Enhanced deep reinforcement learning-based thermal management strategy for PEMFC considering coolant system parasitic power
Thermal management of fuel cells is crucial for maintaining their efficient operation, as improper temperature regulation can significantly reduce fuel cell output performance and lifespan. With the growing global energy demand and the pursuit of sustainable energy, fuel cells play a key role in ach...
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| Published in: | International journal of hydrogen energy Vol. 146; p. 149919 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
09.07.2025
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| ISSN: | 0360-3199 |
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| Abstract | Thermal management of fuel cells is crucial for maintaining their efficient operation, as improper temperature regulation can significantly reduce fuel cell output performance and lifespan. With the growing global energy demand and the pursuit of sustainable energy, fuel cells play a key role in achieving renewable energy goals, particularly in the context of the United Nations Sustainable Development Goal (SDG-7). Therefore, ensuring the high efficiency and stability of fuel cells is essential for driving the transition to clean energy. However, existing thermal management strategies are primarily based on steady-state models and traditional control methods, which are inherently limited by slow response times, low control accuracy, and insufficient utilization of parasitic cooling power. These methods struggle to address the nonlinear dynamic characteristics and multivariable coupling issues of fuel cell systems. This study addresses these challenges by proposing an innovative control strategy that integrates adaptive learning and exploration mechanisms through the use of the twin delay deep deterministic policy gradient (ALEDE-TD3) algorithm based on dynamic models. The proposed method aims to minimize parasitic cooling power while overcoming the limitations of traditional approaches. By introducing dynamic exploration and learning rate decay mechanisms, the strategy significantly enhances the system's response speed and control stability, successfully mitigating the response delays caused by multivariable coupling. Simulation results show that the ALEDE-TD3 strategy outperforms TD3, DDPG, and PID algorithms across all key performance indicators, with temperature overshoot reduced by 63.1 %, 87.4 %, and 88.9 %, respectively, and average settling time reduced by 46.7 %, 56.7 %, and 59.1 %, while consistently maintaining the lowest parasitic cooling power. This approach not only improves the overall performance of the thermal management system but also holds significant implications for advancing renewable energy goals, demonstrating the immense potential of reinforcement learning in fuel cell control.
•Proposed ALEDE-TD3 strategy improves PEMFC thermal control accuracy and stability.•Effectively reduces cooling system parasitic power and total energy consumption.•Introduces adaptive exploration and learning rate decay for faster convergence. |
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| AbstractList | Thermal management of fuel cells is crucial for maintaining their efficient operation, as improper temperature regulation can significantly reduce fuel cell output performance and lifespan. With the growing global energy demand and the pursuit of sustainable energy, fuel cells play a key role in achieving renewable energy goals, particularly in the context of the United Nations Sustainable Development Goal (SDG-7). Therefore, ensuring the high efficiency and stability of fuel cells is essential for driving the transition to clean energy. However, existing thermal management strategies are primarily based on steady-state models and traditional control methods, which are inherently limited by slow response times, low control accuracy, and insufficient utilization of parasitic cooling power. These methods struggle to address the nonlinear dynamic characteristics and multivariable coupling issues of fuel cell systems. This study addresses these challenges by proposing an innovative control strategy that integrates adaptive learning and exploration mechanisms through the use of the twin delay deep deterministic policy gradient (ALEDE-TD3) algorithm based on dynamic models. The proposed method aims to minimize parasitic cooling power while overcoming the limitations of traditional approaches. By introducing dynamic exploration and learning rate decay mechanisms, the strategy significantly enhances the system's response speed and control stability, successfully mitigating the response delays caused by multivariable coupling. Simulation results show that the ALEDE-TD3 strategy outperforms TD3, DDPG, and PID algorithms across all key performance indicators, with temperature overshoot reduced by 63.1 %, 87.4 %, and 88.9 %, respectively, and average settling time reduced by 46.7 %, 56.7 %, and 59.1 %, while consistently maintaining the lowest parasitic cooling power. This approach not only improves the overall performance of the thermal management system but also holds significant implications for advancing renewable energy goals, demonstrating the immense potential of reinforcement learning in fuel cell control.
•Proposed ALEDE-TD3 strategy improves PEMFC thermal control accuracy and stability.•Effectively reduces cooling system parasitic power and total energy consumption.•Introduces adaptive exploration and learning rate decay for faster convergence. |
| ArticleNumber | 149919 |
| Author | Huang, Yin Yuan, Weiwei Wang, Yaxiong Zhang, Zhi Ou, Kai Xuan, Dongji |
| Author_xml | – sequence: 1 givenname: Zhi surname: Zhang fullname: Zhang, Zhi organization: College of Mechanical and Electrical Engineering, Wenzhou University, China – sequence: 2 givenname: Weiwei surname: Yuan fullname: Yuan, Weiwei email: yuanweiwei@mju.edu.cn organization: School of Computer and Big Data, Minjiang University, China – sequence: 3 givenname: Yaxiong surname: Wang fullname: Wang, Yaxiong organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China – sequence: 4 givenname: Kai surname: Ou fullname: Ou, Kai organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China – sequence: 5 givenname: Yin surname: Huang fullname: Huang, Yin organization: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China – sequence: 6 givenname: Dongji orcidid: 0000-0003-1119-8975 surname: Xuan fullname: Xuan, Dongji email: xuandongji@163.com organization: College of Mechanical and Electrical Engineering, Wenzhou University, China |
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| Cites_doi | 10.1016/j.renene.2019.09.048 10.1016/j.egyr.2022.10.236 10.1016/j.apenergy.2021.116977 10.1109/TCYB.2021.3107415 10.1049/rpg2.12240 10.1016/j.applthermaleng.2023.122041 10.1016/j.applthermaleng.2021.117865 10.1016/j.fcr.2022.108475 10.1016/j.esr.2024.101380 10.1016/j.ijhydene.2023.02.012 10.1016/j.jpowsour.2015.02.106 10.1016/j.enconman.2021.115030 10.1016/S0378-7753(97)02683-9 10.1016/j.renene.2021.10.084 10.1016/j.apenergy.2023.122489 10.1007/s42154-023-00253-0 10.1016/j.ijhydene.2015.08.067 10.1016/j.enconman.2024.118887 10.1016/j.ijhydene.2024.10.028 10.1016/j.ijhydene.2010.06.046 10.1016/j.electacta.2014.04.003 10.1016/j.renene.2008.02.015 10.3390/app10020575 10.1016/j.jclepro.2017.02.033 10.1016/j.aei.2021.101360 10.1016/j.neunet.2022.02.014 10.1016/j.ijhydene.2016.10.134 10.1109/TNNLS.2021.3093429 10.1016/j.ijhydene.2022.12.226 10.1016/j.jpowsour.2014.11.126 10.1016/j.ijhydene.2022.08.154 10.1016/j.apenergy.2024.124073 10.1016/j.jpowsour.2007.12.066 10.1177/0958305X18802775 |
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| Keywords | Deep reinforcement learning Thermal management PEMFC Parasitic power Control strategy |
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| References | Li, Li, Gao (bib16) 2015; 283 Kim, Han, Park (bib43) 2020; 10 Lee, Lalk, Appleby (bib29) 1998; 70 Vengatesan, Jayakumar, Sadasivuni (bib1) 2024; 53 Chatrattanawet (bib19) 2017; 148 Horgan (bib24) 2018 Gao, Zhou (bib46) 2024; 375 Fujimoto (bib40) 2018 Iiduka (bib42) 2021; 52 Schaul (bib25) 2015 Wang, Hussain, Sun (bib2) 2022; 280 Mao, Liu, Tan (bib3) 2023; 48 Yuan, Zhou, Zhang (bib5) 2024; 357 Yuan, Zhang, Wei (bib6) 2023; 6 Khan, Shareef, Wahyudie (bib31) 2019; 30 Yuan, Wu, Zhou, Xiong, Wang (bib34) 2022; 8 Deng, Liu, Hai, Peng, Löwenstein, Pischinger, Hameyer (bib22) 2022; 251 Ahn, Choe (bib33) 2008; 179 Li, Li (bib13) 2016 Ahn, Choe (bib7) 2008; 179 Zhao, Pan, Ma (bib23) 2023 Zhiyu, Tao, Zhixiang (bib11) 2014; 132 Pohjoranta (bib18) 2015; 277 Na (bib44) 2022; 150 Han, Park, Yu (bib28) 2015; 40 Hu, Cao, Zhu (bib9) 2010; 35 Li, Cui, Nemeth, Jansen (bib38) 2021; 293 Li, Cui, Nemeth, Jansen (bib41) 2021; 293 Xia, Apergis, Bashir (bib4) 2022; 183 Larminie, Dicks (bib30) 2013 Sun, Li, Hua (bib17) 2020; 147 Qi, Zhu, Song, Yan (bib20) 2020; 238 Chen, Xu, Fang (bib10) 2022; 203 Liu, Chen, Kumar (bib12) 2023; 48 Wang, Li, Xian, Yu, Chen, Wen-Quan (bib45) 2024; 91 Tang, Chang, Liu, Xie, Pan, Yuan, Xuezhe, Dai (bib37) 2024; 318 Yan, Chen, Liu (bib14) 2019 Guzzella (bib32) 1999 Zhou, Zhao, Shuai, Li, Williams, Xu (bib21) 2021; 32 Li, Yang, Yu (bib35) 2022; 16 Yang, Quan, Wang, Liu, Zhang, Chang, Zhao (bib47) 2024; 238 Tan, Hu, Liu, Chen, Xuan (bib36) 2022; 47 Huang, Kang, Mao, Hu, Tan, Xuan (bib26) 2023; 283 Han (bib15) 2017; 42 Yu, Jung (bib27) 2008; 33 Liu, Liu, Xiong, Xu, Liu (bib39) 2021; 49 Naini (bib8) 2021 Yang (10.1016/j.ijhydene.2025.06.109_bib47) 2024; 238 Na (10.1016/j.ijhydene.2025.06.109_bib44) 2022; 150 Zhao (10.1016/j.ijhydene.2025.06.109_bib23) 2023 Lee (10.1016/j.ijhydene.2025.06.109_bib29) 1998; 70 Chen (10.1016/j.ijhydene.2025.06.109_bib10) 2022; 203 Xia (10.1016/j.ijhydene.2025.06.109_bib4) 2022; 183 Naini (10.1016/j.ijhydene.2025.06.109_bib8) 2021 Pohjoranta (10.1016/j.ijhydene.2025.06.109_bib18) 2015; 277 Ahn (10.1016/j.ijhydene.2025.06.109_bib33) 2008; 179 Huang (10.1016/j.ijhydene.2025.06.109_bib26) 2023; 283 Li (10.1016/j.ijhydene.2025.06.109_bib38) 2021; 293 Li (10.1016/j.ijhydene.2025.06.109_bib41) 2021; 293 Han (10.1016/j.ijhydene.2025.06.109_bib15) 2017; 42 Hu (10.1016/j.ijhydene.2025.06.109_bib9) 2010; 35 Deng (10.1016/j.ijhydene.2025.06.109_bib22) 2022; 251 Zhiyu (10.1016/j.ijhydene.2025.06.109_bib11) 2014; 132 Fujimoto (10.1016/j.ijhydene.2025.06.109_bib40) 2018 Mao (10.1016/j.ijhydene.2025.06.109_bib3) 2023; 48 Khan (10.1016/j.ijhydene.2025.06.109_bib31) 2019; 30 Gao (10.1016/j.ijhydene.2025.06.109_bib46) 2024; 375 Li (10.1016/j.ijhydene.2025.06.109_bib16) 2015; 283 Tang (10.1016/j.ijhydene.2025.06.109_bib37) 2024; 318 Sun (10.1016/j.ijhydene.2025.06.109_bib17) 2020; 147 Kim (10.1016/j.ijhydene.2025.06.109_bib43) 2020; 10 Yu (10.1016/j.ijhydene.2025.06.109_bib27) 2008; 33 Liu (10.1016/j.ijhydene.2025.06.109_bib39) 2021; 49 Vengatesan (10.1016/j.ijhydene.2025.06.109_bib1) 2024; 53 Yuan (10.1016/j.ijhydene.2025.06.109_bib6) 2023; 6 Li (10.1016/j.ijhydene.2025.06.109_bib35) 2022; 16 Wang (10.1016/j.ijhydene.2025.06.109_bib2) 2022; 280 Yuan (10.1016/j.ijhydene.2025.06.109_bib5) 2024; 357 Yuan (10.1016/j.ijhydene.2025.06.109_bib34) 2022; 8 Qi (10.1016/j.ijhydene.2025.06.109_bib20) 2020; 238 Guzzella (10.1016/j.ijhydene.2025.06.109_bib32) 1999 Schaul (10.1016/j.ijhydene.2025.06.109_bib25) 2015 Larminie (10.1016/j.ijhydene.2025.06.109_bib30) 2013 Yan (10.1016/j.ijhydene.2025.06.109_bib14) 2019 Han (10.1016/j.ijhydene.2025.06.109_bib28) 2015; 40 Li (10.1016/j.ijhydene.2025.06.109_bib13) 2016 Zhou (10.1016/j.ijhydene.2025.06.109_bib21) 2021; 32 Chatrattanawet (10.1016/j.ijhydene.2025.06.109_bib19) 2017; 148 Horgan (10.1016/j.ijhydene.2025.06.109_bib24) 2018 Wang (10.1016/j.ijhydene.2025.06.109_bib45) 2024; 91 Liu (10.1016/j.ijhydene.2025.06.109_bib12) 2023; 48 Tan (10.1016/j.ijhydene.2025.06.109_bib36) 2022; 47 Ahn (10.1016/j.ijhydene.2025.06.109_bib7) 2008; 179 Iiduka (10.1016/j.ijhydene.2025.06.109_bib42) 2021; 52 |
| References_xml | – volume: 179 year: 2008 ident: bib33 article-title: Coolant controls of a PEM fuel cell system publication-title: J Power Sources – year: 2015 ident: bib25 article-title: Prioritized experience replay – year: 2013 ident: bib30 article-title: Fuel cell systems explained – volume: 30 year: 2019 ident: bib31 article-title: Influences of ambient conditions on the performance of proton exchange membrane fuel cell using various models publication-title: Energy Environ – volume: 280 year: 2022 ident: bib2 article-title: Nitrogen application at a lower rate reduce net field global warming potential and greenhouse gas intensity in winter wheat grown in semi-arid region of the Loess Plateau publication-title: Field Crops Res – volume: 283 year: 2023 ident: bib26 article-title: Deep reinforcement learning based energy management strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle publication-title: Energy (Calg) – volume: 357 year: 2024 ident: bib5 article-title: Shaozhe, “Unconventional frequency response analysis of pem fuel cell based on high-order frequency response function and total harmonic distortion,” publication-title: Appl Energy – volume: 6 start-page: 597 year: 2023 end-page: 610 ident: bib6 article-title: Xuezhe, “Fault diagnosis of proton exchange membrane fuel cell based on nonlinear impedance spectrum,” publication-title: Automot. Innovation – volume: 293 year: 2021 ident: bib38 article-title: Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning publication-title: Appl Energy – volume: 35 start-page: 9110 year: 2010 end-page: 9123 ident: bib9 article-title: Coolant circuit modeling and temperature fuzzy control of proton exchange membrane fuel cells publication-title: Int J Hydrogen Energy – volume: 48 start-page: 19196 year: 2023 end-page: 19206 ident: bib12 article-title: Model-based decoupling control for the thermal management system of proton exchange membrane fuel cells publication-title: Int J Hydrogen Energy – volume: 148 start-page: 934 year: 2017 end-page: 947 ident: bib19 article-title: Control structure design and robust model pre dictive control for controlling a proton exchange membrane fuel cell publication-title: J Clean Prod – volume: 238 year: 2020 ident: bib20 article-title: Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle publication-title: Energy (Calg) – volume: 40 start-page: 13549 year: 2015 end-page: 13557 ident: bib28 article-title: Control strategy of cooling system for the optimization of parasitic power of automotive fuel cell system publication-title: Int J Hydrogen Energy – volume: 70 start-page: 258e68 year: 1998 ident: bib29 article-title: Modeling electrochemical performance in large scale proton exchange membrane fuel cell stacks publication-title: J Power Sources – volume: 283 start-page: 452 year: 2015 end-page: 463 ident: bib16 article-title: On active disturbance rejection in temperature regulation of the proton exchange membrane fuel cells publication-title: J Power Sources – volume: 32 start-page: 5298 year: 2021 end-page: 5308 ident: bib21 article-title: Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle publication-title: IEEE Transact Neural Networks Learn Syst – year: 2018 ident: bib24 article-title: Distributed prioritized experience replay – volume: 48 start-page: 13294 year: 2023 end-page: 13307 ident: bib3 article-title: Multi-objective optimization of gradient porosity of gas diffusion layer and operation parameters in PEMFC based on recombination optimization compromise strategy publication-title: Int J Hydrogen Energy – volume: 147 year: 2020 ident: bib17 article-title: A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control publication-title: Renew Energy – volume: 16 start-page: 1283 year: 2022 end-page: 1298 ident: bib35 article-title: Data-driven optimal PEMFC temperature control via curriculum guidance strategy-based large-scale deep reinforcement learning publication-title: IET Renew Power Gener – volume: 49 year: 2021 ident: bib39 article-title: Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function publication-title: Adv Eng Inform – volume: 318 year: 2024 ident: bib37 article-title: Investigating the effects of multi-dimensional parameters on the internal hydrothermal characteristics of proton exchange membrane fuel cells via an enhanced impedance dimensional model publication-title: Energy Convers Manag – year: 2021 ident: bib8 article-title: Innovative thermal management systems for autonomous vehicles — design, model, and test – volume: 132 start-page: 389 year: 2014 end-page: 396 ident: bib11 article-title: Study on air-cooled self-humidifying PEMFC control method based on segmented predict negative feedback control publication-title: Electrochim Acta – volume: 52 start-page: 13250 year: 2021 end-page: 13261 ident: bib42 article-title: Appropriate learning rates of adaptive learning rate optimization algorithms for training deep neural networks publication-title: IEEE Trans Cybern – volume: 150 start-page: 326 year: 2022 end-page: 335 ident: bib44 article-title: Efficient learning rate adaptation based on hierarchical optimization approach publication-title: Neural Network – volume: 277 start-page: 239 year: 2015 end-page: 250 ident: bib18 article-title: Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data publication-title: J Power Sources – volume: 53 year: 2024 ident: bib1 article-title: FCEV vs. BEV—a short overview on identifying the key contributors to affordable & clean energy (SDG-7) publication-title: Energy Strategy Rev – volume: 8 start-page: 338 year: 2022 end-page: 348 ident: bib34 article-title: MPC-based thermal management for water-cooled proton exchange membrane fuel cells publication-title: Energy Rep – volume: 251 year: 2022 ident: bib22 article-title: Deep reinforcement learning based energy management strategy of fuel cell hybrid railway vehicles considering fuel cell aging publication-title: Energy Convers Manag – volume: 179 start-page: 252 year: 2008 end-page: 264 ident: bib7 article-title: Coolant controls of a PEM fuel cell system publication-title: J Power Sources – volume: 238 year: 2024 ident: bib47 article-title: Characteristics of heat, power generation, and energy efficiency study on a novel air-cooled PEMFC stack based on micro heat pipe arrays publication-title: Appl Therm Eng – volume: 375 year: 2024 ident: bib46 article-title: A co-simulation platform and climate-adaptive optimisation for cross-scale PEMFC combined heat and power supply in buildings with semi-empirical surrogate models publication-title: Appl Energy – year: 2016 ident: bib13 article-title: Temperature control of PEMFC stack based on BP neural network – volume: 47 start-page: 35790 year: 2022 end-page: 35809 ident: bib36 article-title: Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance publication-title: Int J Hydrogen Energy – volume: 91 start-page: 843 year: 2024 end-page: 857 ident: bib45 article-title: Lifetime and efficiency analysis and optimization of PEMFC-based combined heat and power system with auxiliary heating for battery publication-title: Int J Hydrogen Energy – year: 2019 ident: bib14 article-title: Model-based fault tolerant control for the thermal management of PEMFC systems publication-title: IEEE Trans Ind Electron – volume: 42 start-page: 4328 year: 2017 end-page: 4341 ident: bib15 article-title: Advanced thermal management of automotive fuel cells using a model reference adaptive control algorithm publication-title: Int J Hydrogen Energy – start-page: 1587 year: 2018 end-page: 1596 ident: bib40 article-title: Addressing function approximation error in actor critic methods publication-title: Proceedings of the 35th international conference on machine learning – volume: 10 start-page: 575 year: 2020 ident: bib43 article-title: Motion planning of robot manipulators for a smoother path using a twin delayed deep deterministic policy gradient with hindsight experience replay publication-title: Appl Sci Basel – year: 1999 ident: bib32 article-title: Control oriented modelling of fuel-cell based vehicles publication-title: Presentation in nsf workshop on the integration of – volume: 183 year: 2022 ident: bib4 article-title: Investigating the role of globalization, and energy consumption for environmental externalities: empirical evidence from developed and developing economies publication-title: Renew Energy – volume: 203 year: 2022 ident: bib10 article-title: Temperature and humidity management of PEM fuel cell power system using multi-input and multi-output fuzzy method publication-title: Appl Therm Eng – volume: 293 year: 2021 ident: bib41 article-title: Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning publication-title: Appl Energy – volume: 33 start-page: 2540 year: 2008 end-page: 2548 ident: bib27 article-title: Thermal management strategy for a proton exchange membrane fuel cell system with a large active cell area publication-title: Renew Energy – start-page: 48 year: 2023 ident: bib23 article-title: Research on joint control of water pump and radiator of PEMFC based on TCO-DDPG publication-title: Int J Hydrogen Energy – volume: 147 year: 2020 ident: 10.1016/j.ijhydene.2025.06.109_bib17 article-title: A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control publication-title: Renew Energy doi: 10.1016/j.renene.2019.09.048 – volume: 8 start-page: 338 issue: Supplement 16 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib34 article-title: MPC-based thermal management for water-cooled proton exchange membrane fuel cells publication-title: Energy Rep doi: 10.1016/j.egyr.2022.10.236 – start-page: 1587 year: 2018 ident: 10.1016/j.ijhydene.2025.06.109_bib40 article-title: Addressing function approximation error in actor critic methods – year: 2016 ident: 10.1016/j.ijhydene.2025.06.109_bib13 – volume: 293 year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib41 article-title: Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning publication-title: Appl Energy doi: 10.1016/j.apenergy.2021.116977 – volume: 52 start-page: 13250 issue: 12 year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib42 article-title: Appropriate learning rates of adaptive learning rate optimization algorithms for training deep neural networks publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2021.3107415 – volume: 16 start-page: 1283 issue: 7 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib35 article-title: Data-driven optimal PEMFC temperature control via curriculum guidance strategy-based large-scale deep reinforcement learning publication-title: IET Renew Power Gener doi: 10.1049/rpg2.12240 – volume: 238 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib47 article-title: Characteristics of heat, power generation, and energy efficiency study on a novel air-cooled PEMFC stack based on micro heat pipe arrays publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2023.122041 – year: 1999 ident: 10.1016/j.ijhydene.2025.06.109_bib32 article-title: Control oriented modelling of fuel-cell based vehicles – volume: 293 year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib38 article-title: Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning publication-title: Appl Energy doi: 10.1016/j.apenergy.2021.116977 – volume: 203 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib10 article-title: Temperature and humidity management of PEM fuel cell power system using multi-input and multi-output fuzzy method publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2021.117865 – volume: 280 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib2 article-title: Nitrogen application at a lower rate reduce net field global warming potential and greenhouse gas intensity in winter wheat grown in semi-arid region of the Loess Plateau publication-title: Field Crops Res doi: 10.1016/j.fcr.2022.108475 – volume: 53 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib1 article-title: FCEV vs. BEV—a short overview on identifying the key contributors to affordable & clean energy (SDG-7) publication-title: Energy Strategy Rev doi: 10.1016/j.esr.2024.101380 – volume: 48 start-page: 19196 issue: 50 year: 2023 ident: 10.1016/j.ijhydene.2025.06.109_bib12 article-title: Model-based decoupling control for the thermal management system of proton exchange membrane fuel cells publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2023.02.012 – start-page: 48 issue: 98 year: 2023 ident: 10.1016/j.ijhydene.2025.06.109_bib23 article-title: Research on joint control of water pump and radiator of PEMFC based on TCO-DDPG publication-title: Int J Hydrogen Energy – volume: 283 start-page: 452 issue: jun.1 year: 2015 ident: 10.1016/j.ijhydene.2025.06.109_bib16 article-title: On active disturbance rejection in temperature regulation of the proton exchange membrane fuel cells publication-title: J Power Sources doi: 10.1016/j.jpowsour.2015.02.106 – volume: 251 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib22 article-title: Deep reinforcement learning based energy management strategy of fuel cell hybrid railway vehicles considering fuel cell aging publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2021.115030 – volume: 70 start-page: 258e68 issue: 2 year: 1998 ident: 10.1016/j.ijhydene.2025.06.109_bib29 article-title: Modeling electrochemical performance in large scale proton exchange membrane fuel cell stacks publication-title: J Power Sources doi: 10.1016/S0378-7753(97)02683-9 – volume: 183 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib4 article-title: Investigating the role of globalization, and energy consumption for environmental externalities: empirical evidence from developed and developing economies publication-title: Renew Energy doi: 10.1016/j.renene.2021.10.084 – volume: 357 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib5 article-title: Shaozhe, “Unconventional frequency response analysis of pem fuel cell based on high-order frequency response function and total harmonic distortion,” publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.122489 – volume: 6 start-page: 597 issue: 4 year: 2023 ident: 10.1016/j.ijhydene.2025.06.109_bib6 article-title: Xuezhe, “Fault diagnosis of proton exchange membrane fuel cell based on nonlinear impedance spectrum,” publication-title: Automot. Innovation doi: 10.1007/s42154-023-00253-0 – year: 2018 ident: 10.1016/j.ijhydene.2025.06.109_bib24 – volume: 40 start-page: 13549 issue: 39 year: 2015 ident: 10.1016/j.ijhydene.2025.06.109_bib28 article-title: Control strategy of cooling system for the optimization of parasitic power of automotive fuel cell system publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2015.08.067 – volume: 318 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib37 article-title: Investigating the effects of multi-dimensional parameters on the internal hydrothermal characteristics of proton exchange membrane fuel cells via an enhanced impedance dimensional model publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2024.118887 – volume: 91 start-page: 843 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib45 article-title: Lifetime and efficiency analysis and optimization of PEMFC-based combined heat and power system with auxiliary heating for battery publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2024.10.028 – volume: 35 start-page: 9110 issue: 17 year: 2010 ident: 10.1016/j.ijhydene.2025.06.109_bib9 article-title: Coolant circuit modeling and temperature fuzzy control of proton exchange membrane fuel cells publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2010.06.046 – volume: 238 year: 2020 ident: 10.1016/j.ijhydene.2025.06.109_bib20 article-title: Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle publication-title: Energy (Calg) – year: 2015 ident: 10.1016/j.ijhydene.2025.06.109_bib25 – volume: 132 start-page: 389 year: 2014 ident: 10.1016/j.ijhydene.2025.06.109_bib11 article-title: Study on air-cooled self-humidifying PEMFC control method based on segmented predict negative feedback control publication-title: Electrochim Acta doi: 10.1016/j.electacta.2014.04.003 – volume: 33 start-page: 2540 issue: 12 year: 2008 ident: 10.1016/j.ijhydene.2025.06.109_bib27 article-title: Thermal management strategy for a proton exchange membrane fuel cell system with a large active cell area publication-title: Renew Energy doi: 10.1016/j.renene.2008.02.015 – volume: 10 start-page: 575 issue: 2 year: 2020 ident: 10.1016/j.ijhydene.2025.06.109_bib43 article-title: Motion planning of robot manipulators for a smoother path using a twin delayed deep deterministic policy gradient with hindsight experience replay publication-title: Appl Sci Basel doi: 10.3390/app10020575 – volume: 148 start-page: 934 year: 2017 ident: 10.1016/j.ijhydene.2025.06.109_bib19 article-title: Control structure design and robust model pre dictive control for controlling a proton exchange membrane fuel cell publication-title: J Clean Prod doi: 10.1016/j.jclepro.2017.02.033 – volume: 49 year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib39 article-title: Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function publication-title: Adv Eng Inform doi: 10.1016/j.aei.2021.101360 – volume: 150 start-page: 326 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib44 article-title: Efficient learning rate adaptation based on hierarchical optimization approach publication-title: Neural Network doi: 10.1016/j.neunet.2022.02.014 – volume: 42 start-page: 4328 issue: 7 year: 2017 ident: 10.1016/j.ijhydene.2025.06.109_bib15 article-title: Advanced thermal management of automotive fuel cells using a model reference adaptive control algorithm publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2016.10.134 – issue: 99 year: 2019 ident: 10.1016/j.ijhydene.2025.06.109_bib14 article-title: Model-based fault tolerant control for the thermal management of PEMFC systems publication-title: IEEE Trans Ind Electron – volume: 32 start-page: 5298 issue: 12 year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib21 article-title: Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle publication-title: IEEE Transact Neural Networks Learn Syst doi: 10.1109/TNNLS.2021.3093429 – volume: 283 year: 2023 ident: 10.1016/j.ijhydene.2025.06.109_bib26 article-title: Deep reinforcement learning based energy management strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle publication-title: Energy (Calg) – volume: 48 start-page: 13294 year: 2023 ident: 10.1016/j.ijhydene.2025.06.109_bib3 article-title: Multi-objective optimization of gradient porosity of gas diffusion layer and operation parameters in PEMFC based on recombination optimization compromise strategy publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2022.12.226 – volume: 277 start-page: 239 year: 2015 ident: 10.1016/j.ijhydene.2025.06.109_bib18 article-title: Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data publication-title: J Power Sources doi: 10.1016/j.jpowsour.2014.11.126 – volume: 47 start-page: 35790 issue: 84 year: 2022 ident: 10.1016/j.ijhydene.2025.06.109_bib36 article-title: Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2022.08.154 – volume: 375 year: 2024 ident: 10.1016/j.ijhydene.2025.06.109_bib46 article-title: A co-simulation platform and climate-adaptive optimisation for cross-scale PEMFC combined heat and power supply in buildings with semi-empirical surrogate models publication-title: Appl Energy doi: 10.1016/j.apenergy.2024.124073 – year: 2013 ident: 10.1016/j.ijhydene.2025.06.109_bib30 – year: 2021 ident: 10.1016/j.ijhydene.2025.06.109_bib8 – volume: 179 issue: 1 year: 2008 ident: 10.1016/j.ijhydene.2025.06.109_bib33 article-title: Coolant controls of a PEM fuel cell system publication-title: J Power Sources doi: 10.1016/j.jpowsour.2007.12.066 – volume: 30 issue: 6 year: 2019 ident: 10.1016/j.ijhydene.2025.06.109_bib31 article-title: Influences of ambient conditions on the performance of proton exchange membrane fuel cell using various models publication-title: Energy Environ doi: 10.1177/0958305X18802775 – volume: 179 start-page: 252 issue: 1 year: 2008 ident: 10.1016/j.ijhydene.2025.06.109_bib7 article-title: Coolant controls of a PEM fuel cell system publication-title: J Power Sources doi: 10.1016/j.jpowsour.2007.12.066 |
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