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ÖgeEnergy-efficient velocity trajectory optimization using dynamic programming for electric vehicles( 2021-10-22)The electrification and autonomous systems developed in the automotive industry in the last decade bring different solutions. Many methods have been developed and still continue to be developed to reduce energy consumption in vehicles, especially with electrified, connected vehicle technologies and navigation systems. Speed trajectory optimization is part of these methods. The main motivation of speed trajectory optimization is to prevent excessive energy consumption due to driver driving style. In order to prevent this, information such as the slope and speed limit of the road to be traveled is used over the navigation system. When we consider only energy while optimizing the speed trajectory, the prolongation of the driving time will appear as a concern. Because if the vehicle goes faster, the energy consumed will increase quadratically. Therefore, optimization will always demand the vehicle to go slower in order to consume less energy and there must be a balance between energy and travel time. In this thesis, a study has been carried out that periodically updates the speed trajectory, which will ensure that the destination point and arrival time information are provided into the navigation system by the driver while consuming the least energy in the given time. The dynamic Programming (DP) method is used to solve this problem. Dynamic programming always presents the global optimum behavior under the given boundary conditions. The speed of the vehicle was used as the only state variable and its optimization was performed separately over the distance stages. The average speed required to reach the destination on time, based on the destination point and travel time information obtained from the navigation system, is given as an input to the optimization, and the DP state space is constantly updated. The main reason for this is to reduce the memory load required by DP. Thus, a fixed number of states are scanned. But the scanned range values are updated according to this speed input. A longitudinal vehicle model was used for optimization. The limits of the powertrain are also part of the optimization as a boundary condition. Before the optimization is run, a pre-calculation is also made to include the states where the transition between states is possible only in the optimization. Thus, it is aimed to shorten the calculation time by not including the unreachable situations in the optimization. Optimization takes place along a certain horizon. The speed trajectory calculated for this horizon is transmitted to the vehicle speed control unit as an input. The vehicle follows this speed profile. The optimization is updated again after a certain period of time and transmits the speed trajectory calculated for the next horizon to the vehicle. The purpose of this is if the vehicle cannot follow the given speed for any reason during real driving, the optimization is performed again based on the new conditions. This allows the vehicle to progress in real-time using the speed trajectory closest to the global optimum. In the study, simulation and analysis of the all-electric truck were carried out on two different slope routes. Tests were performed with different fixed velocity values and velocity profiles produced by velocity trajectory optimization in both routes. As a result of the simulations carried out, it has been observed that up to 4% of energy consumption and up to 2.5% of the targeted time are saved. Thanks to the proposed adaptive weight factor, it has been observed that the time-energy balance is maintained for different routes, arrival times, and vehicle parameters.