Fuel consumption and emissions of a vehicle are greatly affected by roadway grades or slopes and driving behavior. Therefore, it is necessary to develop optimal driving systems on hilly roads. This work proposes an ecological (eco) driving system (EDS) using the model predictive control (MPC) mechanism that generates the optimal speed trajectory for a vehicle given road slope profile ahead. Based on the factors influencing fuel consumption of a vehicle, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function. In addition, the weight parameter of the objective function is tuned by means of fuzzy inference techniques utilizing the current speed and road slope. We conduct microscopic traffic simulation to evaluate the performance of the proposed EDS considering free flow of vehicles on a hilly road with typical shapes of slopes. The fuel consumptions estimated from simulation results show that the EDS using MPC and fuzzytuned MPC outperform the traditional human driving system by 5.7% and 6.4%, respectively for up-down slope and 7.2% and 8.24%, respectively for down-up slope. Moreover, the proposed EDS reduces the CO2 emissions significantly.
|Title of host publication||2018 Joint 7th International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Number of pages||5|
|Publication status||Published - 2019|
|Event||Joint International Conference on Informatics, Electronics and Vision and International Conference on Imaging, Vision and Pattern Recognition 2018 - Kitakyushu, Japan|
Duration: 25 Jun 2018 → 28 Jun 2018
Conference number: 7th/2nd
|Conference||Joint International Conference on Informatics, Electronics and Vision and International Conference on Imaging, Vision and Pattern Recognition 2018|
|Abbreviated title||ICIEV/IVPR 2018|
|Period||25/06/18 → 28/06/18|
- Fuzzy logic.
- Hilly roads
- Model predictive control
- Nonlinear optimization