Device modeling and performance optimization of thermoelectric generators under isothermal and isoflux heat source condition

Ranjan, Mani; Maiti, Tanmoy

doi:10.1016/j.jpowsour.2020.228867

Cited by 27 publications

(13 citation statements)

References 25 publications

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“…To precisely model the TEG module, Thompson's effect is nonzero and may vary significantly [17]. Thus, dynamic behavior is incorporated into the Seebeck coefficient using Equation (3).…”

Section: Parameter Conditions Valuementioning

confidence: 99%

“…This evaluates which techniques have a better rank and are effective for the usage of the MPPT control of the TEG system. The MPPT rating can be calculated using the Equation (17). The MPPT rating of all techniques is dependent upon the following factor which includes, tuning parameter required, random numbers for updating of position, the requirement of max_iteration for termination criteria, the average value of tracking time, efficiency, response to variation of load, and modification requirement in hardware.…”

Section: Mppt Ratingmentioning

confidence: 99%

“…Rating defined from 1-4 is depicting the best to worst in Table 4, which is presenting the MPPT rating comparison of all techniques. Against all the factors, the rating is awarded, and the MPPT rating is calculated using Equation (17). If tuning parameters are 1, the rating is 1; 2, the rating is also 2; 3 means the rating is 3; and greater than 4 means rating is 4.…”

Section: 𝑀𝑃𝑃𝑇_𝑟𝑎𝑡𝑖𝑛𝑔 =mentioning

confidence: 99%

“…TEG systems drawback for deterministic operating reference is that parameters such as short-circuit current and open-circuit voltage are sensitive to the operating conditions. The efficiency of operation is highly compromised when the operating conditions change [16,17]. Power loss to the load occurs during periodic adjustment of the best operating point [12].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

et al. 2021

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The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

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“…To precisely model the TEG module, Thompson's effect is nonzero and may vary significantly [17]. Thus, dynamic behavior is incorporated into the Seebeck coefficient using Equation (3).…”

Section: Parameter Conditions Valuementioning

confidence: 99%

Section: Mppt Ratingmentioning

confidence: 99%

Section: 𝑀𝑃𝑃𝑇_𝑟𝑎𝑡𝑖𝑛𝑔 =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

et al. 2021

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“…The three-dimensional steady-state equations that govern thermoelectric phenomena are given in vector form as [20,35,36].…”

Section: Thermoelectric Field Equationsmentioning

confidence: 99%

Performance Evaluation of a Nanomaterial-Based Thermoelectric Generator with Tapered Legs

Ebiringa

Adimonyemma

Maduabuchi

2020

Glob. J. Energ. Technol. Res. Updat.

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A thermoelectric generator (TEG) converts thermal energy to electricity using thermoelectric effects. The amount of electrical energy produced is dependent on the thermoelectric material properties. Researchers have applied nanomaterials to TEG systems to further improve the device’s efficiency. Furthermore, the geometry of the thermoelectric legs has been varied from rectangular to trapezoidal and even X-cross sections to improve TEG’s performance further. However, up to date, a nanomaterial TEG that uses tapered thermoelectric legs has not been developed before. The most efficient nanomaterial TEGs still make use of the conventional rectangular leg geometry. Hence, for the first time since the conception of nanostructured thermoelectrics, we introduce a trapezoidal shape configuration in the device design. The leg geometries were simulated using ANSYS software and the results were post-processed in the MATLAB environment. The results show that the power density of the nanoparticle X-leg TEG was 10 times greater than that of the traditional bulk material semiconductor X-leg TEG. In addition, the optimum leg geometry configuration in a nanomaterial-based TEG is dependent on the operating solar radiation intensity.

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General Figures of Merit ZQ for Thermoelectric Generators Under Constant Heat‐In Flux Boundary

Li,

Wang,

Zhu

et al. 2023

Advanced Science

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The thermoelectric figure of merit ZT bridges the efficiency and material parameters for a thermoelectric device operating under constant temperature of the hot‐ and cold‐source thermal boundary (Type‐I TB). However, many application scenarios fall under the constant heat‐in flux (qh) and constant cold‐source temperature (Tc) thermal boundary (Type‐II TB), for which a figure of merit is absent for more than half a century. This study aims to fill this gap and propose a figure of merit ZQD for the thermoelectric devices under the Type‐II TB condition, defined as , where Z, h, and κ are the traditional figure of merit, leg height, and thermal conductivity, respectively. The effectiveness of ZQD is verified through both numerical calculations and experiments, which are more accurate and practical than ZT. Furthermore, a system‐level figure of merit ZQS is suggested after considering the external thermal resistance. Finally, optimization strategies for thermoelectric systems based on ZQS are proposed, showing a 30% enhancement in the efficiency. ZQD and ZQS are expected to be widely used in the thermoelectric field.

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Device modeling and performance optimization of thermoelectric generators under isothermal and isoflux heat source condition

Cited by 27 publications

References 25 publications

Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

Performance Evaluation of a Nanomaterial-Based Thermoelectric Generator with Tapered Legs

General Figures of Merit ZQ for Thermoelectric Generators Under Constant Heat‐In Flux Boundary

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