Basic Idea of Quadrant Dynamic Programming for Adaptive Cruise Control to Create Energy Efficient Velocity Trajectory of Electric Vehicle

Previous studies proposed various optimization algorithms such as gradient method and model predictive control (MPC) to reduce the energy consumption of vehicles with adaptive cruise control. Reducing energy consumption is achieved by optimal velocity control and reducing energy loss. We propose an approach based on dynamic programming (DP). DP is a feedback control with a calculated table of inputs. Autonomous driving trains widely use this method for reducing energy consumption. We created an algorithm, quadrant dynamic programming (QDP), to calculate optimal velocity trajectory. We divided the table into quadrants and seamlessly connected them. With this algorithm, we managed to support many situations even though the table is two-dimension. The result of the simulation and bench tests with an actual vehicle support the fact that the algorithm is valid.