DQN regenerative braking control strategy based on adaptive weight coefficients

Abstract

Aiming at the problems existing in regenerative braking control strategy based on Q-learning which include the dimensional disaster of state and action variables discretization and the return function weight coefficient determined empirically. This paper proposes deep Q-learning network (DQN) regenerative braking control strategy based on adaptive weight coefficients. Firstly, braking performance evaluation indexes are determined which are braking energy recovery efficiency and braking stability coefficient. Then, the state and action variables and return function are constructed respectively. Therein the braking demand power and state of charge ( SOC) are taken as state variables, braking torque proportional coefficient, and weight coefficients are taken as action variables. And return function is formulated by trading off braking energy recovery efficiency and braking stability. Finally, using the MATLAB/Simulink software, the simulation model of real working condition in Yubei district of Chongqing is established. The simulation results show that braking recovery efficiency of the proposed strategy is 7.4% higher than that of Q-learning strategy, and the average braking stability coefficient is decreased by 0.08. The results indicate the proposed strategy can better balance between braking energy recovery efficiency and braking stability than the conventional strategy.