Optimal eco-driving control of hybrid electric vehicles through signalized intersections

Mobility has always played a crucial role in the cycle of human development. In almost every era, humans have attempted to find the means to transport people over long distances at the highest possible speed. It took the development of reliable internal combustion engines operated on liquid fuels to turn the vision of a self-propelling "automobile" into reality (combination of Greek: autos = self and Latin: mobilis = mobile). This thesis focuses on the problem of speed planning for hybrid vehicles that connected to traffic lights. The uncertainty of the timing of the traffic lights at intersections on the road is considered. The economic driving problem has formulated as a strong data-bound optimization problem. Effective red-light duration (ERD), formulated as a random variable, to describe the feasible passing time through signalized intersections. A chance constraint appended to the optimal control problem to incorporate robustness with respect to uncertain signal timing. Moreover, describes the possible passage time through intersections with signals. Typically, the true probability distribution of an ERD is unknown. Dynamic programming (DP) used to solve the optimization problem. The system relies heavily on artificial intelligence to solve this problem. It will work on its own on calculations and estimate the time to pass through intersections at green light time, making sure to reach the highest degree of fuel economy. Finally, after the simulation, it expected to generate reference paths for the optimum speed with a lower fuel consumption of 50-60%.