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

Cycle-life model for graphite-LiFePO4 cells

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The fuel cell electric vehicles: The highlight review

Pramuanjaroenkij, A., & Kakaç, S. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.11.103 · Cited by 484

Prospects and impediments for hydrogen and fuel cell vehicles in the transport sector

Ajanovic, A., & Haas, R. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2020.03.122 · Cited by 371

Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus

Wu, Y., Tan, H., Peng, J., Zhang, H., & He, H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.04.021 · Cited by 337

Deep Reinforcement Learning-Based Energy Management for a Series Hybrid Electric Vehicle Enabled by History Cumulative Trip Information

Li, Y., He, H., Peng, J., & Wang, H. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2926472 · Cited by 213

Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving

Wu, J., Huang, Z., Hu, Z., & Lv, C. (2023) · Engineering · doi:10.1016/j.eng.2022.05.017 · Cited by 205

Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning

Tang, X., Zhou, H., Feng, W., et al. (2021) · Energy · doi:10.1016/j.energy.2021.121593 · Cited by 202

Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time

Pereira, D. F., Lopes, F. D. C., & Watanabe, E. H. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2979528 · Cited by 195

A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness

Jia, C., Zhou, J., He, H., Li, J., Wei, Z., Li, K., & Shi, M. (2023) · Energy · doi:10.1016/j.energy.2023.127105 · Cited by 155

Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy

Lu, D., Yi, F., Hu, D., Li, J., Yang, Q., & Wang, J. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121516 · Cited by 150

A noise-tolerant model parameterization method for lithium-ion battery management system

Wei, Z., Zhao, D., He, H., Cao, W., & Dong, G. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114932 · Cited by 127

Assessing the influence of urban transportation infrastructure construction on haze pollution in China: A case study of Beijing-Tianjin-Hebei region

Zhang, M., Liu, X., & Ding, Y. (2020) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2020.106547 · Cited by 126

Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm

Li, Q., Meng, X. Y., Gao, F., et al. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3113021 · Cited by 119

A Novel Energy Management Strategy Based on Dual Reward Function Q-learning for Fuel Cell Hybrid Electric Vehicle

Zhang, Y., Ma, R., Zhao, D., et al. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3062273 · Cited by 114

2018 IEEE Transportation Electrification Conference and Expo (ITEC)

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Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections

Liu, B., Sun, C., Wang, B., et al. (2022) · Energy · doi:10.1016/j.energy.2022.123956 · Cited by 111

Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle

Huang, Y., Hu, H., Tan, J., et al. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.116678 · Cited by 110

An Improved Energy Management Strategy for Hybrid Electric Vehicles Integrating Multistates of Vehicle-Traffic Information

He, H., Wang, Y., Li, J., et al. (2021) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3054896 · Cited by 101

Optimal power source sizing of fuel cell hybrid vehicles based on Pontryagin's minimum principle

Liu, C., & Liu, L. (2015) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2015.04.112 · Cited by 97

Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm

Jia, C., Li, K., He, H., Zhou, J., Li, J., & Wei, Z. (2023) · Energy · doi:10.1016/j.energy.2023.128462 · Cited by 85

Robust fuzzy model predictive control for energy management systems in fuel cell vehicles

Shen, D. W. C., Lim, C., & Shi, P. J. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104364 · Cited by 83

Urban traffic regulation and air pollution: A case study of urban motor vehicle restriction policy

Sun, C., Xu, S., Yang, M., & Gong, X. (2022) · Energy Policy · doi:10.1016/j.enpol.2022.112819 · Cited by 78

Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles

Yao, Y., Wang, J., Zhou, Z., et al. (2022) · Energy · doi:10.1016/j.energy.2022.125405 · Cited by 77

Lifespan-consciousness and minimum-consumption coupled energy management strategy for fuel cell hybrid vehicles via deep reinforcement learning

Huo, W., Chen, D., Tian, S., et al. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.05.194 · Cited by 74

Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning

Shi, W., Huangfu, Y., Xu, L., et al. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120234 · Cited by 73

A comparison of rule-based and model predictive controller-based power management strategies for fuel cell/battery hybrid vehicles considering degradation

Wang, Y., Advani, S. G., & Prasad, A. K. (2020) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2020.09.030 · Cited by 64

Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles

Hou, S., Yin, H. Y., Xu, F., Plá, B., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.126466 · Cited by 59

Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L., et al. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3028089 · Cited by 57

Quantification on degradation mechanisms of polymer exchange membrane fuel cell cathode catalyst layers during bus and stationary durability test protocols

Wang, J., Geng, J., Wang, M., Hu, X., Shao, Z., & Zhang, H. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230878 · Cited by 53

A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery health-aware control

He, H., Jia, C., & Li, J. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.04.297 · Cited by 49

Convex programming improved online power management in a range extended fuel cell electric truck

Wu, X., Hu, X., Yin, X., Peng, Y., & Pickert, V. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228642 · Cited by 43

Modular Energy Systems in Vehicular Applications

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L., et al. (2019) · Energy Procedia · doi:10.1016/j.egypro.2019.04.003 · Cited by 40

Multi-parameter optimization of stepwise distribution of parameters of gas diffusion layer and catalyst layer for PEMFC peak power density

Yu, R. J., Guo, H., Ye, F., et al. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119764 · Cited by 40

Battery-degradation-involved energy management strategy based on deep reinforcement learning for fuel cell/battery/ultracapacitor hybrid electric vehicle

Lu, H., Tao, F., Fu, Z., et al. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109235 · Cited by 37

The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus

Zhang, K., Ruan, J., Li, T., Cui, H., & Wu, C. (2023) · Energy · doi:10.1016/j.energy.2023.126760 · Cited by 36

Comparison of Decentralized ADMM Optimization Algorithms for Power Allocation in Modular Fuel Cell Vehicles

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L., et al. (2021) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3105950 · Cited by 35

Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle

Lin, X., Xu, X., & Wang, Z. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119378 · Cited by 32

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L., et al. (2021) · SAE International Journal of Electrified Vehicles · doi:10.4271/14-11-02-0012 · Cited by 22

A novel energy efficiency improvement framework based on data-driven learning and energy online decoupling for fuel cell hybrid buses

Yu, X. Q., Lin, C. W., Xie, P., et al. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117153 · Cited by 12

Guided Control for Plug-In Fuel Cell Hybrid Electric Vehicles Via Vehicle to Traffic CommunicationPreprint

Wei, X., Liu, B., Sun, C., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4212041 · Cited by 7

Bidirectional DC-DC Converter Topologies for Hybrid Energy Storage Systems in Electric Vehicles: A Comprehensive Review

Tong, Y., Salhi, I., Wang, Q., Lu, G., & Wu, S. (2025) · Energies · doi:10.3390/en18092312 · Cited by 60

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Jiang, Z., & Song, G. (2022) · International Conference on Computing, Robotics and System Sciences (ICRSS) · doi:10.1109/icrss57469.2022.00031 · Cited by 17

Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment

Chen, D., Zhu, Y., Liu, M., et al. (2022) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3533271.3561761 · Cited by 2

Classification without gradients: multi-agent reinforcement learning approach to optimization (Conference Presentation)

Morcos, A., Man, H., Maguire, B., et al. (2023) · doi:10.1117/12.2664025

Performance of distributed multi-agent multi-state reinforcement spectrum management using different exploration schemes

Ko, A. H. R., Sabourin, R., & Gagnon, F. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.035 · Cited by 9

Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning

Wu, X., Wedernikow, E., Nitsche, C., & Huber, M. F. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186787 · Cited by 6