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

Beyond energy efficiency: A battery-aware reward design for deep reinforcement learning-based energy management in electric vehicles with hybrid storage

Raeesi, M., Chavoshnia, P., Mansouri, S., & Mollajafari, M. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123376 · Cited by 1

Low-temperature energy management for fuel cell buses: A DRL approach integrating powertrain electro-thermal-aging characteristic and cabin thermal comfort

Gao, S., Ju, F., Wang, Q., Zong, Y., Zhang, C., Zhao, Z., Dai, Z., & Wang, L. (2026) · Energy · doi:10.1016/j.energy.2026.141849 · Cited by 1

Adaptive-weight model predictive control for online energy management of fuel cell heavy-duty trucks based on the soft actor-critic algorithm

Xiao, X., Gu, Y., Yuan, H., Zhu, S., Bai, S., Li, G., & Duan, H. (2026) · Energy · doi:10.1016/j.energy.2026.142119 · Cited by 1

Degradation-aware multi-objective optimization and adaptive energy management for fuel cell hybrid electric trucks

Shi, D., Yan, W., Wang, J., Liu, K., Ren, C., Wu, Z. Q., & Liu, X. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.157053 · Cited by 1

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

Yang, T., Li, P., Li, Q., Zhang, S., & Li, Z. (2024) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070241279486 · Cited by 1

A novel EMS for FCV combined with condition-adaptive search ranges of decision variables based on the spatial and temporal traffic characteristics

Wu, H., Min, H., Zhao, H., & Sun, W. (2025) · Energy · doi:10.1016/j.energy.2025.138118 · Cited by 1

A Novel Method for Estimating Soh of Batteries with Multi-Level Fusion FeaturesPreprint

Zhou, J., Zhang, J., Zhang, J., Liu, C., Yi, F., Jiao, Z., & Wu, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4775382

Analysis of Energy Efficiency Parameters of a Hybrid Vehicle Powered by Fuel with a Liquid Catalyst

Osipowicz, T. K., Gołębiewski, W., Lewicki, W., Koniuszy, A., ABRAMEK, K. F., Prajwowski, K., KLYUS, O., & Gałdyński, D. (2024) · Energies · doi:10.3390/en17205138

Self start stop control decision of milling system based on ICS platform

Huang, Y., Cai, J., Li, J., Gao, L., Chang, P., Lv, Y., Liu, H., Ma, Q., & Cao, F. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778757

Two-Stage Multi-Objective Optimal Planning of Hybrid AC/DC Microgrid by Using ϵ-Constraint Method

Mahmoudian, A., & Lu, J. (2025) · Energies · doi:10.3390/en18081905

Eco-Cooperative Planning and Control of Connected Autonomous Vehicles Considering Energy Consumption Characteristics

Pan, C., Pi, J., & Wang, J. (2025) · Electronics · doi:10.3390/electronics14081646

Using A novel noisy deep reinforcement learning approach for detecting the cyberattacks in stored hydrogen fuel based AC-DC networks

Oveysikian, A., Abed, A. M., Davatgar, S., Mosallaei, B., Basem, A., Ali, S. H., & Ahmed, M. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.04.274

Adaptive Energy Optimization Based on Fuel Cell Degradation Prediction for Fuel Cell Hybrid Electric Vehicles

Wang, Y., Hou, S., Jia, Z., Gao, J., Hao, D., & Chen, H. (2026) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-026-00873-x

Model Predictive Energy Management for Open Cathode Fuel Cell and Battery Hybrid Power System Considering Effects of Varied Ambient Conditions in Unmanned Aerial Vehicle ApplicationsPreprint

Xiao, C., Wang, C., Wang, B., Yan, Y., & Cao, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5342579

Joint Optimization of Configuration Design and Energy Management Strategy for a Fuel Cell/Supercapacitor Rubber Tyre Gantry CranePreprint

Wang, P., Dou, J., Ni, Z., Jiang, Z., & Guan, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5351294

Mission profiles and energy management strategies for electric trucks with fuel cell range extenders

Cruz, I. W. S. L., Buck, L., Wyszynski, T., Schramm, M., & Frerichs, L. (2025) · Transportation Engineering · doi:10.1016/j.treng.2025.100356

An Energy Management Strategy for a Fuel Cell-Battery Power System Targeting the Fuel Cell Anode Flooding

Zhang, T., Hu, X. H., Guo, D., Yu, D., Zhang, X., Zhao, F., & Ma, C. (2025) · International Symposium on New Energy Technologies and Power Systems (NETPS) · doi:10.1109/netps65392.2025.11102032

Investigation of dynamic start-up characteristics in thermal management system for heavy-duty fuel cell trucks under high/low temperature conditions

Li, C., Qin, B., Shen, Y., Kuang, J., & Fu, J. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.128070

An Adaptive Exploration-Oriented Multi-Agent Co-Evolutionary Method Based on MATD3

Wang, S., Lyu, Z., Yue, Q., Shang, Q., Ke, Y., & Gao, F. (2025) · Electronics · doi:10.3390/electronics14214181

Deep reinforcement learning based energy management for hybrid electric buses under uncertain operational states

Lu, C., & Renjing, G. A. O. (2025) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070251380121

Research on the configuration design and hierarchical energy management for dual-stack fuel cell electric bus

Yang, K., Chu, E., Wang, J., Chu, Y., Zhang, B., & Wang, W. C. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.153144

Intelligent Control System of HVAC Using Deep Reinforcement Learning Algorithm

Fan, C. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.03.374

Joint Optimization of Configuration Design and Energy Management Strategy for a Fuel Cell/Supercapacitor Rubber Tire Gantry Crane

Wang, P. H., Dou, J., Yin, P., Ni, Z., Jiang, Z., & Zhao, D. (2026) · Electronics · doi:10.3390/electronics15132794

A Review of Energy Management Strategies for Hybrid Electric Vehicles

Gao, S., Zhou, B., Fu, Z., Jiang, Z., Zhang, K., Ma, Q., Dong, M., Tao, F., Si, P., & Jin, X. (2026) · Energy Science & Engineering · doi:10.1002/ese3.70599

Continuous fuel cell vehicle-to-grid (FCV2G) operation strategy for grid-interactive energy systems: Scenario-based assessment of potential durability benefits beyond conventional driving

Shin, D., Kweon, D. H., Kwon, J., Lim, K. H., Seong, J. G., Ryu, H., Park, H., Na, Y., & Jang, J. H. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.240838

A novel hybrid intelligent energy management framework based on swarm optimization and deep reinforcement learning for fuel cell hybrid buses

Zhou, Y., Ji, J., Hou, J., Lv, S., & Wang, X. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.156970

Research on the Attenuation Effect of Isolation Walls on Overpressure from Hydrogen Explosion

Gui, G., Wang, G., Xv, S., Kang, Z., Wang, J., & Wang, C. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-92-1110-4_4

Energy Management Strategy for a Hybrid Commercial Vehicle Considering Road Gradient

Lin, C., Gong, J., Tu, J., Chang, C., Xue, S., & Yang, W. (2026) · Energies · doi:10.3390/en19174024

Evolution of multi-algorithm fusion strategies in energy management for hybrid electric vehicles: Synergistic advancement of intelligent and connected technologies

Liu, D., Shen, Z., Wang, Y., Ji, Z., & Ma, C. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117474

Decoupling engine operation from transient loads: a Mamba-based predictive reinforcement learning framework for quasi-steady energy management in gasoline compression ignition hybrid powertrains

Zhao, M., Wang, H., Zheng, Z., & Yao, M. (2026) · Energy Conversion and Management · doi:10.1016/j.enconman.2026.122118

Artificial intelligence-enabled energy optimization in electric and hybrid vehicles: towards sustainable and smart transportation systems

Kiros, M., Yeneneh, K., & Sufe, G. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128796

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