Deep Deterministic Policy Gradient Based Energy Management Strategy for Hybrid Electric Tracked Vehicle With Online Updating Mechanism

• Main Authors: Zhikai Ma, Qian Huo, Tao Zhang, Jianjun Hao, Wei Wang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Energy management; hybrid electric tracked vehicle; online updating mechanism; deep deterministic policy gradient
• Online Access: https://ieeexplore.ieee.org/document/9312596/

In this paper, an online energy management strategy (EMS) for hybrid electric tracked vehicle (HETV) is developed based on deep deterministic policy gradient (DDPG) with time-varying weighting factor to further improve economic performance of HETV and reduce computational burden. The DDPG is applied to model the EMS problem for the target HETV. Especially, a time-varying weighting factor is introduced here to update old network parameters with experience learned from most recent cycle segment. Afterwards, simulation is conducted to verify the effectiveness and adaptability of the proposed method. Results show that DDPG-based EMS with online updating mechanism can achieve nearly 90% fuel economy performance as that of dynamic programming while computational time is greatly reduced. Finally, hardware-in-loop experiment is carried out to evaluate the real-world performance of the proposed method.