A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience

[1]  Jianwei Li,et al.  Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy , 2023, Applied Energy.

[2]  Peng Xie,et al.  A novel energy efficiency improvement framework based on data-driven learning and energy online decoupling for fuel cell hybrid buses , 2023, Energy Conversion and Management.

[3]  Hongwen He,et al.  Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm , 2023, Energy.

[4]  Hongwen He,et al.  A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness , 2023, Energy.

[5]  Jinwu Gao,et al.  Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles , 2023, Energy.

[6]  Dongji Xuan,et al.  Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle , 2023, Energy Conversion and Management.

[7]  Jiageng Ruan,et al.  The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus , 2023, Energy.

[8]  S. Kakaç,et al.  The fuel cell electric vehicles: The highlight review , 2022, International Journal of Hydrogen Energy.

[9]  S. Pang,et al.  Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning , 2022, Applied Energy.

[10]  Fengchun Sun,et al.  Guided Control for Plug-In Fuel Cell Hybrid Electric Vehicles Via Vehicle to Traffic Communication , 2022, SSRN Electronic Journal.

[11]  Hang Guo,et al.  Multi-parameter optimization of stepwise distribution of parameters of gas diffusion layer and catalyst layer for PEMFC peak power density , 2022, Applied Energy.

[12]  Xiaosong Hu,et al.  Comparison of Decentralized ADMM Optimization Algorithms for Power Allocation in Modular Fuel Cell Vehicles , 2022, IEEE/ASME Transactions on Mechatronics.

[13]  Qi Li,et al.  Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm , 2022, IEEE Transactions on Industrial Electronics.

[14]  Xinhao Xu,et al.  Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle , 2022, Applied Energy.

[15]  Zhiyu Huang,et al.  Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving , 2022, Engineering.

[16]  W. Huo,et al.  Lifespan-consciousness and minimum-consumption coupled energy management strategy for fuel cell hybrid vehicles via deep reinforcement learning , 2022, International Journal of Hydrogen Energy.

[17]  Hongwen He,et al.  A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery health-aware control , 2022, International Journal of Hydrogen Energy.

[18]  Chuanwang Sun,et al.  Urban traffic regulation and air pollution: A case study of urban motor vehicle restriction policy , 2022, Energy Policy.

[19]  Fengchun Sun,et al.  Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections , 2022, Energy.

[20]  Weiguo Liu,et al.  A Novel Energy Management Strategy Based on Dual Reward Function Q-learning for Fuel Cell Hybrid Electric Vehicle , 2022, IEEE Transactions on Industrial Electronics.

[21]  Hongjie Zhang,et al.  Quantification on degradation mechanisms of polymer exchange membrane fuel cell cathode catalyst layers during bus and stationary durability test protocols , 2022, Journal of Power Sources.

[22]  Xiaosong Hu,et al.  A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach , 2021, SAE International Journal of Electrified Vehicles.

[23]  Edson H. Watanabe,et al.  Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time , 2021, IEEE Transactions on Industrial Electronics.

[24]  M. Zhang,et al.  Assessing the influence of urban transportation infrastructure construction on haze pollution in China: A case study of Beijing-Tianjin-Hebei region , 2021 .

[25]  Hongwen He,et al.  An Improved Energy Management Strategy for Hybrid Electric Vehicles Integrating Multistates of Vehicle-Traffic Information , 2021, IEEE Transactions on Transportation Electrification.

[26]  Arash Khalatbarisoltani,et al.  Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications , 2020, IEEE Transactions on Vehicular Technology.

[27]  Suresh G. Advani,et al.  A comparison of rule-based and model predictive controller-based power management strategies for fuel cell/battery hybrid vehicles considering degradation , 2020 .

[28]  Xiaosong Hu,et al.  Convex programming improved online power management in a range extended fuel cell electric truck , 2020 .

[29]  Guangzhong Dong,et al.  A noise-tolerant model parameterization method for lithium-ion battery management system , 2020, Applied Energy.

[30]  Cheng-Chew Lim,et al.  Robust fuzzy model predictive control for energy management systems in fuel cell vehicles , 2020 .

[31]  Amela Ajanovic,et al.  Prospects and impediments for hydrogen and fuel cell vehicles in the transport sector , 2020, International Journal of Hydrogen Energy.

[32]  Hongwen He,et al.  Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus , 2019, Applied Energy.

[33]  Hongwen He,et al.  Deep Reinforcement Learning-Based Energy Management for a Series Hybrid Electric Vehicle Enabled by History Cumulative Trip Information , 2019, IEEE Transactions on Vehicular Technology.

[34]  Mehdi Karbalaye Zadeh,et al.  An Intelligent Power and Energy Management System for Fuel Cell/Battery Hybrid Electric Vehicle Using Reinforcement Learning , 2019, 2019 IEEE Transportation Electrification Conference and Expo (ITEC).

[35]  Loic Boulon,et al.  Modular Energy Systems in Vehicular Applications , 2019, Energy Procedia.

[36]  Lin Liu,et al.  Optimal power source sizing of fuel cell hybrid vehicles based on Pontryagin's minimum principle , 2015 .

[37]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[38]  Fazhan Tao,et al.  Battery-degradation-involved energy management strategy based on deep reinforcement learning for fuel cell/battery/ultracapacitor hybrid electric vehicle , 2023, Electric Power Systems Research.

[39]  Weida Wang,et al.  Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning , 2022 .

[40]  Hang Li,et al.  Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles , 2022, Energy.