Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information

An overview: Current progress on hydrogen fuel cell vehicles

Aminudin, M. A., Kamarudin, S. K., Lim, B. H., Majilan, E. H., Masdar, M. S., & Shaari, N. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.10.156 · Cited by 813

A lumped-parameter electro-thermal model for cylindrical batteries

Lin, X., Perez, H. E., Mohan, S., Siegel, J. B., Stefanopoulou, A. G., Ding, Y., & Castanier, M. P. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.01.097 · Cited by 677

Comparison study on the battery models used for the energy management of batteries in electric vehicles

He, H., Xiong, R., Guo, H., & Li, S. (2012) · Energy Conversion and Management · doi:10.1016/j.enconman.2012.04.014 · Cited by 464

Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles

Ebbesen, S., Elbert, P., & Guzzella, L. (2012) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2012.2203836 · Cited by 331

Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Wei, Z., Quan, Z., Wu, J., Li, Y., Pou, J., & Zhong, H. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3070514 · Cited by 243

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

Real-time estimation of battery internal temperature based on a simplified thermoelectric model

Zhang, C., Li, K., & Deng, J. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.10.052 · Cited by 181

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Khayyam, H., & Bab‐Hadiashar, A. (2014) · Energy · doi:10.1016/j.energy.2014.03.020 · Cited by 181

Energy sources durability energy management for fuel cell hybrid electric bus based on deep reinforcement learning considering future terrain information

Li, K., Zhou, J., Jia, C., Yi, F., & Zhang, C. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.05.311 · Cited by 180

Dynamic programming technique for optimizing fuel cell hybrid vehicles

Fares, D. A., Chedid, R. B., Panik, F., Karaki, S. H., & Jabr, R. A. (2015) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2014.12.120 · Cited by 170

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

Prospects and impediments for hydrogen fuel cell buses

Ajanović, A., Glatt, A., & Haas, R. (2021) · Energy · doi:10.1016/j.energy.2021.121340 · Cited by 141

Longevity-aware energy management for fuel cell hybrid electric bus based on a novel proximal policy optimization deep reinforcement learning framework

Huang, R., He, H., Zhao, X., & Gao, M. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.232717 · Cited by 129

Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems

Hu, H., Yuan, W., Su, M., & Ou, K. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117288 · Cited by 116

Energy management strategy for fuel cell vehicles via soft actor-critic-based deep reinforcement learning considering powertrain thermal and durability characteristics

Zhang, Y., Zhang, C., Fan, R., Deng, C., Wan, S., & Chaoui, H. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.116921 · Cited by 100

Twin delayed deep deterministic policy gradient-based deep reinforcement learning for energy management of fuel cell vehicle integrating durability information of powertrain

Zhang, Y., Zhang, C., Fan, R., Huang, S., Yang, Y. J., & Xu, Q. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.116454 · Cited by 95

Adaptive ECMS based on speed forecasting for the control of a heavy-duty fuel cell vehicle for real-world driving

Piras, M., Bellis, V. D., Malfi, E., Novella, R., & Lopez-Juarez, M. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117178 · Cited by 81

Deep Reinforcement Learning Based PHEV Energy Management With Co-Recognition for Traffic Condition and Driving Style

Cui, N., Cui, W., & Shi, Y. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3235110 · Cited by 75

A review of accelerated stress tests dedicated to proton exchange membrane fuel cells – Part I: Fuel cell component level

Pahon, E., Hissel, D., & Yousfi-Steiner, N. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231895 · Cited by 69

Real-Time Energy Management Strategy of a Fuel Cell Electric Vehicle With Global Optimal Learning

Hou, S., Yin, H. Y., Plá, B., Gao, J., & Chen, H. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3238101 · Cited by 68

Energy management strategy of a novel parallel electric-hydraulic hybrid electric vehicle based on deep reinforcement learning and entropy evaluation

Zhang, Z., Zhang, T., Hong, J., Zhang, H., & Yang, J. L. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136800 · Cited by 61

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

Delamination evolution of PEM fuel cell membrane/CL interface under asymmetric RH cycling and CL crack location

Ma, S., Qin, Y., Liu, Y., Sun, L., Guo, Q., & Yin, Y. S. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118551 · Cited by 56

Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning

Zhou, J., Liu, J., Xue, Y., & Liao, Y. (2021) · Energy · doi:10.1016/j.energy.2021.121866 · Cited by 51

Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach

Estrada, P. M., Lima, D. D., Bauer, P. H., Mammetti, M., & Bruno, J. C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120231 · Cited by 50

An Energy Management Strategy Considering the Economy and Lifetime of Multistack Fuel Cell Hybrid System

Li, Q., Cai, L., Yin, L., Wang, T., Li, L., Xie, S., & Chen, W. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3218505 · Cited by 43

Strength, weakness, opportunities, and threats (SWOT) analysis of fuel cells in electric vehicles

Olabi, A. G., Abdelkareem, M. A., Wilberforce, T., Alami, A. H., Alkhalidi, A. A. T., Hassan, M. M., & Sayed, E. T. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.02.090 · Cited by 42

Power Management for Hybrid Energy Storage System of Electric Vehicles Considering Inaccurate Terrain Information

Zhang, Q., Ju, F., Zhang, S., Deng, W., Wu, J., & Gao, C. (2017) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2645780 · Cited by 40

2010 IEEE Vehicle Power and Propulsion Conference

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Multi-objective reinforcement learning-based energy management for fuel cell vehicles considering lifecycle costs

Wu, J., Song, D., Zhang, X. M., Duan, C. S., & Yang, D. P. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.06.145 · Cited by 38

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

Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication

Wei, X., Wang, J., Sun, C., Liu, B., Huo, W., & Sun, F. (2022) · Energy · doi:10.1016/j.energy.2022.126469 · Cited by 36

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

Reinforcement Learning Based on Energy Management Strategy for HEVs

Inuzuka, S., Xu, F., Zhang, B., & Shen, T. (2019) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc46532.2019.8952511 · Cited by 22

Data-driven bi-level predictive energy management strategy for fuel cell buses with algorithmics fusion

Li, M., Liu, H., Yan, M., Wu, J., Jin, L., & He, H. (2023) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2023.100414 · Cited by 15

The Hydrogen Fuel Cell Battery: Replacing the Combustion Engine in Heavy Vehicles

Kendall, K., Ye, S., & Liu, Z. (2022) · Engineering · doi:10.1016/j.eng.2022.11.007 · Cited by 14

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

Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management

Safari, A., Daneshvar, M., & Anvari‐Moghaddam, A. (2024) · Applied Sciences · doi:10.3390/app142311112 · Cited by 176

Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control

Jia, C., Liu, W., He, H., & Chau, K. T. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119032 · Cited by 125

Comprehensive optimization of fuzzy logic-based energy management system for fuel-cell hybrid electric vehicle using genetic algorithm

Mazouzi, A., Hadroug, N., Alayed, W. M., Hafaifa, A., Iratni, A., & Kouzou, A. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.07.237 · Cited by 122

Health-conscious energy management for fuel cell vehicles: An integrated thermal management strategy for cabin and energy source systems

Jia, C., Liu, W., He, H., & Chau, K. T. (2025) · Energy · doi:10.1016/j.energy.2025.137330 · Cited by 100

Modeling and temperature control of a water-cooled PEMFC system using intelligent algorithms

Chen, J., He, P., Cai, S.-J., He, Z.-H., Zhu, H.-N., Yu, Z., Yang, L.-Z., & Tao, W. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123790 · Cited by 92

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

Jia, C., He, H., Zhou, J., Li, J., Wei, Z., & Li, K. (2023) · Energy · doi:10.1016/j.energy.2023.128928 · Cited by 25

Energy sources durability energy management for fuel cell hybrid electric bus based on deep reinforcement learning considering future terrain information

Li, K., Zhou, J., Jia, C., Yi, F., & Zhang, C. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.05.311 · Cited by 180

A novel data-driven energy management strategy for fuel cell hybrid electric bus based on improved twin delayed deep deterministic policy gradient algorithm

Huang, R., & He, H. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.04.335 · Cited by 55

Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control

Jia, C., Liu, W., He, H., & Chau, K. T. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119032 · Cited by 125

A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms

Li, K., Jia, C., Han, X., & He, H. (2023) · Sustainability · doi:10.3390/su15107967 · Cited by 13