A novel hierarchical predictive energy management strategy for plug-in hybrid electric bus combined with deep deterministic policy gradient. (1st August 2022)

• Record Type: Journal Article
• Authors: He, Hongwen; Huang, Ruchen; Meng, Xiangfei; Zhao, Xuyang; Wang, Yong; Li, Menglin
• Is Part Of: Journal of energy storage. Volume 52:Part A(2022)
• Publication Date: 2022-08-01
• Subjects: Plug-in hybrid electric bus -- Energy management -- SOC reference planning -- Model predictive control -- Deep deterministic policy gradient (DDPG); Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.104787
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Energy management is a crucial technology to improve the energy economy of the plug-in hybrid electric bus (PHEB). This article proposes a novel hierarchical predictive energy management strategy combined with the deep deterministic policy gradient (DDPG) algorithm for superior energy economy performance and fast state of charge ( SOC ) reference planning of PHEB. In the upper layer, real velocity data collected from a fixed bus route are used to train the DDPG algorithm and the well-trained neural networks are extracted to plan the SOC reference trajectory efficiently before departure. In the lower layer, deep neural network (DNN) is employed to predict the velocity in a short term and a model predictive control (MPC) optimal controller is designed to allocate power flows optimally by tracking the SOC reference trajectory accurately. Simulation results show that the proposed strategy with high-efficiency SOC reference planning improves the energy economy by 4.32% compared with DDPG, and the energy economy achieves 98.61% of the global optimal algorithm. More importantly, the robustness and adaptiveness are validated in the case of imprecise velocity prediction and inaccurate pre-known driving cycles. This article contributes to the energy economy improvement for PHEBs through MPC and DDPG methods. Highlights: A hierarchical MPC-based energy management strategy combined with DDPG is proposed. DDPG is adopted to plan the SOC reference trajectory adaptively and quickly. Real-world velocity data are utilized as the training and testing datasets. Energy economy performance is improved in comparison with DDPG. Robustness and adaptiveness are verified with imprecise prediction and acquisition.