Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Gresik
  • Muhammad Aqifur Rohman Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Gresik


This research examines the performance of implementing Model Predictive Control (MPC) in the energy management system of fuel cell – battery hybrid electric vehicles. Three parameter variations were carried out on the MPC controller, namely variations in the horizon value, objective function, and weighting in the objective function. In testing variations in the horizon value of the designed MPC controller, it shows that the higher the horizon value used, the SOC and the final SOC are faster and closer to the optimal SOC determined by compensating for higher hydrogen consumption. Testing of objective function variations shows that the objective function implemented on the MPC controller influences the system response characteristics. It was found in objective function testing that optimal use of fuel cell power produces fuel cell power output with a working efficiency range of 57% - 60% when compared to other objective functions that work in the efficiency range of 49.2% - 57%. Finally, in testing variations in weighting values, it was found that the higher the weighting of an expression in the objective function, the more the optimizer will penalize the expression so that the solver will minimize the expression in the optimization process. Therefore, the MPC controller parameter values ​​need to be paid attention to so that the response characteristics are in accordance with the design. which are desired. It should be noted that this research does not use a speed prediction model so it is assumed that the speed is known without any uncertainty.