Model Of Thermoelectric Generator Research Articles

Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators

Despite extensive studies on thermoelectric materials in a thermoelectric generator (TEG), the inverse design of TEG operating conditions remains challenging. Recent efforts in deep learning of TEGs using artificial neural network (ANN) lack global sensitivity analyses and interpretation of the connection-weights, leading to unknown coupling between input parameters and errors in importance ranking. This work reports a deep learning framework for TEGs using generalizable ANN, interpretable local (LSA) and global sensitivity analyses (GSA). Sensitivity analyses are applied to determine how energy conversion efficiency (η) under the practical Neumann and Robin thermal boundary conditions is affected by TEG operating conditions, including hot-side heat flux (qh), cold-side convective heat transfer coefficient (hf), temperature of the cooling medium (Ta), load resistance (RL), electric contact resistance (Rc), and thermal contact resistance (Kc). First, multiphysics TEG simulation results based on 3D finite element method (FEM) are validated by our experimental measurements using a self-built TEG performance evaluation system. The partial derivative-based LSA is done on a 1D theoretical TEG model to obtain the trend in sensitivity factors. Then, an ANN model is trained and validated based on the FEM simulation results and applied to the GSA process, aiming at reducing the computational load. Variance-based GSA is applied on the ANN model to determine the importance ranking and reflect the coupling between input parameters. The variance-based GSA ranks the importance as qh>hf>RL>Ta≈Rc≈Kc, and reflects the coupling between qh and hf. Finally, to interpret the ANN and the hidden deep learning process, a method combining the connection-weights algorithm and neural interpretation diagram (NID) is proposed to extract the hidden information in the connection weights. The ANN model is proven generalizable towards larger ranges of operating conditions. This work provides a framework to design TEG operating conditions towards higher energy conversion efficiencies.

An electrical analogy model for performance evaluation of natural air-cooled TEG modules considering nonlinear effect of heat convection

With the outstanding merits of the simple principle and low computational cost, the electrical analogy model of thermoelectric generator (TEG) modules plays an important role in evaluating the performance of thermoelectric energy harvesting systems. However, owing to the simplification of the heat dissipation structure and difficulty in determining the heat dissipation parameters, it is difficult to accurately describe the nonlinear influence of thermal convection on the heat dissipation performance of the TEG module under natural air-cooled conditions, thereby reducing the estimation accuracy of the module temperature and output. A new nonlinear electrical analogy model to evaluate the performance of natural air-cooled TEG modules was proposed in this study. The proposed model more accurately describes the nonlinear influence of thermal convection on the thermal dynamic characteristics of the TEG module by introducing a heat transfer branch between the cold and hot side surfaces of the TEG module and the environment. A parameter extraction method based on multi-objective optimization and the non-dominated sorting genetic algorithm II (NSGA-II) was proposed to overcome the problem of the heat dissipation parameters of natural air-cooled TEG modules. The experimental results show that under natural convection conditions, the maximum estimation errors of the temperature and output voltage evaluated by the proposed model and parameter identification method are less than 6.18 % and 9.14 %, respectively. Under forced convection, the maximum estimation errors of the temperature and output voltage were less than 7.36 % and 8.71 %, respectively. The above estimation errors are significantly lower than the traditional model.

Multiphysics performance evaluation of thermoelectric generator arrays

Thermoelectric generators (TEGs) are widely used nowadays in heat recovery and power generation applications. TEGs are connected in series and/or parallel configurations in an array to meet the voltage and/or current requirements of electric load. Therefore, it is necessary to examine the electrical performance of different TEG array configurations employed in TEG systems. In the present study, an experimentally validated hydro-thermoelectric multiphysics computational model is employed by integrating the computational fluid dynamics (CFD) approach with the TEG model to test the electrical performance of various TEG array configurations and determined load voltage, current, associated power, and conversion efficiency. Each TEGs array configuration’s performance is evaluated by varying the hot water temperature from 27 °C to 42 °C as well as hot and cold water flowrate from 0.5 L/min to 2 L/min. Various configurations including series and a combination of series and parallel connections were designed. The findings show that the series configuration of the TEGs array generates higher electric power than series and parallel combined configurations whereas the latter achieves the maximum power point at a higher electric current than the series configuration. The comparative analysis of CFD model with the experimental results indicates a maximum discrepancy of 7 %. For thermoelectric power-generating systems, the proposed model might serve as a benchmark for optimizing TEGs array configurations according to the electric load requirements.

Global sensitivity analysis and optimization of multi-stage TEG based on random and fuzzy mixed input variables

In practical engineering, uncertainties in thermoelectric power generation arise from various factors such as material properties, geometric dimensions, assembly processes, and operational fluctuations of the thermoelectric components. Addressing subjective and objective uncertainties in the design parameters of multi-stage thermoelectric generators (TEGs), this study establishes a robust TEG model incorporating non-equilibrium thermodynamics. Fuzzy and stochastic theories are employed for uncertainty representation, and a variance-based global sensitivity analysis is conducted. The results indicate that the uncertainties in the design variables have varying effects on the output responses of the three-stage TEG. Among them, the temperature of the hot and cold sources has the most significant impact on the output responses（STHP: 0.5018, STLP:0.2199, STHη: 0.5436, STLη: 0.3336）, Parameters like Seebeck coefficient, thermal resistance, thermal conductivity, and operating current exhibit relatively minor effects. The coefficient of variation of the output response varies consistently with changes in the standard deviation or coefficient of variation of input variables, following a similar order as the sensitivity indicators. The variations in the distribution parameters of the key design variable, i.e., the temperature of the hot and cold sources, do not affect the sensitivity index rankings of the variables concerning the system output responses. As the distribution parameters of the hot and cold source temperatures gradually increase, the sensitivity indices of the design variables to the system output responses exhibit different trends, indicating a change in the degree of sensitivity to the variations in distribution parameters of the design variables. Optimization based on global sensitivity analysis enhances power output by 43.37 %, reaching a maximum of 11.37 W under constraints compared to the original 7.93 W. This research provides guidance for designing and optimizing thermoelectric power systems under mixed uncertainties.

Liquid metal-enhanced thermoelectric generator for high-temperature thermal harvesting

It is a challenge to achieve high output power of the thermoelectric generator (TEG) for high-temperature waste heat recovery due to the working fluids with low thermal conductivity (such as air) or low tolerance of temperature (water or oil). In this paper, the gallium-based liquid metal (LM) with a high boiling point (>1000 ℃) and thermal conductivity (>20 W/mK) are introduced to significantly enhance heat charging and discharging at the hot/cold sides of TEG. We also developed an electromagnetic induction pumping method for the effective LM-driven flow at high temperatures. A three-dimensional coupling multi-parameter optimization simulation model of LM-based TEG was developed to reveal the internal heat transfer and power generation mechanism for clarifying the impact factors of the performance. The results have shown that the power generation capacity can be effectively improved by increasing the inlet temperature (Thot-in). It is worth noting that the temperature difference (ΔT) of the TEG module is severely limited by the thermal resistance (Rcapacity) of the LM heat capacity, such as, when the flow rate of 2 L/min, the Rcapacity accounts for 90.6 % (0.020 ℃/W). It is easier to achieve a larger open circuit voltage by increasing the height of the thermoelectric leg, but the Pmax is constantly weakened. Compared with Xceltherm 600 and Solar salt, LM heat transfer fluid has more obvious advantages in enhancing the power generation performance of the system. The LM-based TEG experiment platform was established to study heat transfer and power generation characteristics. The results have demonstrated that good consistency was proved by comparing experimental and numerical data. And the Pmax of the LM-based TEG system is 0.16 W/cm2 at ΔT of 136 ℃. LM-based TEG can open new perspectives for broad applications in high-temperature waste heat recovery.

High performance analysis and optimization of metal halide perovskite-based thermoelectric generators for sustainable energy harvesting

Metal halide perovskite-based thermoelectric materials have garnered considerable attention due to their ultralow thermal conductivities and decent electric transport properties, while the research into thermoelectric generators (TEGs) lag much behind the progress of materials. In this paper, a real-sized 3D finite element model of a metal halide perovskite-based TEG is established under iso-thermal and iso-heat flux boundary conditions. Compared with traditional PbTe-based TEGs, perovskite-based TEGs exhibit more promising TE performance with TE conversion efficiency reaching ∼12 % at a 104 W/m2 thermal input power. In addition, the geometric parameters as well as the thermal management parameters under the iso-heat flux boundary condition, are optimized and discussed. The Taguchi optimization combined with the analysis of variance (ANOVA) method is further employed to quantify the contribution and importance of these factors on the output performance of the perovskite-based TEG. The results demonstrate that changing the device geometry and thermal management parameters will affect the temperature distribution between the hot- and cold-side. The thermal input power and the height of the p-n legs are the dominant factors affecting the output power and the TE conversion efficiency of the perovskite-based TEG. Promisingly, the TE conversion efficiency can reach above 20 % (or 65 % of the corresponding ideal Carnot efficiency) for an optimized perovskite-based TEG. This work predicts and verifies the highly promising TE performance of new metal halide perovskite-based TEGs. The principles behind the analyses here provide new strategies for designing and optimizing TEGs for waste-heat harvesting and other sustainable energy conversion applications.

Studies on the TEG with changes in temperature difference and material properties

The thermoelectric generators are solid-state devices that produce electricity. They are designed to harness unused energy, or “waste heat”. These devices were primarily utilized in military and space projects due to their reliability as a self-contained power source, requiring minimal maintenance. Additionally, they are environmentally sustainable and renewable sources of energy, emitting no air or noise pollution. According to research, Thermoelectric Generators (TEGs) have a relatively low efficiency rate of less than 5%. However, they hold the potential to effectively harness low-temperature waste heat, making them a promising energy source for the purpose of charging battery cells or super capacitors utilized in autonomous sensors. This study analyses the feasibility of generating electricity through segmented TEGs operating at temperatures below 296 K. Thermoelectric materials were studied to improve the conversion efficiency of the TEG. Furthermore, an investigation was conducted on the configuration of the thermoelectric generator. The COMSOL Metaphysics software is used to design and stimulate the segmented TEG model. This model is initially derived from an existing generator and is known for its accurate outcomes. The study revealed that using an alloy consisting of 75% Bi2Te3, and 25% as a substitute for the thermoelectric material PbSe0.5Te0.5, which led to a significant increase in conversion efficiency and output voltage. A segmented TEG model exhibited higher conversion efficiency. The model is subsequently refined by incorporating various modifications aimed at improving its conversion efficiency.

Comparison of control strategies for thermoelectric generator emulator

Thermoelectric generator (TEG) can directly convert heat energy into electrical energy. It improves the power efficiency of the energy generation system by converting the power loss in the form of heat produced during the generation process into additional electrical energy. The TEG emulator (TEGE) is a power converter that produces a similar current-voltage characteristic as the TEG. It is a valuable device used to develop and test the TEG-based energy generation system. Nonetheless, the research on the TEGE is still in the early stage. This paper proposed a proper, low-cost, and high-efficient TEGE design using the buck converter. The contribution of the paper covers the TEG model in the form of an array, the buck converter design tailored to the TEGE, and 4 new control strategies proposed for the TEGE. The control strategies are the direct referencing method (DRM), perturb and observed (PnO) method, resistance comparison method (RCM), and resistance feedback method (RFM). The conventional proportional-integral controller is used to maintain a smooth operation during transient and steady-state periods. The results show the merits or demerits for each proposed control strategy based on the accuracy, transient response, stability, overshoot, and efficiency.

Consideration of Thomson effect in multi-objective optimization of thermoelectric generator using metaheuristic techniques

This paper proposes the performance estimation of a multi-parameter multi-objective optimization problem of a thermoelectric generator (TEG) considering the Thomson Effect using metaheuristic techniques. The hot and cold side temperatures, load resistance, length of thermoelectric legs, and fill factor of the TEG are varied simultaneously to optimize the output power and efficiency of the TEG. Thirteen-metaheuristic algorithms have been used to perform single and multi-objective optimization of TEG system with multi-variable space and obtained the best performing algorithm among them for TEG model with and without considering the Thomson effect. In terms of accuracy and speed, moth flame optimization (MFO) and cuckoo search (CS) algorithms are found to be the most suitable for the models with and without considering the Thomson effect. Cuckoo search, genetic algorithm (GA), and moth flame optimization (MFO) algorithms gave consistent results for the model considering the Thomson effect. The TEG performance of both models with and without considering the Thomson effect is also compared. For single-objective optimization, CS and MFO produced optimal power output and efficiency of 16.99325 W & 2.0696 % and 19.20837 W & 2.4427 % respectively for models with and without considering the Thomson effect respectively. For multi-objective optimization, the optimal power outputs of 16.993 W & 19.20837 W and efficiencies of 1.9583 % & 2.25932 % are obtained on the Pareto front for models with and without considering the Thomson effect respectively. These optimal points are determined using the Shannon entropy decision-making approach which is found to be the best technique.

Power Optimization of Systems for Direct Thermal to Electrical Energy Conversion

Thermoelectric generators (TEGs) offer the possibility of extracting electrical power from any heat source, including low-enthalpy heat. The operation of thermoelectric generators is closely related to not only thermal factors but also the electrical load conditions. Consequently, simulations of TEG-based thermoelectric systems require the adequate modeling of both thermal and electric phenomena. In this paper, the developed TEG model is presented and verified through the design of a complete thermoelectric system able to optimize the extraction of electrical power according to the thermal conditions. The system consisted of a TEG, a properly designed low-cost DC/DC converter, and an energy storage element. The DC/DC converter was controlled by a specific algorithm that optimized the electrical power extraction following the maximum power point. The choices adopted for the system design, the implementation of the DC/DC converter, and the algorithm development were verified with experimental measurements.

Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques

Thermoelectric generators (TEGs) have gained significant interest as a sustainable energy source, due to their ability to convert thermal energy into electrical energy through the Seebeck effect. However, the power output of TEGs is highly dependent on the thermoelectric material properties and operational conditions. Accurate modeling and parameter estimation are essential for optimizing and designing TEGs, as well as for integrating them into smart grids to meet fluctuating energy demands. This work examines the challenges of accurate modeling and parameter estimation of TEGs and explores various optimization metaheuristics techniques to find TEGs parameters in real applications from experimental conditions. The paper stresses the importance of determining the properties of TEGs with precision and using parameter estimation as a technique for determining the optimal values for parameters in a TEG mathematical model that represent the actual behavior of a thermoelectric module. This methodological approach can improve TEG performance and aid in efficient energy supply and demand management, thus reducing the reliance on traditional fossil fuel-based power generation.

Performance comparison and analysis of mathematical, ANSYS and neural network model of a thermo electrical generator

- The need to generate energy from waste energy is growing as the demand for energy rises. Heat engines can only exchange around 35% of accessible energy, leaving the excess as surplus heat. A thermoelectric generator can transform this waste heat into useful energy. The Seebeck principle is used to create a thermoelectric device, which consists of a P-Type and N-Type semiconductor coupled electrically in series and thermally in parallel. Thermoelectric generators immediately convert thermal energy to electrical energy without the use of rotating parts, and because there are no moving parts, they are possibly less prone to failure. With the rising cost of fossil fuels and their negative influence on the atmosphere, it is past time to consider renewable energy sources (RES). Thermoelectric generators (TEG) are widely utilized as autonomous RES, with applications ranging from mW to W power. In this paper mainly focusing the comparison of three different model of Thermo Electric Generator. Out of these three mathematical model give a better result compared to neural and ANSYS model. Neural network model was developed from the hardware result obtained from the experiment. In practical case the temperature difference is very less so the output voltage current and power are very less. The maximum hot side temperature of SP1848 27145 SA TEG is 50 °C and minimum temperature is 27 °C. The seebeck coefficient, voltage, current, Figure of Merit performances are also plotted.

Experimental and modeling analysis on thermoelectric heat recovery to maximize the performance of next-generation diesel engines dedicated for future electrified powertrains

• A fast predictive thermoelectric generator (TEG) model is developed and validated. • A 4-cylinder 2.2 L diesel engine model with TEG is developed and calibrated. • The effective power of 1.1 kW is generated from a 3-layer TEG (1.5 × A4 size). • Brake thermal efficiency of 49.9 % is demonstrated in the next-generation engine. • The TEG system improves the engine brake thermal efficiency by 1.1 %. Thermoelectric generators (TEG) can recover significant energy loss from exhaust heat and improve brake thermal efficiency (BTE) of internal combustion engine (ICE) vehicles. However, its utilization is restricted due to increased pump losses when it is integrated with ICEs. This work uses a model-based design approach to maximize the performances of a next-generation, production-intent 2.2 L diesel engine dedicated to future electrified powertrains via TEG heat recovery. To take full advantage of TEG in the engine, a thermoelectric module (TEM) arrangement in TEG must be optimized, accounting for a tradeoff between increased pump loss and TEG effective power. Firstly, a high-fidelity 1D TEG model is developed, and a novel model calibration method is proposed by using simplified user-defined functions of heat transfer and flow friction factors. Measured thermal performances of the TEG heat exchanger are reproduced under various fin pitches, Reynolds numbers, and inlet gas conditions. The TEG model is integrated with the well-calibrated engine to maximize its BTE and power. Under the most efficient condition (peak BTE at 2250 RPM, 60% load), BTE = 48.7% and 52.1 kW brake power are obtained under the baseline case without TEG. An optimal 9 × 10 TEM arrangement generates a 1.1 kW effective power from the 3-layer TEG model (1.5 × A4 paper size, 150 mm height, 36 kg weight). Finally, a 1.1% BTE improvement is obtained without power loss in the next-generation, highly efficient, hybridized diesel engine.

Fully–coupled thermal–electric modeling of thermoelectric generators

Numerical models of thermoelectric generators require quantification of discretization uncertainty, the scrutinization of thermoelectric phenomena on model energy imbalances and simultaneous thermal–electric predictions, and the rectification of disagreement with analytic models when considering temperature-dependent material properties. Within this methods paper, two fully coupled, thermal–electric unicouple-level models are developed and evaluated over various thermal and electrical conditions to address the aforementioned issues—a numeric model in ANSYS CFX and an iterative analytic model. Model results were compared to ANSYS Thermal–Electric (TE) and ANSYS Fluent. Agreement between all models’ electrical and thermal predictions was achieved, albeit ANSYS Fluent’s thermal predictions exhibited high percent differences (7–8%) and had global energy imbalances on the order of 15% due to incongruent thermal–electrical power output predictions. ANSYS TE congruently predicts power output when considering the device’s thermal behavior and electrical performance separately, with disagreement on the order of a percent. Through incorporating all thermoelectric phenomena, ANSYS CFX’s global energy imbalances were hundredths to thousandths of a percent; exclusion of pertinent thermoelectric phenomena such as Thomson and Bridgman heating caused imbalances of tens of percent. The inclusion of Thomson heat is imperative when modeling thermoelectric devices. The analytic model’s thermal and electrical performance predictions are within ANSYS CFX’s uncertainty (2–5%), and these predictions yielded percent difference of less than 1% in comparison to ANSYS CFX when the unicouple produces ±50% maximum power. By using temperature-integrated averages of material properties, analytic modeling is sufficient for the thermal–electric characterization of unicouples with interconnectors operating under Dirichlet thermal boundary conditions.

Optimization Criteria and Efficiency of a Thermoelectric Generator.

The efficiency of a thermoelectric generator model under maximum conditions is presented for two optimization criteria proposed under the context of finite-time thermodynamics, namely, the efficient power criterion and the Omega function, where this last function represents a trade-off between useful and lost energy. The results are compared with the performance of the device at maximum power output. A macroscopic thermoelectric generator (TEG) model with three possible sources of irreversibilities is considered: (i) the electric resistance R for the Joule heating, (ii) the thermal conductances Kh and Kc of the heat exchangers between the thermal baths and the TEG, and (iii) the internal thermal conductance K for heat leakage. In particular, two configurations of the macroscopic TEG are studied: the so-called exoreversible case and the endoreversible limit. It shows that for both TEG configurations, the efficiency at maximum Omega function is always greater than that obtained in conditions of maximum efficient power, and this in turn is greater than that of the maximum power regime.

Development and validation of thermal performances in a novel thermoelectric generator model for automotive waste heat recovery systems

Integrated thermoelectric generators (TEGs) and heat exchangers (HEX) can transform heat into electrical power by converting temperature gradients between heat sources and cold sinks. Predicting TEG thermal performances with compact HEX is crucial to designing efficient TEGs for automotive waste heat recovery. However, previous investigations mainly concentrated on TEG electrical performance, neglecting the thermal performances under high Reynolds numbers (Re) of exhaust gasses. This study utilizes the model-based development (MBD) method to develop a novel 1D TEG model, focusing heat transfer coefficient α and pressure drop ΔP inside a louvered corrugated fin HEX, component temperatures, and boundary heat flux, which affect electrical power from thermoelectric modules (TEM). The performance data are measured from 36 tests under different fin pitches Fp = 1.0 - 2.0 mm, Re = 4,000–14,000, and inlet gas conditions. Two methods are used to develop the model using user-defined functions (UDFs) for friction coefficient Cf and α, accounting for actual fin geometries. In Method 1, UDF multipliers for each Fp are required to calibrate the TEG model. In Method 2, simplified UDFs for Cf and α are implemented. Method 1 shows that the model is well-calibrated with maximum average relative errors δa< 6.7% but requires model fitting for each Fp. The fast-predictive TEG model with the novel calibration Method 2 can reproduce the thermal and electrical performances with δa< 20% without model tuning, which can be used for TEG redesign and integrated into vehicle-level MBD with a conventional and electrified powertrain, leveraging a fast driving-cycle simulation.

Using dual mutation particle swarm method to optimize the variable cross-section of a thermoelectric generator based on a comprehensive thermodynamic model

The shape design for a thermoelectric generator (TEG) plays an important role in its performance. In this paper, a hyperbolic function was introduced to design a variable cross-section TEG module to optimize the configuration for maximum power generation and efficiency was sought. A comprehensive thermodynamic model was applied to establish the governing equations for the newly designed TEG module. The mutation particle swarm optimization (MPSO) method was invoked to solve the thermodynamic model. The thermodynamic model's output results include the temperatures at both ends of the TE element, thus making it possible to evaluate the actual performance of the TEG module. The results indicate that both the power generation and efficiency of the hyperbolic TEG are superior to those obtained based on a traditional design. The studies also disclosed that the hyperbolic structure can increase the thermal resistance of the TE couple making it possible to enlarge the temperature difference. This is the main mechanism to improve the performance of a hyperbolic TEG. Besides, the four non-dimensional parameters (shape parameter ( β ), area ratio ( μ ), temperature ratio ( θ ), and resistance ratio ( r x )) related to the geometric structure and working conditions have notable effects on the TEG performance. It is thus worthwhile optimizing the TEG power generation and efficiency in the variable searching space of these parameters. However, differing from the traditional optimization, it is necessary to solve the governing equations in every iteration when searching for an optimal configuration based on the comprehensive model. In order to overcome the challenge, the Dual-MPSO algorithm was used in this research. • MPSO is effective in solving the control equations for a comprehensive TEG model. • Hyperbolic varying TE element is better than constant cross-section. • The effect of the temperature ratio on a TEG's performance is non-monotonic. • The cross-sectional area affects the temperature difference of TE elements. • The hyperbolic TEG performance can be optimized through the Dual-MPSO method.

Energy, exergy and ecology performance prediction of a novel SOFC-AMTEC-TEG power system

• A novel triple-cycle power system is proposed. • Energy, exergy and ecology performance are analyzed. • Effectiveness and generic performance characteristics are demonstrated. • There is an optimum thermoelectric element number to optimize performance. In order to realize the cascade utilization of the waste heat for the solid oxide fuel cell, a novel triple-cycle system including a solid oxide fuel cell, an alkali metal thermal electric converter and a thermoelectric generator is proposed. In this system, the alkali metal thermal electric converter powered by the waste heat of the solid oxide fuel cell can drive the thermoelectric generator. The proposed system can efficiently generate electricity in a green and noiseless way, and Thomson effect is considered in the thermoelectric generator model. The results show that improving the number of the thermoelectric generator stages is not conducive to the improvement of the system performance. The maximum power density and ecology function density can result in 42.0% and 53.5% improvement over those of the sole solid oxide fuel cell system, respectively. Further sensitivity analysis confirms that there is an optimum element number of the thermoelectric generator to optimize the performance of the triple-cycle system. This work proves the feasibility of the triple-cycle system, and hints the broad prospect of high-grade heat recovery through an alkali metal thermal electric converter-thermoelectric generator unit.

Theoretical and experimental research on control strategy of maximum power point tracking for monolayer thermoelectric generator considering the degree of disturbance

In this paper, the simulation model of thermoelectric generator and maximum power point tracking control (MPPT) DC/DC circuit is set up from the theory and mathematical formula. On this basis, this paper completes the maximum power point tracking control by observation and disturbance method, constant voltage method, pure fuzzy control, constant voltage method combined with PI control and constant voltage method combined with single neuron PI control. In this paper, the above five methods are firstly simulated and compared. The maximum power point tracking accuracy and corresponding time of constant voltage method combined with PI control and constant voltage method combined with single neuron PI control are the best, and these two methods are selected for subsequent experimental verification in this paper. The intelligent control algorithm of constant voltage method combined with single neuron PI control has high maximum power point tracking accuracy, and can greatly reduce the disturbance degree of output power. The experimental tracking accuracy of the control strategy studied in this paper is between 99.3% and 99.4%. The experimental, simulation and theoretical results of the thermoelectric generator and its maximum power point tracking control algorithm in this paper are highly consistent, which can prove the rationality and correctness of the research in this paper.

Numerical study on the performance of the thermoelectric generator with different air-water mixtures

Thermoelectric generators (TEGs) have been widely used to convert thermal energy into electrical energy directly. Nowadays, the most popular TEG applications, such as vehicle heat recovery and geothermal power generation, typically utilize water as the cooling working fluid. Water sources, however, may not be available or extremely limited in some cases. Therefore, in this study, we proposed a new method of mixing water and air as the working fluid to save water. It is necessary to find out the suitable ratio of water to air, under which the performance loss could be minimized. A numerical model of TEG was built utilizing COMSOL Multiphysics to investigate the effect of water mole fraction (WMF, representing water to air ratio) on the performance of TEG, where the properties of the air-water mixture (AWM) could vary with WMF. In this study, 5 cases were considered at different flow velocities, different WMFs, and different combinations of air, water, and AWMs. The effects of these parameters on the voltage and the output power of TEGs were studied and analyzed. The TEG module could generate higher voltage and power output with increasing velocity and WMF. When air fixed on one side, only 0.2 of WMF on the other side could boost over 1.5 times of voltage and 3 times of power generation than only using air. Based on the simulation results, TEGs reach the best performance when the value of WMF is equal to 0.5 on both sides.