Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle
The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future d...
Uloženo v:
| Vydáno v: | Energy (Oxford) Ročník 295; s. 130728 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.05.2024
|
| Témata: | |
| ISSN: | 0360-5442 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost.
•BPNN agent online learning-based approach to estimate optimal gradient factor in real-time for SOC trajectories by using DP solutions.•The constructed SOC threshold value deterministic rule enables the planning of SOC trajectory for diverse driving modes.•Combining the sequence quadratic programming algorithm enables real-time optimal solving, allowing the design of an approximate global optimal strategy.•Comparative analysis proves that the proposed strategy is effective in achieving global cost optimization. |
|---|---|
| AbstractList | The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost.
•BPNN agent online learning-based approach to estimate optimal gradient factor in real-time for SOC trajectories by using DP solutions.•The constructed SOC threshold value deterministic rule enables the planning of SOC trajectory for diverse driving modes.•Combining the sequence quadratic programming algorithm enables real-time optimal solving, allowing the design of an approximate global optimal strategy.•Comparative analysis proves that the proposed strategy is effective in achieving global cost optimization. The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost. |
| ArticleNumber | 130728 |
| Author | Huang, Hao Lin, Xinyou Xie, Liping Xu, Xinhao |
| Author_xml | – sequence: 1 givenname: Xinyou orcidid: 0000-0002-4061-1055 surname: Lin fullname: Lin, Xinyou email: linxinyoou@fzu.edu.cn – sequence: 2 givenname: Hao surname: Huang fullname: Huang, Hao – sequence: 3 givenname: Xinhao surname: Xu fullname: Xu, Xinhao – sequence: 4 givenname: Liping surname: Xie fullname: Xie, Liping email: lpxie@fzu.edu.cn |
| BookMark | eNqFkb-O1DAQxl0cEncHb0AxJU0WO7HXCQUSWv5KJ10B1JbjTLJeOfZie08XHo2nwyFUFFDNFPP7Zr75bsiVDx4JecHojlG2f3Xaocc4Lbua1nzHGirr9opc02ZPK8F5_ZTcpHSilIq2667Jz3eL17M1cI5hinqerZ8gBXfJNvgE-JijNhkH-HJ_qEp_QpNDXCB4Zz2CQx39ikzrVr1CoN0Uos3HGXqdCqnPRfvRzjojTC702kE4ZzvbH9u8CT7H4CAV-YzTAmOIoGG8oAODzsFx6aMdAF3ZHcupD3i0xuEz8mTULuHzP_WWfPvw_uvhU3V3__Hz4e1dZZqmy1U7jDWKuqWNZKjNIJiRmnayp0aWTmDdaiMkCt6jqfuRdoyj6DotKeW8Nc0tebnpFhvfL5iymm1aD9MewyWpholGSr7vRBl9vY2aGFKKOCpj82-XxZt1ilG1hqROagtJrSGpLaQC87_gcyxfi8v_sDcbhuUHDxajSsaiNzjYWB6mhmD_LfALmde49g |
| CitedBy_id | crossref_primary_10_1016_j_energy_2025_138164 crossref_primary_10_3390_wevj16080467 crossref_primary_10_1109_TIE_2024_3481880 crossref_primary_10_3390_wevj15090414 crossref_primary_10_1016_j_apenergy_2024_124198 crossref_primary_10_1016_j_energy_2025_137591 |
| Cites_doi | 10.1109/TITS.2017.2729621 10.1016/j.jpowsour.2020.228798 10.1016/j.energy.2020.117224 10.1109/TCST.2015.2409235 10.1016/j.energy.2020.117499 10.1016/j.apenergy.2015.06.003 10.1109/TVT.2019.2903119 10.1016/j.apenergy.2015.01.021 10.1016/j.apenergy.2015.10.152 |
| ContentType | Journal Article |
| Copyright | 2024 |
| Copyright_xml | – notice: 2024 |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.energy.2024.130728 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Environmental Sciences |
| ExternalDocumentID | 10_1016_j_energy_2024_130728 S0360544224005000 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARJD AAXUO ABJNI ABMAC ABYKQ ACDAQ ACGFS ACIWK ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHIDL AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SES SEW SPC SPCBC SSR SSZ T5K TN5 XPP ZMT ~02 ~G- 29G 6TJ 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABFNM ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AHHHB AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HVGLF HZ~ LY6 R2- SAC WUQ ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c339t-8df2e5280371eacd51c7a097b0c7c7a5e28ac57e54bec2bf0914e599a700448c3 |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001258469900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0360-5442 |
| IngestDate | Thu Oct 02 21:42:27 EDT 2025 Sat Nov 29 06:35:53 EST 2025 Tue Nov 18 21:48:13 EST 2025 Sat May 04 15:43:29 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Energy management strategy Back propagation neural network Dynamic programming Fuel cell electric vehicle |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c339t-8df2e5280371eacd51c7a097b0c7c7a5e28ac57e54bec2bf0914e599a700448c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-4061-1055 |
| PQID | 3153774695 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_3153774695 crossref_citationtrail_10_1016_j_energy_2024_130728 crossref_primary_10_1016_j_energy_2024_130728 elsevier_sciencedirect_doi_10_1016_j_energy_2024_130728 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-15 |
| PublicationDateYYYYMMDD | 2024-05-15 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Energy (Oxford) |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Xie (bib26) 2019 Fengyan (bib9) 2022; 14 Sun, Sun, He (bib28) 2016; 185 Hongxu (bib20) 2023 Zhang (bib39) 2021; 481 Adrian (bib5) 2022; 7 Lulu (bib29) 2017; 18 Zhou (bib31) 2020 Jianfei, Hongwen, Dong (bib36) 2021; 281 Onori, Tribioli (bib12) 2015; 147 Yaqian, Xiaohong (bib38) 2022; 15 Wang, Zeng, Song (bib3) 2020; 199 Alexis (bib11) 2023; 344 Liu (bib17) 2019; 68 Xinyou, Xinhao, Zhaorui (bib23) 2022 Li, Zhang, He (bib32) 2016; 194 Du (bib4) 2020; 234 Robert (bib7) 2022; 47 Zhang, Xiong (bib15) 2015; 155 Hansang, Wencong (bib35) 2018; 67 Zeng, Wang (bib27) 2015; 23 Yupeng (bib14) 2022; 236 van Harselaar (bib18) 2019; 52 Hujun (bib13) 2021 Hu (bib19) 2020 Li (bib33) 2016; 162 MK (bib1) 2022; 47 Geng (bib30) 2020; 199 Nan (bib16) 2022; 312 Yang, Alexandre, Marie-Cécile (bib21) 2021; 229 Chunhua (bib37) 2022; 543 Bragadeshwaran (bib24) 2023; 280 Krishnaiah, Naik (bib2) 2022; 72 Xiaodong (bib10) 2023; 267 Fangwu (bib22) 2021; 92 Roberta, Cristina, Ezio (bib6) 2022; 317 He (bib34) 2018; 19 Shabbir, Evangelou (bib8) 2019 Xinyou, Jiajin, Lian (bib25) 2022; 52 Liu (10.1016/j.energy.2024.130728_bib17) 2019; 68 Nan (10.1016/j.energy.2024.130728_bib16) 2022; 312 He (10.1016/j.energy.2024.130728_bib34) 2018; 19 Yaqian (10.1016/j.energy.2024.130728_bib38) 2022; 15 Hu (10.1016/j.energy.2024.130728_bib19) 2020 MK (10.1016/j.energy.2024.130728_bib1) 2022; 47 Roberta (10.1016/j.energy.2024.130728_bib6) 2022; 317 Jianfei (10.1016/j.energy.2024.130728_bib36) 2021; 281 Wang (10.1016/j.energy.2024.130728_bib3) 2020; 199 Xinyou (10.1016/j.energy.2024.130728_bib23) 2022 Du (10.1016/j.energy.2024.130728_bib4) 2020; 234 Fangwu (10.1016/j.energy.2024.130728_bib22) 2021; 92 Xinyou (10.1016/j.energy.2024.130728_bib25) 2022; 52 Hansang (10.1016/j.energy.2024.130728_bib35) 2018; 67 Bragadeshwaran (10.1016/j.energy.2024.130728_bib24) 2023; 280 Sun (10.1016/j.energy.2024.130728_bib28) 2016; 185 Hongxu (10.1016/j.energy.2024.130728_bib20) 2023 Li (10.1016/j.energy.2024.130728_bib32) 2016; 194 Xiaodong (10.1016/j.energy.2024.130728_bib10) 2023; 267 Onori (10.1016/j.energy.2024.130728_bib12) 2015; 147 Lulu (10.1016/j.energy.2024.130728_bib29) 2017; 18 van Harselaar (10.1016/j.energy.2024.130728_bib18) 2019; 52 Adrian (10.1016/j.energy.2024.130728_bib5) 2022; 7 Alexis (10.1016/j.energy.2024.130728_bib11) 2023; 344 Zeng (10.1016/j.energy.2024.130728_bib27) 2015; 23 Xie (10.1016/j.energy.2024.130728_bib26) 2019 Yang (10.1016/j.energy.2024.130728_bib21) 2021; 229 Chunhua (10.1016/j.energy.2024.130728_bib37) 2022; 543 Zhang (10.1016/j.energy.2024.130728_bib39) 2021; 481 Krishnaiah (10.1016/j.energy.2024.130728_bib2) 2022; 72 Fengyan (10.1016/j.energy.2024.130728_bib9) 2022; 14 Zhou (10.1016/j.energy.2024.130728_bib31) 2020 Robert (10.1016/j.energy.2024.130728_bib7) 2022; 47 Yupeng (10.1016/j.energy.2024.130728_bib14) 2022; 236 Hujun (10.1016/j.energy.2024.130728_bib13) 2021 Geng (10.1016/j.energy.2024.130728_bib30) 2020; 199 Zhang (10.1016/j.energy.2024.130728_bib15) 2015; 155 Li (10.1016/j.energy.2024.130728_bib33) 2016; 162 Shabbir (10.1016/j.energy.2024.130728_bib8) 2019 |
| References_xml | – volume: 47 year: 2022 ident: bib7 article-title: Comparative study of energy management systems for a hybrid fuel cell electric vehicle - a novel mutative fuzzy logic controller to prolong fuel cell lifetime publication-title: Int J Hydrogen Energy – volume: 14 year: 2022 ident: bib9 article-title: Energy management strategy for hybrid energy storage electric vehicles based on Pontryagin's minimum principle considering battery degradation publication-title: Sustainability – volume: 68 year: 2019 ident: bib17 article-title: Heuristic dynamic programming based online energy management strategy for plug-in hybrid electric vehicles publication-title: IEEE Trans. Vehicular Technology – volume: 19 start-page: 1607 year: 2018 end-page: 1617 ident: bib34 article-title: Adaptive fuzzy logic energy management strategy based on reasonable SOC reference curve for online control of plug-in hybrid electric city bus publication-title: IEEE Trans Intell Transport Syst – volume: 47 year: 2022 ident: bib1 article-title: Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: a review publication-title: Int J Hydrogen Energy – volume: 199 year: 2020 ident: bib30 article-title: A cascaded energy management optimization method of multimode power-split hybrid electric vehicles publication-title: Energy – volume: 194 year: 2016 ident: bib32 article-title: Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus publication-title: Appl Energy – volume: 7 year: 2022 ident: bib5 article-title: Probabilistic feasibility space of scaling up green hydrogen supply publication-title: Nat Energy – year: 2020 ident: bib19 article-title: Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming publication-title: Energy – volume: 344 year: 2023 ident: bib11 article-title: Optimal energy management of hybrid electric vehicles considering pollutant emissions during transient operations publication-title: Appl Energy – volume: 267 year: 2023 ident: bib10 article-title: Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication publication-title: Energy – volume: 15 year: 2022 ident: bib38 article-title: Dual heuristic dynamic programming based energy management control for hybrid electric vehicles publication-title: Energies – volume: 52 year: 2019 ident: bib18 article-title: Improved implementation of dynamic programming on the example of hybrid electric vehicle control publication-title: IFAC-PapersOnLine – volume: 72 year: 2022 ident: bib2 article-title: A review on hybrid electrical vehicles publication-title: Strojnícky časopis - Journal of Mechanical Engineering – start-page: 282 year: 2021 ident: bib13 article-title: Offline optimal energy management strategies considering high dynamics in batteries and constraints on fuel cell system power rate: from analytical derivation to validation on test bench publication-title: Appl Energy – volume: 92 year: 2021 ident: bib22 article-title: Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections publication-title: Transport Res Part D – start-page: 236 year: 2019 ident: bib26 article-title: Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus publication-title: Appl Energy – volume: 185 year: 2016 ident: bib28 article-title: Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles publication-title: Appl Energy – start-page: 235 year: 2019 ident: bib8 article-title: Threshold-changing control strategy for series hybrid electric vehicles publication-title: Appl Energy – volume: 236 year: 2022 ident: bib14 article-title: A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control publication-title: Proc IME M J Eng Marit Environ – volume: 481 year: 2021 ident: bib39 article-title: Bi-Level energy management of plug-in hybrid electric vehicles for fuel economy and battery lifetime with intelligent state-of-charge reference publication-title: J Power Sources – volume: 52 year: 2022 ident: bib25 article-title: A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction publication-title: J Energy Storage – start-page: 451 year: 2020 ident: bib31 article-title: An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle publication-title: J Power Sources – volume: 280 year: 2023 ident: bib24 article-title: Intelligent energy management through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in plugin parallel hybrid electric vehicle publication-title: Energy Convers Manag – volume: 147 year: 2015 ident: bib12 article-title: Adaptive Pontryagin's Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt publication-title: Appl Energy – volume: 317 year: 2022 ident: bib6 article-title: Investigation by modelling of a plug-in hybrid electric commercial vehicle with diesel engine on WLTC publication-title: Fuel – volume: 67 year: 2018 ident: bib35 article-title: Hierarchical energy management for power-split plug-in HEVs using distance-based optimized speed and SOC profiles publication-title: IEEE Trans Veh Technol – start-page: 321 year: 2022 ident: bib23 article-title: Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle publication-title: Appl Energy – volume: 162 start-page: 868 year: 2016 end-page: 879 ident: bib33 article-title: Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses publication-title: Appl Energy – volume: 543 year: 2022 ident: bib37 article-title: Reinforcement learning-based energy management strategies of fuel cell hybrid vehicles with multi-objective control publication-title: J Power Sources – volume: 234 year: 2020 ident: bib4 article-title: Parameter optimization of rule-based control strategy for multi-mode hybrid electric vehicle publication-title: Proc Inst Mech Eng - Part D J Automob Eng – volume: 312 year: 2022 ident: bib16 article-title: Global optimization energy management for multi-energy source vehicles based on “Information layer - physical layer - energy layer - dynamic programming” (IPE-DP) publication-title: Appl Energy – volume: 155 year: 2015 ident: bib15 article-title: Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming publication-title: Appl Energy – start-page: 284 year: 2023 ident: bib20 article-title: Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus publication-title: Energy – volume: 199 year: 2020 ident: bib3 article-title: Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information publication-title: Energy – volume: 281 year: 2021 ident: bib36 article-title: Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario publication-title: Appl Energy – volume: 229 year: 2021 ident: bib21 article-title: Real-time cost-minimization power-allocating strategy via model predictive control for fuel cell hybrid electric vehicles publication-title: Energy Convers Manag – volume: 18 year: 2017 ident: bib29 article-title: Optimal energy management for HEVs in eco-driving applications using Bi-level MPC publication-title: IEEE Trans Intell Transport Syst – volume: 23 year: 2015 ident: bib27 article-title: A parallel hybrid electric vehicle energy management strategy using stochastic model predictive control with road grade preview publication-title: IEEE Trans. Contr. Sys. Techn. – volume: 19 start-page: 1607 issue: 5 year: 2018 ident: 10.1016/j.energy.2024.130728_bib34 article-title: Adaptive fuzzy logic energy management strategy based on reasonable SOC reference curve for online control of plug-in hybrid electric city bus publication-title: IEEE Trans Intell Transport Syst doi: 10.1109/TITS.2017.2729621 – volume: 281 year: 2021 ident: 10.1016/j.energy.2024.130728_bib36 article-title: Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario publication-title: Appl Energy – volume: 18 issue: 8 year: 2017 ident: 10.1016/j.energy.2024.130728_bib29 article-title: Optimal energy management for HEVs in eco-driving applications using Bi-level MPC publication-title: IEEE Trans Intell Transport Syst – volume: 481 year: 2021 ident: 10.1016/j.energy.2024.130728_bib39 article-title: Bi-Level energy management of plug-in hybrid electric vehicles for fuel economy and battery lifetime with intelligent state-of-charge reference publication-title: J Power Sources doi: 10.1016/j.jpowsour.2020.228798 – volume: 199 year: 2020 ident: 10.1016/j.energy.2024.130728_bib30 article-title: A cascaded energy management optimization method of multimode power-split hybrid electric vehicles publication-title: Energy doi: 10.1016/j.energy.2020.117224 – volume: 14 issue: 3 year: 2022 ident: 10.1016/j.energy.2024.130728_bib9 article-title: Energy management strategy for hybrid energy storage electric vehicles based on Pontryagin's minimum principle considering battery degradation publication-title: Sustainability – volume: 344 year: 2023 ident: 10.1016/j.energy.2024.130728_bib11 article-title: Optimal energy management of hybrid electric vehicles considering pollutant emissions during transient operations publication-title: Appl Energy – volume: 312 year: 2022 ident: 10.1016/j.energy.2024.130728_bib16 article-title: Global optimization energy management for multi-energy source vehicles based on “Information layer - physical layer - energy layer - dynamic programming” (IPE-DP) publication-title: Appl Energy – volume: 7 issue: 9 year: 2022 ident: 10.1016/j.energy.2024.130728_bib5 article-title: Probabilistic feasibility space of scaling up green hydrogen supply publication-title: Nat Energy – volume: 194 year: 2016 ident: 10.1016/j.energy.2024.130728_bib32 article-title: Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus publication-title: Appl Energy – volume: 67 issue: 10 year: 2018 ident: 10.1016/j.energy.2024.130728_bib35 article-title: Hierarchical energy management for power-split plug-in HEVs using distance-based optimized speed and SOC profiles publication-title: IEEE Trans Veh Technol – volume: 234 issue: 10–11 year: 2020 ident: 10.1016/j.energy.2024.130728_bib4 article-title: Parameter optimization of rule-based control strategy for multi-mode hybrid electric vehicle publication-title: Proc Inst Mech Eng - Part D J Automob Eng – volume: 23 issue: 6 year: 2015 ident: 10.1016/j.energy.2024.130728_bib27 article-title: A parallel hybrid electric vehicle energy management strategy using stochastic model predictive control with road grade preview publication-title: IEEE Trans. Contr. Sys. Techn. doi: 10.1109/TCST.2015.2409235 – year: 2020 ident: 10.1016/j.energy.2024.130728_bib19 article-title: Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming publication-title: Energy – volume: 47 issue: 17 year: 2022 ident: 10.1016/j.energy.2024.130728_bib1 article-title: Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: a review publication-title: Int J Hydrogen Energy – volume: 199 year: 2020 ident: 10.1016/j.energy.2024.130728_bib3 article-title: Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information publication-title: Energy doi: 10.1016/j.energy.2020.117499 – volume: 92 year: 2021 ident: 10.1016/j.energy.2024.130728_bib22 article-title: Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections publication-title: Transport Res Part D – volume: 52 issue: 5 year: 2019 ident: 10.1016/j.energy.2024.130728_bib18 article-title: Improved implementation of dynamic programming on the example of hybrid electric vehicle control publication-title: IFAC-PapersOnLine – volume: 543 year: 2022 ident: 10.1016/j.energy.2024.130728_bib37 article-title: Reinforcement learning-based energy management strategies of fuel cell hybrid vehicles with multi-objective control publication-title: J Power Sources – volume: 52 issue: PC year: 2022 ident: 10.1016/j.energy.2024.130728_bib25 article-title: A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction publication-title: J Energy Storage – volume: 236 issue: 3 year: 2022 ident: 10.1016/j.energy.2024.130728_bib14 article-title: A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control publication-title: Proc IME M J Eng Marit Environ – volume: 155 year: 2015 ident: 10.1016/j.energy.2024.130728_bib15 article-title: Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming publication-title: Appl Energy doi: 10.1016/j.apenergy.2015.06.003 – volume: 68 issue: 5 year: 2019 ident: 10.1016/j.energy.2024.130728_bib17 article-title: Heuristic dynamic programming based online energy management strategy for plug-in hybrid electric vehicles publication-title: IEEE Trans. Vehicular Technology doi: 10.1109/TVT.2019.2903119 – volume: 47 issue: 57 year: 2022 ident: 10.1016/j.energy.2024.130728_bib7 article-title: Comparative study of energy management systems for a hybrid fuel cell electric vehicle - a novel mutative fuzzy logic controller to prolong fuel cell lifetime publication-title: Int J Hydrogen Energy – volume: 147 year: 2015 ident: 10.1016/j.energy.2024.130728_bib12 article-title: Adaptive Pontryagin's Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt publication-title: Appl Energy doi: 10.1016/j.apenergy.2015.01.021 – volume: 185 year: 2016 ident: 10.1016/j.energy.2024.130728_bib28 article-title: Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles publication-title: Appl Energy – start-page: 235 year: 2019 ident: 10.1016/j.energy.2024.130728_bib8 article-title: Threshold-changing control strategy for series hybrid electric vehicles publication-title: Appl Energy – volume: 317 year: 2022 ident: 10.1016/j.energy.2024.130728_bib6 article-title: Investigation by modelling of a plug-in hybrid electric commercial vehicle with diesel engine on WLTC publication-title: Fuel – volume: 280 year: 2023 ident: 10.1016/j.energy.2024.130728_bib24 article-title: Intelligent energy management through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in plugin parallel hybrid electric vehicle publication-title: Energy Convers Manag – volume: 72 issue: 2 year: 2022 ident: 10.1016/j.energy.2024.130728_bib2 article-title: A review on hybrid electrical vehicles publication-title: Strojnícky časopis - Journal of Mechanical Engineering – volume: 229 year: 2021 ident: 10.1016/j.energy.2024.130728_bib21 article-title: Real-time cost-minimization power-allocating strategy via model predictive control for fuel cell hybrid electric vehicles publication-title: Energy Convers Manag – volume: 162 start-page: 868 year: 2016 ident: 10.1016/j.energy.2024.130728_bib33 article-title: Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses publication-title: Appl Energy doi: 10.1016/j.apenergy.2015.10.152 – volume: 15 issue: 9 year: 2022 ident: 10.1016/j.energy.2024.130728_bib38 article-title: Dual heuristic dynamic programming based energy management control for hybrid electric vehicles publication-title: Energies – volume: 267 year: 2023 ident: 10.1016/j.energy.2024.130728_bib10 article-title: Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication publication-title: Energy – start-page: 236 year: 2019 ident: 10.1016/j.energy.2024.130728_bib26 article-title: Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus publication-title: Appl Energy – start-page: 284 year: 2023 ident: 10.1016/j.energy.2024.130728_bib20 article-title: Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus publication-title: Energy – start-page: 321 year: 2022 ident: 10.1016/j.energy.2024.130728_bib23 article-title: Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle publication-title: Appl Energy – start-page: 282 year: 2021 ident: 10.1016/j.energy.2024.130728_bib13 article-title: Offline optimal energy management strategies considering high dynamics in batteries and constraints on fuel cell system power rate: from analytical derivation to validation on test bench publication-title: Appl Energy – start-page: 451 year: 2020 ident: 10.1016/j.energy.2024.130728_bib31 article-title: An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle publication-title: J Power Sources |
| SSID | ssj0005899 |
| Score | 2.4914186 |
| Snippet | The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 130728 |
| SubjectTerms | algorithms Back propagation neural network batteries Dynamic programming energy Energy management strategy Fuel cell electric vehicle fuel cells fuels vehicles (equipment) |
| Title | Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle |
| URI | https://dx.doi.org/10.1016/j.energy.2024.130728 https://www.proquest.com/docview/3153774695 |
| Volume | 295 |
| WOSCitedRecordID | wos001258469900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0360-5442 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0005899 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ1Za9tAEIAX1ym0L6VNG5JeTKFvQsG6vNrH0LqkJYRC0-I3oWPlA1sKtmXsH5U_kF_XGe2uZDeUtIW-CLFIq2M-7Y5m52DsPerkXuqj5pb3hbD9JM7I0JSSlzlO_4mTZ3X64h8X_PIyHA7F107nxsTCrGe8KMLNRlz_V1FjGwqbQmf_QtxNp9iA-yh03KLYcftHgv-oaswbz6t5bTAwV7RwLK4TNGeUA8HG_Wlttt9aKmWGqSIxotLKUtMRz0blYrIazy2a8zKVh3wzQV1XmoQiJY48cx3S2bi_L1XmW-USGlt5JWcWLRRY4y3FiVmqBA_e6lqOm6cwqwQqJpGSoW6U_31jsbhQaQ-Gk2JbVi2W2vB9HpembVjp48Y7bbqyNJXsHu2aPFyfVutV0Keyw5lYnNbxScV_9ezA9_fGdldV8LwzTyiTxfRU1g9zShehuthcB6rvZ-D-Rl1Tz-RvSwUkHrADlwci7LKDs8-D4ZfWpyisC5Y2t2JiNWuHwrvX-p0u9ItWUKs6V0_ZE_2PAmeKrWesI4tD9siEsC8P2dGgDY_EA_X8sHzObjV8sAMfNPBBAx_swwcKPjDwQQsfNPBBDR_swAcKPtiFDzR8YOADpAdiIPiA4AMFHxj4QMP3gn3_NLj6cG7r6iB26nliZYdZ7sqAiqtxB7WHLHBSHvcET3opx71AumGcBlwGPg5TbpKjYuzLQIiYCjr4YeodsW5RFvKYQe54XsJFkrqZ9PvSE7247zgi95KMdFp-wjwjpyjVqfOpgsssMj6S00hJNyLpRkq6J8xuzrpWqWPuOZ4bBCKt_iq1NkJq7znznSEmwtmBXmZcyLJaRh4qNPiD1xfBy3_u_RV73H6Er1l3tajkG_YwXa8my8Vb_Qn8BMqD7tg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Dynamic+programming+solutions+extracted+SOC-trajectory+online+learning+generation+algorithm+based+approximate+global+optimization+control+strategy+for+a+fuel+cell+hybrid+electric+vehicle&rft.jtitle=Energy+%28Oxford%29&rft.au=Lin%2C+Xinyou&rft.au=Huang%2C+Hao&rft.au=Xu%2C+Xinhao&rft.au=Xie%2C+Liping&rft.date=2024-05-15&rft.pub=Elsevier+Ltd&rft.issn=0360-5442&rft.volume=295&rft_id=info:doi/10.1016%2Fj.energy.2024.130728&rft.externalDocID=S0360544224005000 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0360-5442&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0360-5442&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0360-5442&client=summon |