Battery-Based Dynamic Voltage Compensator for Thermoelectric Generation System with Adaptive Hierarchical Actor–Critic Algorithm: Modeling, Design and Hardware-in-the-Loop Experiment Validation.
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| Title: | Battery-Based Dynamic Voltage Compensator for Thermoelectric Generation System with Adaptive Hierarchical Actor–Critic Algorithm: Modeling, Design and Hardware-in-the-Loop Experiment Validation. |
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| Authors: | Huang, Wenli1 (AUTHOR), Wang, Long1,2 (AUTHOR), Guo, Yinyuan2,3 (AUTHOR), Chen, Zhuo1,2 (AUTHOR), Yang, Bo2,3 (AUTHOR) yangbo_ac@outlook.com |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2425. 45p. |
| Subject Terms: | *Thermoelectric generators, *Reinforcement learning, *Hardware-in-the-loop simulation, *Mathematical optimization, *Thermoelectric effects, *Battery management systems |
| Abstract: | Thermoelectric generation (TEG) systems suffer from severe power losses under heterogeneous temperature distributions (HTDs). This paper proposes a battery-based dynamic voltage compensation scheme optimized by an adaptive hierarchical actor–critic (AHAC) reinforcement learning algorithm. Unlike conventional methods, the AHAC controller is rigorously mapped to the physical TEG model, where the state vector explicitly incorporates temperature-dependent Seebeck coefficients, internal resistances, column voltages, currents, thermal profiles, and battery states. The action corresponds directly to battery voltage injection, and the reward function is strictly derived from the net power maximization objective defined by the system's power balance equations. By complying with thermoelectric material characteristics and thermal–electrical coupling dynamics, the proposed method ensures physical interpretability and reproducibility. The simulation and hardware-in-the-loop (HIL) results confirm the real-time feasibility of online inference and control execution, with power enhancement rates from 3.14% to 13.91% (9 × 9) and 0.44% to 13.23% (9 × 6), outperforming Dyna-Q, GA, and PG methods. The revised framework guarantees methodological coherence between the control algorithm and the TEG's physical and optimization models. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194141540 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Battery-Based Dynamic Voltage Compensator for Thermoelectric Generation System with Adaptive Hierarchical Actor–Critic Algorithm: Modeling, Design and Hardware-in-the-Loop Experiment Validation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Wenli%22">Huang, Wenli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Long%22">Wang, Long</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Yinyuan%22">Guo, Yinyuan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhuo%22">Chen, Zhuo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Bo%22">Yang, Bo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> yangbo_ac@outlook.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2425. 45p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Thermoelectric+generators%22">Thermoelectric generators</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Hardware-in-the-loop+simulation%22">Hardware-in-the-loop simulation</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Thermoelectric+effects%22">Thermoelectric effects</searchLink><br />*<searchLink fieldCode="DE" term="%22Battery+management+systems%22">Battery management systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Thermoelectric generation (TEG) systems suffer from severe power losses under heterogeneous temperature distributions (HTDs). This paper proposes a battery-based dynamic voltage compensation scheme optimized by an adaptive hierarchical actor–critic (AHAC) reinforcement learning algorithm. Unlike conventional methods, the AHAC controller is rigorously mapped to the physical TEG model, where the state vector explicitly incorporates temperature-dependent Seebeck coefficients, internal resistances, column voltages, currents, thermal profiles, and battery states. The action corresponds directly to battery voltage injection, and the reward function is strictly derived from the net power maximization objective defined by the system's power balance equations. By complying with thermoelectric material characteristics and thermal–electrical coupling dynamics, the proposed method ensures physical interpretability and reproducibility. The simulation and hardware-in-the-loop (HIL) results confirm the real-time feasibility of online inference and control execution, with power enhancement rates from 3.14% to 13.91% (9 × 9) and 0.44% to 13.23% (9 × 6), outperforming Dyna-Q, GA, and PG methods. The revised framework guarantees methodological coherence between the control algorithm and the TEG's physical and optimization models. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141540 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102425 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 45 StartPage: 2425 Subjects: – SubjectFull: Thermoelectric generators Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Hardware-in-the-loop simulation Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Thermoelectric effects Type: general – SubjectFull: Battery management systems Type: general Titles: – TitleFull: Battery-Based Dynamic Voltage Compensator for Thermoelectric Generation System with Adaptive Hierarchical Actor–Critic Algorithm: Modeling, Design and Hardware-in-the-Loop Experiment Validation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Wenli – PersonEntity: Name: NameFull: Wang, Long – PersonEntity: Name: NameFull: Guo, Yinyuan – PersonEntity: Name: NameFull: Chen, Zhuo – PersonEntity: Name: NameFull: Yang, Bo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
