Multi-level deceleration planning based on reinforcement learning algorithm for autonomous regenerative braking of EV

Abstract

A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking.