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

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

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

Reinforcement learning based energy management for fuel cell hybrid electric vehicles: A comprehensive review on decision process reformulation and strategy implementation

Li, J., Liu, J., Yang, Q., Wang, T., He, H., Wang, H., & Sun, F. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115450 · Cited by 47

Comprehensive Review of Hydrogen Leakage in Relation to Fuel Cell Vehicles and Hydrogen Refueling Stations: Status, Challenges, and Future Prospects

Hu, D., Gao, P., Cheng, Z., Shen, Y., He, R., Yi, F., Lu, M., Wang, J., & Liu, S. (2024) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c04557 · Cited by 45

A HO-BiGRU-Transformer based PEMFC degradation prediction method under different current conditions

Quan, R., Gong, C., Guan, X., Zhang, G., & Quan, J. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.124132 · Cited by 42

Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

Ma, L., Tian, J., Zhang, T., Guo, Q., & Chung, C. Y. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.10.001 · Cited by 41

Enhancing fuel cell electric vehicle efficiency with TIP-EMS: A trainable integrated predictive energy management approach

Wu, J., Peng, J., Li, M., & Wu, Y. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.118499 · Cited by 40

Interval prediction of fuel cell degradation based on voltage signal frequency characteristics with TimesNet-GPR under dynamic conditions

Zhu, W., Li, Y., Xu, Y., Zhang, L., Guo, B., Xiong, R., & Xie, C. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.144503 · Cited by 39

Deep learning estimation of state of health for lithium-ion batteries using multi-level fusion features of discharge curves

Zhou, J., Rong, J., Zhang, J., Liu, C., Yi, F., Jiao, Z., & Zhang, C. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.237781 · Cited by 34

An improved soft actor-critic-based energy management strategy of heavy-duty hybrid electric vehicles with dual-engine system

Zhang, D., Sun, W., Zou, Y., Zhang, X., & Zhang, Y. (2024) · Energy · doi:10.1016/j.energy.2024.132938 · Cited by 33

Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system

Song, D., Wu, Q., Zeng, X., Zhang, X., Qian, Q., & Yang, D. (2024) · Energy · doi:10.1016/j.energy.2024.132347 · Cited by 31

Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles

Zhao, Y., Huang, S., Wang, X., Shi, J., & Yao, S. (2024) · Energy · doi:10.1016/j.energy.2024.133395 · Cited by 31

Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Tang, T., Peng, Q., Shi, Q., Peng, Q., Zhao, J. C., Chen, C., & Wang, G. (2024) · Energy · doi:10.1016/j.energy.2024.133313 · Cited by 30

Optimal flexible power allocation energy management strategy for hybrid energy storage system with genetic algorithm based model predictive control

Ma, B., & Li, P.-H. (2025) · Energy · doi:10.1016/j.energy.2025.135958 · Cited by 30

Experimental validation of a predictive energy management strategy for agricultural fuel cell electric tractors

Varlese, C., Ferrara, A., Hametner, C., & Hofmann, P. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.06.097 · Cited by 30

Digital Twin-Based Hydrogen Refueling Station (HRS) Safety Model: CNN-Based Decision-Making and 3D Simulation

An, N. H., Yang, J. H., Song, E., Hwang, S.-H., Byun, H., & Park, S. (2024) · Sustainability · doi:10.3390/su16219482 · Cited by 31

An Energy Management Strategy for Multistack Fuel Cell Hybrid Locomotives With Integrated Optimization of System Service Life

Li, Q., Tan, H., Wang, T., Li, X., & Chen, W. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3491107 · Cited by 29

Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles

Rostami, S. M. R., Al-Shibaany, Z. Y. A., Kay, P., & Karimi, H. R. (2024) · Scientific Reports · doi:10.1038/s41598-024-81769-1 · Cited by 29

Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage

Toquica, D., Agbossou, K., & Henao, N. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.01.413 · Cited by 29

Knowledge-Guided Deep Reinforcement Learning for Multiobjective Energy Management of Fuel Cell Electric Vehicles

Li, X., He, H., & Wu, J. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3421342 · Cited by 25

Comprehensive experimental analysis of performance parameters and inductive process to determinate dynamic voltage characteristics for proton exchange membrane fuel cell

Huang, L., Zhang, X., Jiang, Y. H., Tang, S. W., Liao, H., Huang, R., & Dong, S. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119426 · Cited by 22

A review on the powertrains and energy management strategies of electric tractors

Yu, Q., He, X., Liu, L. D., Tan, Y., Jiang, Z., Pan, X., Zhang, M., Zhang, Z., Xie, B., Wen, C., et al. (2024) · The Innovation Energy · doi:10.59717/j.xinn-energy.2024.100064 · Cited by 21

Integrated optimization of energy management and thermal control strategies for fuel-cell heavy truck

Ju, F., Jiang, Y., Zhuang, W., Li, B., & Dai, J. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.05.388 · Cited by 21

A Lyapunov-based adaptive control strategy with fault-tolerant objectives for proton exchange membrane fuel cell air supply systems

Meng, J., Guo, Q., Yue, M., & Diallo, D. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124275 · Cited by 20

Safety Management Technologies for Wireless Electric Vehicle Charging Systems: A Review

Niu, S., Jia, Q., Yang, H., Yang, C., & Jian, L. (2025) · Electronics · doi:10.3390/electronics14122380 · Cited by 24

A Machine Learning Approach to Understanding Sociodemographic Factors in Electric Vehicle Ownership in the U.S.

Sadeghvaziri, E., Javid, R., Omidi, H., & Arafat, M. (2024) · Sustainability · doi:10.3390/su162310202 · Cited by 19

A real-time prediction method for PEMFC life under actual operating conditions

Zhou, J., Zhang, J., Yi, F., Feng, C., Wu, G., Li, Y., Zhang, C., & Wang, C. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103949 · Cited by 19

Life Cycle Assessment Comparison of Orchard Tractors Powered by Diesel and Hydrogen Fuel Cell

Martelli, S., Martini, V., Mocera, F., & Somà, A. (2024) · Energies · doi:10.3390/en17184599 · Cited by 18

Hierarchical eco-driving of connected hybrid electric vehicles: Integrating predictive cruise control and cost-to-go approximation-guided energy management

Zhang, Y., You, X., Song, Y., Zhao, Y., Wei, Z., & Jiao, X. (2025) · Energy · doi:10.1016/j.energy.2025.134579 · Cited by 17

Optimization on the peroxide ratio control strategy of PEMFC system based on immune algorithm

Chen, Y., Jiang, S. B., Long, M., Xu, J., Liu, Y., Kong, X., & Wan, Z. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.05.241 · Cited by 15

Predictive power demand for energy management of hybrid electric tractors

Varlese, C., Konrad, J., Volf, T., Brunner, D., Hametner, C., & Hofmann, P. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101026 · Cited by 13

Investigation of the energy dissipation mechanism for centrifugal air compressor used for fuel cell based on CFD coupling with CIT

Shen, Y., Bao, H., He, T., Fu, J., & Sun, X. (2024) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2024.124223 · Cited by 13

Hierarchical optimization of SAC-driven speed planning and energy management in intelligent fuel cell hybrid vehicle platoons under complex traffic environments

Nie, Z., Song, H., Lian, Y., & Shi, Z. (2025) · Energy · doi:10.1016/j.energy.2025.137625 · Cited by 14

Analysis of the effects of ambient wind and vehicle opening conditions on the hydrogen leakage hazard of hydrogen fuel cell buses

Ma, Q., & Guo, F. (2025) · Fuel · doi:10.1016/j.fuel.2025.134912 · Cited by 12

LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles

Wang, Y., He, H., Wu, Y., Wang, P., Wang, H., Lian, R., Wu, J., Li, Q., Meng, X., Tang, Y., et al. (2024) · Engineering · doi:10.1016/j.eng.2024.10.021 · Cited by 12

A novel braking energy management strategy for battery electric trucks with hydraulic retarder on long downhill

Li, X., Zhao, X., Xu, S., Wei, L., He, J., Shi, P., & Li, M. (2025) · Energy · doi:10.1016/j.energy.2025.135684 · Cited by 11

Multidisciplinary design optimisation of lattice-based battery housing for electric vehicles

Wang, J., Schutzeichel, M. O. H., Plaumann, B., Kletschkowski, T., & Panesar, A. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-60124-4 · Cited by 10

A Novel Experience Replay-Based Offline Deep Reinforcement Learning for Energy Management of Hybrid Electric Vehicles

Niu, Z., Wu, J., & He, H. (2024) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3511032 · Cited by 10

Weighting Optimization for Fuel Cell Hybrid Vehicles: Lifetime-Conscious Component Sizing and Energy Management

Xiao, X., Chen, S., Dong, H., Tang, Y., Feng, J., Yuan, H. R., Bai, S., Zhu, S., & Li, G. (2025) · Applied Sciences · doi:10.3390/app15073586 · Cited by 10

Energy Management Strategy for Fuel Cell Vehicles Based on Deep Transfer Reinforcement Learning

Wang, Z., He, R., Hu, D., & Lu, D. (2025) · Energies · doi:10.3390/en18092192 · Cited by 10

Adaptive wavelet-fuzzy energy management system for battery-supercapacitor energy storage system in electric vehicles integrating driving pattern recognition

Hasrouri, M., Charrouf, O., Betka, A., Abdeddaim, S., & Tiar, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117330 · Cited by 10

Development of multiple driving cycles with equivalent energy consumption to expand a standard driving cycle

Xu, N., Liu, Q., Zhang, Z., & Li, J. (2025) · Energy · doi:10.1016/j.energy.2025.134951 · Cited by 9

A TCN-BiLSTM-based approach for remaining useful life prediction of PEMFC

Jiang, J. Y., Du, D., Su, L., & Du, Y. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.153481 · Cited by 9

A load disturbance-based energy management strategy in fuel cell‑lithium battery hybrid systems for marine applications

Chigan, D., Du, Y., Chen, Y., Xiao, Y., Pang, S., Li, Y., Zheng, J., Gao, Z., Liu, X., Huangfu, Y., et al. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127660 · Cited by 9

Comparative assessment and improvement of the powertrain supplied by hybrid energy source in fuel cell vehicles

Ma, J., Zhou, Y., & Lian, J. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103750 · Cited by 9

A Nonlinear Active Disturbance Rejection Feedback Control Method for Proton Exchange Membrane Fuel Cell Air Supply Subsystems

Zhou, J., Ding, W., Zhang, J., Yi, F., Zhang, Z., Wu, G., & Zhang, C. (2024) · Actuators · doi:10.3390/act13070268 · Cited by 8

Real-Time Multiobjective EMS for Fuel Cell Vehicle Considering Energy Source Health Awareness Under Unknown Real-World Conditions

Li, T., Cui, W., & Cui, N. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3546924 · Cited by 8

Development of a real-time link-based predictive energy management strategy for extending FCEV lifespan using an experiment-driven degradation model

Park, G., Choi, K., Kim, M., Cho, E., Sung, K., & Kum, D. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126246 · Cited by 7

Degradation-based predictive energy management for intelligent fuel cell hybrid electric vehicles with a novel deep reinforcement learning architecture

Nie, Z., Shi, Z., Lian, Y., & Song, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112222 · Cited by 7

Optimization of fuel cell heavy-duty commercial vehicles sizing and energy management based on an offline-online framework

You, J., Wu, J., Zhang, Y., & Shi, W. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.239202 · Cited by 7

Advancements on Lumped Modelling of Membrane Water Content for Real-Time Prognostics and Control of PEMFC

Sicilia, M., Cervone, D., Polverino, P., & Pianese, C. (2024) · Energies · doi:10.3390/en17194841 · Cited by 6

Collaborative optimization of velocity planning and energy management for fuel cell hybrid buses at multiple intersections and bus stations

Wu, X., Ma, P., Zhou, L., Fan, Z., Zhu, Q., Yin, X., & Ouyang, Q. (2025) · Energy · doi:10.1016/j.energy.2025.135912 · Cited by 6

Synchronous control of multiple hydraulic cylinders in aerial building machine using improved deep reinforcement learning

Zhang, L., & Wang, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106448 · Cited by 6

Battery Life-Aware Predictive Deep Reinforcement Learning Energy Management for Hybrid Electric Vehicles

Wang, X.-M., & Ma, B. (2026) · Sustainability · doi:10.3390/su18052555 · Cited by 8

Equivalent Circuit Models for Lithium-Ion Batteries: A Comprehensive Review

Sun, X. J., Zuo, L., Zhang, M., Su, Y., Fu, Q., & Jiang, J. (2026) · Electronics · doi:10.3390/electronics15091968 · Cited by 6

Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid

Li, W., Shi, J., & Zhou, H. (2024) · Energies · doi:10.3390/en17133148 · Cited by 6

Expansion of Next-Generation Sustainable Clean Hydrogen Energy in South Korea: Domino Explosion Risk Analysis and Preventive Measures Due to Hydrogen Leakage from Hydrogen Re-Fueling Stations Using Monte Carlo Simulation

Lee, K., & Kang, C. (2024) · Sustainability · doi:10.3390/su16093583 · Cited by 6

Simulation of hydrogen leakage and diffusion in an enclosed garage of mixed parking of fuel-cell vehicles

Wang, G., Wang, J., Hu, C., Lv, W., & Mao, H. (2024) · Next research. · doi:10.1016/j.nexres.2024.100073 · Cited by 5

An integrated power, energy, and thermal management strategy using cascaded Control for off-road autonomous hybrid vehicles

Sundar, A., Ghate, A., Zhu, Q., Prucka, R., Figueroa-Santos, M., & Barron, M. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127269 · Cited by 5

Hierarchical model predictive control with multi-agent reinforcement learning for eco-driving of fuel cell hybrid electric vehicles

Liu, S., Chen, J., Cheng, H., Jung, S., & Kim, Y. B. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.153914 · Cited by 5

Stratospheric wind field feature extraction and energy management for hybrid electric solar airship with deep reinforcement learning

Liu, Y., Sun, K., & Lv, M. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103993 · Cited by 5

Comparative Analysis of Energy Efficiency between Battery Electric Buses and Modular Autonomous Vehicles

Oargă, I.-T., Prunean, G., Varga, B. O., Moldovanu, D., & Micu, D. D. (2024) · Applied Sciences · doi:10.3390/app14114389 · Cited by 5

A safe region method to quantitatively evaluate the safety of fuel cell operating states

Li, J., Wang, T., Yang, Q., Tian, Z., Lv, H., Wang, X., & Shen, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124386 · Cited by 5

Energy Management Strategy for Fuel Cell Uncrewed Aerial Vehicles Considering Comprehensive System Operating Cost

Li, P., Zhuo, S., & Huangfu, Y. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3590577 · Cited by 4

An experimentally validated health-aware hierarchical energy management strategy for fuel cell heavy-duty trucks

Wang, H., Zhu, D., Chen, B., Ren, Y., Ning, S., & Zhao, X. (2025) · Energy · doi:10.1016/j.energy.2025.139416 · Cited by 4

Modeling and degradation-aware energy management strategy for fuel cell vehicle considering cathode catalyst layer status

Yu, B., Jia, X., Han, T., Li, J., Xu, L., Fang, C., Zhang, L., Meng, D., Hu, Z., & Ouyang, M. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.239228 · Cited by 4

Reinforcement learning for energy management in fuel cell hybrid electric vehicles: an up-to-date review with topology-centric insights

Sahbani, A., & Saad, K. B. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01025-9 · Cited by 4

Emerging trends in intelligent management of fuel cell-battery hybrid systems: Applications, challenges, and opportunities

Nguyen, L. T., & Nguyen, C. T. (2026) · Fuel · doi:10.1016/j.fuel.2026.138987 · Cited by 4

The Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions Based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic

Lewicki, W., Bera, M. A., & Śpiewak-Szyjka, M. (2024) · Energies · doi:10.3390/en17215375 · Cited by 4

Decoding Customer Experience: A Comparative Analysis of Electric and Internal Combustion Vehicles in the U.S. Market Through Structured Topic Modeling

Rizun, N., & Duzinkewicz, B. (2025) · IEEE Access · doi:10.1109/access.2025.3561219 · Cited by 4

Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles

Lu, D., Chen, Y., Sun, Y., Wei, W., Ji, S., Ruan, H., Yi, F., Jia, C., Hu, D., Tang, K., et al. (2025) · Energies · doi:10.3390/en18174597 · Cited by 5

Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models

Ta, N. T., Huy, D. Q., Phat, T. N. T., Ngan, N. N. K., Tuấn, N. M., Tue, N. H. M., Yen, V. T. K., Ngan, D., & Tap, T. D. (2025) · Fuel Cells · doi:10.1002/fuce.70030 · Cited by 3

Hybrid energy management strategy combining adaptive frequency separation and deep reinforcement learning for fuel-cell hybrid electric vehicles

Aoumari, A. E., Ouadi, H., Rafia, H., El–bakkouri, J., & Boumaalif, Y. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.109433 · Cited by 3

Improved deep reinforcement learning optimized energy efficiency management strategy of hybrid electric commercial vehicles based on operational & maintenance costs

Jian, J., Sun, D., Xu, B., Cheng, K., Kong, W., & Lai, K. (2026) · Energy · doi:10.1016/j.energy.2026.140525 · Cited by 3

Multi-grouping-compatible frame-level unsupervised fault diagnosis and localization for electric vehicle battery packs in realistic conditions

Cao, R., Li, Y., Lu, J., Tian, F., Zhang, H., Lü, Y., Zhang, L., Chen, J., Yan, X., & Yang, S. (2026) · eTransportation · doi:10.1016/j.etran.2026.100579 · Cited by 3

Eco-friendly energy management strategy for turbo-electric eVTOLs: Coupling fuel economy, battery health and life-cycle emissions via improved TD3

Yu, F., Chen, J., Li, S., Jiang, T., Zhang, T., Sun, X., & Wang, J. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127724 · Cited by 3

Research on the dynamic price decision model of hydrogen refueling stations considering multiple uncertainties

Wang, X., Zhang, A., Wang, L., & Dong, H. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127982 · Cited by 3

Structural Design and Optimization of Proton Exchange Membrane Fuel Cell Ejector

Jiang, H., Zhao, Z., & Ni, P. (2024) · Sustainability · doi:10.3390/su162210089 · Cited by 3

Energy management in HDHEV with dual APUs: Enhancing soft actor-critic using clustered experience replay and multi-dimensional priority sampling

Zhang, D., Sun, W., Zou, Y., & Zhang, X. (2025) · Energy · doi:10.1016/j.energy.2025.134926 · Cited by 3

Green-Powered Electric Public Mobility: Integrating Urban and Interurban Routes—A Case Study Analysis

Franco, A., Lutzemberger, G., Bevilacqua, M. G., Quilici, F. G., & Vezzani, M. (2024) · Electricity · doi:10.3390/electricity5020013 · Cited by 3

Oxygen excess ratio control strategy for fuel cell dynamic response optimization

Zhang, Q., Li, Z., Zhang, J., Liu, X., Zheng, J., & Zhou, J. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104583 · Cited by 2

Predictive slope information compression using in deep reinforce learning for enhancing the economic and trip efficiency of battery electric vehicles

Gao, H., Leng, J., Li, Y., Song, X., Yang, D., Zhu, F., & Cai, X. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.120049 · Cited by 2

Experimental characterization of electric vehicle battery degradation in tropical environments: Influence of climatic factors and local driving conditions.

Lotchouang, F. H., Kibong, M. T., Nyatte, S. S., Emmanuel, S., Mewoli, A. E., & Gaston, T. J. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109126 · Cited by 2

Adaptive entropy soft actor–critic for hybrid electric vehicle energy management with battery degradation and thermal considerations

Zeng, Q., Wang, C., & Lu, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121148 · Cited by 2

Soft actor–critic two-stage joint optimization for carbon-aware scheduling and allowance procurement under market uncertainty in internet data centers

Wu, Q., Lin, W.-T., Chen, G., Zhou, X., Ming, T., & Huang, Y. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.147822 · Cited by 2

Reinforcement learning-based energy management strategy with prediction-driven reward for hybrid electric propulsion systems considering pure-electric sailing time

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