Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells

Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm

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. 

A novel hierarchical predictive energy management strategy for plug-in hybrid electric bus combined with deep deterministic policy gradient

Optimal energy management strategy of a novel hybrid dual-motor transmission system for electric vehicles

Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method

Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle

Self-supervised reinforcement learning-based energy management for a hybrid electric vehicle

Energy management of hybrid electric vehicles based on inverse reinforcement learning

Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem. These solutions include a monocular depth estimation method based on uncertainty analysis, which solves the problem in which a neural network has strong expressive ability but cannot evaluate the reliability of an output result. In addition, this paper proposes a photometric loss function based on the Retinex algorithm, which solves the problem of pulling around pixels due to the presence of moving objects. We objectively compare our method to current mainstream monocular depth estimation methods and obtain satisfactory results.

Chunyang Qi

Papers

Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle

Self-supervised reinforcement learning-based energy management for a hybrid electric vehicle

Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method

Energy management of hybrid electric vehicles based on inverse reinforcement learning

Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm.