Kun Xu

TL;DR: A deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules in HEVs, and the online learning architecture is proved to be effective.

TL;DR: In this article, a fuzzy-logic-based regenerative braking strategy (RBS) was proposed to improve the energy recuperation efficiency of electric vehicles by using the driver's braking force command, vehicle speed, battery SOC, battery temperature, and vehicle acceleration to determine the distribution between friction braking force and regenerative force.

TL;DR: The proposed model under different drive cycles show remarkable advancement in state of charge estimation with high potential to overcome the drawbacks in traditional methods and therefore provides an alternative approach in stateof charge estimation.

TL;DR: To maintain the stability and to improve the performance of the regenerative braking in unknown tire-road conditions, a knowledge-based methodology in a hierarchical control structure is proposed, where the maximum adhesion force and the motor reference torque (MRT) are determined online.

TL;DR: In this article, an energy management approach for hybrid vehicles is proposed, which optimizes the vehicle velocity profile while minimizing the fuel consumption with the help of the traffic preview information, so that a further energy saving for hybrid cars can be achieved.